LongBench v2 / 66f28ede821e116aacb29a27 / How do Charmaz and Thornberg in The Pursuit of Quality in Grounded Theory and…

The pursuit of quality in grounded theory
Kathy Charmaza and Robert Thornberg
b
aDepartment of Sociology, Sonoma State University, Rohnert Park, Sonoma, USA; bDepartment of 
Behavioural Sciences and Learning, Linköping University, Linköping, Sweden
ABSTRACT
This article introduces grounded theory and places this method 
in its historical context when 1960s quantitative researchers 
wielded harsh criticisms of qualitative research. The originators 
of grounded theory, sociologists Glaser and Strauss, sought to 
defend the quality of qualitative research and argued that 
grounded theory increased its quality by providing a method 
of theory construction. Our major foci include: (1) introducing 
the logic of grounded theory, with emphasis on how research­
ers can use it to construct theory, (2) detailing criteria for quality 
in the major forms of grounded theory advocated by Glaser and 
Strauss and augmented by Glaser, Strauss and Corbin and 
refined by Corbin, and Charmaz and (3) providing an analysis 
of how constructivist grounded theorists Thornberg, Halldin, 
Bolmsjö and Petersson attended to the interviewing process, 
coding, and developing their theoretical concept of double 
victimizing. Students and researchers new to the method can 
use our concluding guidelines as a checklist to assess the quality 
of their constructivist grounded theory research.
KEYWORDS 
Grounded theory; 
constructivist grounded 
theory; quality; qualitative 
research; credibility
Introduction
American sociologists Barney Glaser and Anselm Strauss’s (1967) The 
Discovery of Grounded Theory: Strategies for Qualitative Research not only 
proposed a new method of analysis, but also led the charge of defending the 
quality of qualitative research. Grounded theory is a systematic method of 
conducting research that shapes collecting data and provides explicit strategies 
for analyzing them. The defining purpose of this method is to construct 
a theory that offers an abstract understanding of one or more core concerns 
in the studied world.
In grounded theory studies, the researcher’s analytic focus emerges during 
the research process, rather than being determined before empirical inquiry 
begins. Increasingly, grounded theorists assume that the method is a way of 
thinking about, constructing, and interacting with data throughout the 
research process (Morse et al., forthcoming). Using grounded theory now 
means more than openness to learning about the participants’ lives. It also 
CONTACT Robert Thornberg 
robert.thornberg@liu.se 
Department of Behaviorial Sciences and Learning, 
Linköping University, Linköping SE-58183, Sweden
QUALITATIVE RESEARCH IN PSYCHOLOGY          
https://doi.org/10.1080/14780887.2020.1780357
© 2020 The Author(s). Published with license by Taylor & Francis Group, LLC. 
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means making what the researchers learn transparent by showing how the 
research has been conducted thoroughly and systematically.
The grounded theory method offers useful strategies to develop researchers’ 
theoretical analyses. This method helps them to generate new concepts in their 
discipline and the larger research literature. These concepts may have direct 
application for professional policies and practices in psychology and beyond.
We begin by briefly explaining the logic of the method and then sketch the 
historical context of the development of grounded theory. From its beginning 
to the present, grounded theory has addressed questions about quality in 
qualitative research. Glaser and Strauss (1967) tied quality to making new 
theoretical contributions. Throughout our discussion, we detail how concerns 
about quality pervade the research process in grounded theory studies. We 
also compare notions of quality among grounded theorists with the larger 
literature and then detail development of a concept and its relation to 
a theoretical framework. We end by synthesizing our key points and offering 
a checklist and suggestions for conducting quality in constructivist grounded 
theory research, although most of the suggestions are applicable to other 
versions of grounded theory as well.
Before outlining quality in grounded theory research, we wish to point out 
that many researchers, psychologists among them, use this method for 
a variety of worthy purposes instead of or in conjunction with theory con­
struction. Such purposes include exploring a new area of study (Bronk 2012) 
explicating and understanding a major process (Qin and Lykes 2006), illumi­
nating the situations of people denied a public voice (Ayón et al. 2017; Gibson 
2016; Scull, Mbonyingabo, and Kotb 2016; Tuason 2013), developing policy 
(Faija et al. 2017), and implications for professional practice (Song and de Jong 
2013; Yakushko 2010). Numerous researchers use several grounded theory 
strategies for conducting thematic analyses (cf. Braun and Clarke 2006). In this 
article, we emphasize quality in grounded theory studies that aim for theory 
construction, despite whether they also serve other goals.
The logic of grounded theory
Glaser and Strauss (1967) developed grounded theory by explaining the 
methods they used to construct their remarkable qualitative studies of death 
and dying in hospitals (Glaser & Strauss, 1965, 1968). In this methodological 
treatise, they introduced the innovative and systematic strategy of simulta­
neous data collection and analysis. It became a hallmark of grounded theory 
and has become widely adopted throughout qualitative inquiry. Glaser and 
Strauss argued that by engaging in an iterative process of data collection and 
analysis, researchers would sequentially focus on the most significant issues in 
the field of study. Simultaneous data collection and analysis help researchers to 
steadily focus on developing concepts about the data and to gather further data 
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that flesh out their nascent concepts. Glaser and Strauss opposed the separa­
tion of data gathering and analysis that characterized ethnographic research of 
their time. Ethnographers of the 1960s commonly discovered their consider­
able quantities of data lacked depth and richness. But by the time they made 
this unwelcome discovery, they had left the research site and often could not 
return. If, however, researchers systematically interrogate their data as they 
gather it, their early ideas can then inform subsequent data collection.
To begin analyzing data, Glaser and Strauss advocated comparing and 
coding the data. For them, initial coding meant labeling snippets of data to 
take them apart while being attentive to the meanings and actions suggested by 
these data. We advocate line-by-line coding as a first step, because it forces the 
researcher to take a fresh look at the data, compare fragments of these data, 
and ask analytic questions about them. The grounded theory method not only 
helps researchers to synthesize data but, moreover, to move beyond descrip­
tion through constructing new concepts that explicate what is happening.
Line-by-line coding helps grounded theorists to understand their research 
participants’ experiences and perspectives. Gaining this understanding can 
lead to rethinking or relinquishing cherished disciplinary concepts that 
researchers might have believed would fit their data. For example, psycholo­
gists have conducted many important studies about resilience. However, the 
usefulness of this concept may not fit the stance of desperate people who 
experience constant crises. Kapriskie Seide (in Charmaz 2020, 166) conducted 
firsthand research on the situation in Haiti during the cholera epidemic. She 
found herself face-to-face with homelessness, immense poverty, starvation, 
isolation, hopelessness, and death. Seide reported that ‘For a young woman, 
the word resilient was patronizing and seemed to normalize structural vio­
lence as it is surprising to some that more Haitians are not ‘losing their shit’ 
from being tested and challenged by their everyday lives’.
Glaser and Strauss’s (1965, 1968) studies were based on extensive field 
research in multiple sites. Today, most grounded theory studies are based on 
interviews. By following up on codes in the early interviews, the grounded 
theorists develop more pointed questions and ask about areas that they had 
not anticipated would be important. Tacking back and forth between data and 
analysis helps to check the pertinence of their nascent ideas, raise the analytic 
level of those that hold up, and support them more fully. While grounded 
theorists are coding, they write memos about their codes and the questions 
they have about them.
Memo writing is the intermediate step between coding and writing the first 
draft of the paper, including the provisional analysis. Early memos may 
include discussions of grounded theorists’ codes, analytic and methodological 
questions as well as comparisons between fragments of data. Other memos are 
more analytic because grounded theorists take codes apart. We suggest that 
you first define a code by its properties or characteristics. These properties are 
QUALITATIVE RESEARCH IN PSYCHOLOGY
3


what constitute the code and differentiate it from other codes. Look for 
unstated assumptions and examine your own as well as those of your research 
participants. Record where the code takes you and how it is linked to other 
codes. Not all codes are equal. Some codes are concrete and descriptive. Others 
are more analytic and account for a wider range of data. As you proceed with 
your research, your memos become more definitive and analytic. In this way, 
you are building quality into your study.
How much line-by-line coding should you do? After seeing how your codes 
coalesce and identifying which are the most important, you can stop line-by- 
line coding. Define the most important codes as focused codes. These codes 
account for more data than other codes, subsume related codes, and raise the 
analytic level of your work. Then use the focused codes to examine how they 
work with large batches of data. Focused coding expedites the analytic process 
while retaining your strong foundation in the data. Focused codes help to 
generate tentative analytic categories, which you then pursue. Do these cate­
gories hold up? Can they account for these data, or is something else going on?
Grounded theory builds checks into the research process that contribute to 
its quality, such as engaging in focused coding. Simultaneously, using focused 
coding helps you to expedite your analysis and streamline your subsequent 
data collection to gather targeted data that answer questions in your emerging 
analysis. Hence, grounded theory gives the researcher more analytic power 
with fewer data. Nonetheless, this point does not excuse having very small 
samples (cf., Thomson 2011).
Qualitative research allows researchers to discern explicit and implicit 
processes in their data. Grounded theory offers a useful way of studying 
processes. To make processes explicit, grounded theorists study actions as 
well as meanings and show how they are connected. For example, psycholo­
gists Qin and Lykes (2006) provide an excellent analysis of the convergence of 
meanings and actions. They studied the situations of Chinese women graduate 
students in the United States and discovered the basic psychological process of 
gaining self-understanding through reweaving a fragmented self. These stu­
dents often experienced a fragmented self before their arrival in the US. In 
China, their self-understanding became fragmented due to gender discrimina­
tion, political corruption, and inability to manage the web of social relation­
ships in which they were embedded. The students’ quest for higher education 
contradicted cultural norms for women. In the US their selves became further 
fragmented, as they experienced ‘discrimination, disrespect, loneliness, and 
self-doubt’ (p. 192). Yet many gained more complex selves as they rewove 
cross-cultural insights into their self-understandings.
At every stage of the research process grounded theorists make comparisons 
beginning with data and ending with comparisons between their categories.1 
1For a first-hand depiction of the comparative process, see Minas, Anglin, and Ribeiro (2018).
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Subsequently, they compare their final category or categories with the existing 
literature. Because they do not know in advance where grounded theory may 
take them, they need to do a systematic search of the literature as they 
complete their study.
Glaser and Strauss realized that the iterative process of increasing the 
analytic power of the researcher’s conceptual categories gave grounded theory 
much of its strength. They proposed a new type of sampling, ‘theoretical 
sampling’, to fill out these categories with new data. In this type of sampling, 
researchers look for data that will inform their categories. The term is confus­
ing because it is often mistaken for representative population sampling in 
quantitative research. However, theoretical sampling has nothing to do with 
representative sampling. Glaser and Strauss are aiming for what they call 
theoretical saturation of the emerging conceptual categories. Grounded the­
orists achieve theoretical saturation, when they seek more data while theore­
tical sampling, but find no new properties or characteristics of their categories. 
Theoretical sampling and theoretical saturation depend upon pursuing the 
iterative process and thoroughly checking the constructed categories against 
data. If, however, researchers only ask the same questions of each participant, 
they are likely to elicit similar stories about the topic. That is data saturation, 
and it occurs with relatively few interviews but seldom contributes to the 
analytic power of the study.
Theoretical sampling and theoretical saturation contribute to the quality of 
the study by strengthening researchers’ analysis and giving them material for 
making explicit claims about it. In short, grounded theorists are building the 
kind of ‘trustworthiness,’ of their research that counseling psychologists 
Morrow (2005) and Williams and Morrow (2009) rightly argue is imperative 
to achieve in research practice.
Concerns about quality in the development of grounded theory
At the time of Glaser and Strauss’s (1967) cutting edge statement, they were 
very much speaking to sociologists. They never dreamt that their method 
would cross many disciplinary and professional borders such as in psychology, 
nursing, medicine, education, computer science, and urban planning. Glaser 
and Strauss’s defense of the quality of qualitative research in sociology stood as 
a valiant rebuttal to quantitative sociologists. By the 1960s, qualitative research 
had come under attack by US quantitative sociologists who viewed qualitative 
inquiry as lacking objectivity, validity, reliability, and replicability (Bryant & 
Charmaz 2007; Charmaz, 1995, Charmaz 2006). Quantitative research had 
already been systematically codified and textbooks were increasingly available 
for learning how to do it. In contrast, qualitative research had largely been 
taught through an oral tradition of mentoring and immersion in the field of 
inquiry. The results were small descriptive studies. Until Glaser and Strauss’s 
QUALITATIVE RESEARCH IN PSYCHOLOGY
5


(1967) statement, little had been written, much less codified into a systematic 
statement, about how to construct a qualitative theoretical analysis of high 
quality.
When answering quantitative social scientists’ sharp criticisms of its quality, 
Glaser and Strauss rejected the common practice of evaluating qualitative 
research according to the tenets of the quantitative tradition. Glaser and 
Strauss rejected quantitative researchers’ criticisms of qualitative inquiry as 
being unsystematic idiosyncratic, biased, and intuitive. Instead, they argued 
that qualitative research must be evaluated on its own canons, not on those 
imposed by the dominant quantitative tradition.
Glaser and Strauss avowed that deductive quantitative research assumed 
that researchers knew the ‘right’ questions to ask before beginning their 
studies. Such questions were typically derived from speculative theorizing far 
removed from the empirical world. Glaser and Strauss proposed that (1) 
research questions had to arise from studying empirical situations, (2) deduc­
tive hypothesis testing undermined developing new theoretical approaches, 
and (3) thus, reduced the quality, relevance and usefulness of the research 
products.
In short, Glaser and Strauss declared that inductive qualitative research with 
rich first-hand data could lead to theory construction and that adhering to 
canons of objectivity, validity, reliability and replicability would inhibit theo­
rizing. Hence, they argued that immersion in the research and theoretical 
literatures before conducting research would sway researchers and subse­
quently, preconceive their studies. For Glaser and Strauss, theorizing–and 
creating high quality research–needed to be predicated on direct knowledge 
of the studied phenomenon. Moreover, they contended that studies of direct 
experience could be conducted with rigor.
To place Glaser and Strauss’s position in context, it helps to know more 
about what was happening in US sociology departments of the 1960s. 
Sociology and anthropology have had long histories of first-hand qualitative 
research from the beginning of their disciplines at the turn of the 20th century. 
But by the 1960s, quantitative researchers had attacked and marginalized 
qualitative inquiry in sociology by gaining control over leading departments, 
journals, research institutes, and funding agencies. They believed only a few 
talented stars could produce qualitative studies worthy of academic attention. 
Glaser and Strauss rejected this view and democratized qualitative inquiry. 
They promised that grounded theory would give ordinary researchers the tools 
to construct solid theories in their respective areas.
Glaser and Strauss’s (1967) book inspired generations of graduate students. 
The book legitimized inductive qualitative research and made coding and 
memo-writing integral strategies of qualitative inquiry. However, few 
researchers engaged in theoretical sampling and saturation and constructed 
theory. The appearance of Strauss and Corbin (1990, 1998) immensely popular 
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K. CHARMAZ AND R. THORNBERG


manuals claimed to give researchers instructions about how to do grounded 
theory. However, their books came across as rigid and prescriptive in sharp 
contrast to the open-ended, fluid approach of Strauss’s (1959) earlier work, 
and evident in the Discovery book.2 Strauss and Corbin’s manuals also 
diverged from Glaser’s (1978) elaboration of the method, to Glaser’s (1992) 
furious response and avowals to have the only ‘classic’ grounded theory 
method.
Perhaps ironically, early grounded theory researchers and proponents (e.g., 
Glaser 1978; Strauss and Corbin 1990, 1998) often treated the method as 
a mechanistic application of procedures to gather and analyze data. The 
residues of an epistemology of positivism are also evident in both Glaser’s 
and Strauss and Corbin’s texts because they presuppose neutral observers who 
(1) assume an external world, which can be discovered by meticulous observa­
tion, (2) separate themselves from the research process, (3) form an objective 
view of the data, and (4) treat their representation of research participants and 
the research process as unproblematic. In addition, Glaser defends small 
samples, presumes that grounded theory can be used with any epistemology 
(a philosophical theory of the nature of knowledge and reasoned justifications 
for it) and pursues parsimonious theoretical generalizations that transcend 
time, place, individuals, and circumstances. Epistemological stances are, how­
ever, significant because they shape how researchers gather their data and 
whether they acknowledge their influence on these data and the subsequent 
analysis.
Psychologists Henwood and Pidgeon (2003) raised a crucial point 17 years 
ago about grounded theory that still holds today. They contended that rather 
than seeing grounded theory as a unitary method, we can view it as a useful 
nodal point around which we can debate significant issues in qualitative 
inquiry. Unlike earlier manuals describing how to do grounded theory, con­
temporary versions of the method including Bryant (2017, 2019), Charmaz 
(2006, 2014), Corbin and Strauss (2008, 2015), and Thornberg and Charmaz 
(2014) take a flexible approach to grounded theory and adopt more recent 
epistemologies. These methodologists subscribe to acknowledging multiple 
realities, seeking diverse perspectives, and engaging in critical analysis 
throughout the research process. This approach appeals to researchers who 
(1) engage in reflexivity throughout the research process, (2) aim to make their 
standpoints, starting points, and research actions as transparent as possible, 
(3) read theoretical and substantive literatures on their topics before engaging 
in research, but do not necessarily take these literatures as true or final 
statements, and (4) assume every methodological approach, including 
grounded theory, assumes an epistemology.
2The open-ended character of Strauss’s earlier work is more clearly represented in constructivist grounded theory 
than in Strauss and Corbin's books in the 1990 and 1998. (Charmaz 2008).
QUALITATIVE RESEARCH IN PSYCHOLOGY
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Glaser and Strauss’s statement of grounded theory was revolutionary in the 
1960s. By 2007, Kenneth Gergen (in Cisneros-Puebla 2007) dismissed Glaser 
and Strauss’s (1967) grounded theory as passé because of its reliance on 
empiricism, an epistemological approach ‘holding that all knowledge of mat­
ters of fact either arises from experience or requires experience for its valida­
tion’ (APA Dictionary of Psychology). Gergen pointed out that all empirical 
views are interpretive, an epistemological view most 21st century grounded 
theorists share. Language and experience shape how researchers see the world. 
In contrast, Glaser (2013) still assumes that data are unproblematic and insists 
researchers must not preconceive analyzing them by invoking earlier concepts, 
perspectives, and studies.
Qualitative research and psychology
Criteria about the quality in qualitative research remain unsettled. Although 
quality is crucial in qualitative research, there is, as Corbin states, ‘little 
consensus about what constitutes an appropriate set of evaluation criteria 
for qualitative research’ (Corbin and Strauss 2015, 341). This can, at least in 
part, be explained by the multiplicity of qualitative research traditions (e.g., 
discourse analysis, ethnography, phenomenological research, conversation 
analysis) and diversities within them (e.g., critical discourse analysis, inter­
pretative phenomenological analysis, critical ethnography, constructivist 
grounded theory), rooted in various ontological and epistemological beliefs 
and research ideals. Psychologists have subscribed to this wide range of 
qualitative methods and contributed to their development.
Concerns about objectivity, validity, reliability and replicability in qualita­
tive research still pervade academic psychology. In a recent example, Anczyk 
et al. (2019) plea for replication in qualitative research. Rubin, Bell, and 
McCleland (2018) document the above concerns in their mixed methods 
study of graduate psychology programs. They found negative judgments 
about qualitative inquiry that had long been evident in the discipline. They 
reported that only 13% of the responding programs required a course in 
qualitative research. Echoing sociological criticisms of qualitative studies in 
the 1960s, Rubin et al.’s respondents stated that “qualitative research was 
either imagined to be or believed to be ‘inaccurate,’ ‘subjective,’ and ‘lacking 
rigor’ (p. 41). However, the respondents described qualitative research as 
providing the ‘best methods for feminist, community-based, and social justice 
research’ (p. 43). The critics are correct on this point. Psychologists have made 
enormous contributions in these areas, and many of them use grounded 
theory (see, for example, Ayón et al. 2017; Faija et al. 2017; Gibson, 2016; 
Lee 2018; Scull, Mbonyingabo, and Kotb 2016; Tsai, L. 2017; Tuason 2013).
Yet critics appear to ignore other important considerations about the 
contributions of psychologists who conduct qualitative research. Qualitative 
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psychologists study an amazing array of problems and are affiliated with 
a wide range of programs, including those in medicine and psychiatry, educa­
tion, religion, ethnic and gender studies, and business, to name a few. Their 
locations and diverse publication outlets may mask both the quality of their 
research and the scope of their contributions (e.g. Bronk 2012; Mazzone et al. 
2018; Tsai, Seballos-Llena, and Castellano-Datta 2017; Wójcik 2018). Also, 
qualitative psychologists often work in specialized areas on multidisciplinary 
teams (e.g. Khankeh et al. 2015).3 Furthermore, it is not unusual for psychol­
ogists to hold multi-disciplinary expertise, although they may not be employed 
in psychology programs.4
As in the 1960s, neither qualitative research, in general, nor grounded 
theory, in particular, fit judgments of quality based on quantitative research. 
We agree with Tracy’s (2010) ‘big tent’ markers of quality in qualitative 
research: a worthy topic, rich rigor, sincerity, credibility, resonance, significant 
contribution, ethics, and meaningful coherence (p. 837). However, what these 
terms mean in research practice may differ among researchers. Nonetheless, 
grounded theorists can meet these markers of quality when researchers con­
duct thorough studies. Increasingly, researchers ask for transparency and 
reflexivity as criteria of quality in qualitative research (e.g. Treharne and 
Riggs 2014).
Quality criteria in various versions of grounded theory
Grounded theory needs its own set of criteria for evaluating quality due to its 
unique features (cf., Berthelsen, Grimshaw-Aagaard, and Hansen 2018; 
Chiovitti and Piran 2003; Elliott and Lazenbatt 2005; Hutchison, Johnston, 
and Breckon 2011), although grounded theorists cannot escape more general 
guidelines concerning their use of data gathering methods to ensure quality in 
doing interviews, fieldwork etc. The principle of ‘garbage in, garbage out’ is 
very much applicable to grounded theory, particularly considering its empha­
sis on data and groundedness. Criteria for evaluating quality in grounded 
theory can vary due to which version of grounded theory is of concern. In the 
original but less developed one, Glaser and Strauss (1967) focused on cred­
ibility and applicability. They connected credibility to researchers’ confidence 
in their own knowledgeability based on carefully studying and analyzing the 
actual field, and gaining systematic knowledge of the data. Glaser and Strauss 
33.A quick search of Elsevier publications in Science Direct on the Internet showed that over 10,000 authors or co- 
authors of grounded theory articles were linked to psychology departments. Elsevier represents only one major 
publishing venue.
4Exemplars have made stellar contributions to grounded theory. Methodologist Uwe Flick (2014, 2019) holds 
advanced degrees in both psychology and sociology; Lillemor Hallberg (2006, Hallberg & Strandmark, 2006) served 
as the first editor of the International Journal of Qualitative Studies on Health and Well-Being and conducts research 
as an expert in nursing research and psychology, and psychiatrist Suzan Joon Song ( Song and de Jong 2013) has 
special training in the psychology of trauma and in medicine.
QUALITATIVE RESEARCH IN PSYCHOLOGY
9


suggested a few criteria for judging credibility: (1) a detailed and vivid descrip­
tion of data so the readers feel that they have been in the field as well, and 
literally can hear and see the participants; (2) readers’ assessments of how the 
researchers came to their conclusions (what are the data and how have they 
been gathered and analyzed?); and (3) multiple comparison groups to increase 
the scope and generality of the theory, and to correct and adjust the emerging 
theory to diverse conditions. Applicability, in turn, can be connected to 
generalizability (cf., Larsson 2009), and Glaser and Strauss (1967) proposed 
four interrelated criteria: (1) Fitness: The ‘theory must fit the substantive area 
to which it will be applied’ (p. 238), and they emphasized that the theory needs 
to fit the data under study. (2) Understanding: The theory should make sense 
and be understandable to non-researchers working or living in the substantive 
area. (3) Generality: The theory needs to be sufficiently general to increase its 
applicability – ‘categories should not be so abstract as to lose their sensitizing 
aspect, but yet must be abstract enough to make . . . theory a general guide to 
multi-conditional, ever-changing daily situations’ (p. 242). (4) Control: The 
theory should help the users to understand and analyze their social reality, to 
include enough explanatory power on what is going on in situations and how 
to bring about change in them.
In his further development of grounded Glaser (1978, 1998) suggested four 
criteria in judging quality: (1) Workability: Does the theory work to explain 
relevant behavior in the substantive area of the study? (2) Relevance: Does it 
have relevance to the people in the substantive field? (3) Fit: Does the theory fit 
the substantive area? The theory and its categories must fit the data. (4) 
Modifiability: Is the theory readily modifiable as new data emerge? In their 
development of grounded theory, Strauss and Corbin (1990; Corbin and 
Strauss 1990; Strauss and Corbin 1998) emphasized four general criteria: (1) 
quality of the data, in other words, grounded theorists need to carefully 
address and adopt quality criteria related to methods used for data gathering 
to secure credibility and trustworthiness of data; (2) the plausibility and value 
of the theory itself; (3) adequacy of the research process (sampling procedures, 
theoretical sampling, coding, categorization, development of hypotheses or 
conceptual relations between categories, and selection of core category 
through which the theory is developed); and (4) the issue of the empirical 
grounding of the theory. A comprehensive description of checklists and guide­
lines for evaluation of the two later criteria is offered by Strauss and Corbin 
(1990; Corbin and Strauss 1990; Strauss and Corbin 1998).
According to Strauss and Corbin, questions to ask regarding empirical 
grounding of the study are: (1) Are concepts generated? (2) Are the concepts 
systematically related? (3) Are there many conceptual linkages, and are the 
categories well developed? Do categories have conceptual density? (4) Is 
variation built into the theory? Have the concepts been examined under 
a broad range of conditions and do they offer several dimensions? (5) Are 
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the conditions under which variation can be found built into the study and 
explained? (6) Has process been considered and identified? (7) Do the theore­
tical findings seem significant, and to what extent? (8) Does the theory stand 
the test of time and become part of the discussions and ideas exchanged 
among relevant social and professional groups? (For further reading, see 
Strauss and Corbin 1998, pp. 270–272.)
In later editions (Corbin and Strauss 2008, 2015), Corbin also added more 
general criteria of quality in qualitative research: methodological consistency, 
clarity of the purpose, self-awareness, training in how to conduct qualitative 
research, sensitivity to participants and data, willingness to work hard, ability 
to connect with the creative self, methodological awareness, and strong desire 
to do research. Note that in the original grounded theory (Glaser and Strauss 
1967), Glaserian grounded theory (Glaser 1978, 1998) and Straussian 
grounded theory (Strauss and Corbin 1990, 1998), explanatory power (i.e., 
the power to explain and predict) is a significant quality criterion of the 
constructed theory. These approaches of grounded theory share what 
Charmaz (2006, 2014) terms as positivist definitions of theory: theories that 
seek causes, and stress explanation, prediction, generality, and universality.
From a constructivist view of grounded theory, Charmaz (2006, 2014) 
contrasts such definitions of theory with interpretive definitions of theory, in 
which theory ‘emphasizes interpretation and gives abstract understanding 
greater priority than explanation’ (Charmaz 2014, 230). Such theories aim to 
understand meanings and actions and how people construct them, and they 
‘bring in the subjectivity of the actor and may recognize the subjectivity of the 
researcher’ (p. 231). They are situated in their social, historical, local, and 
interactional contexts. Thus, different epistemologies, ideals, aims etc. between 
different versions of grounded theory have to be considered when evaluating 
quality and discussing quality criteria for grounded theory (for overviews of 
similarities and differences between Glaserian, Straussian and constructivist 
grounded theory approaches, see Berthelsen, Grimshaw-Aagaard, and Hansen 
2018; Thornberg 2017; for further discussions about the link between episte­
mology and quality criteria, see for example; Healy and Perry 2000; Lincoln, 
Lynham, and Guba 2018; Lützhöft, Nyce, and Petersen 2010; Santiago- 
Deleofosse et al. 2015).
Within a constructivist grounded theory, Charmaz (2006, 2014) proposes 
four main criteria for grounded theory studies: credibility, originality, reso­
nance, and usefulness. Credibility begins with having sufficient relevant data 
for asking incisive questions about the data, making systematic comparisons 
throughout the research process, and developing a thorough analysis. The 
more controversial the topic and/or analysis is, the more data the researcher 
needs to persuade skeptical audiences.
Credibility also involves the researcher’s views and actions. Constructivist 
grounded theory requires strong reflexivity throughout the research process. 
QUALITATIVE RESEARCH IN PSYCHOLOGY
11


This means researchers must explicate their taken-for-granted assumptions, 
which requires gaining ‘methodological self-consciousness’ (Charmaz 2017) of 
how hidden beliefs can enter the research process. During her fieldwork in 
Brazil, Elizabeth Hordge-Freeman (in Charmaz 2020, pp. 168–169) acciden­
tally discovered the plight of black adopted daughters, filhas de criação, in 
white families. Hordge-Freeman writes:
When I discovered that one woman was sleeping on the floor in their family’s home and 
several others had been working for no pay in adoptive families for over 40 years, I was 
overwhelmed with a sense of anger over this injustice. As a critical researcher, my initial 
impulse was a desire to mete out justice by exposing these ‘monstrous’ families; however, 
very early on the sensibilities of constructivist grounded theory with its emphasis on 
scrutinizing our interpretations of data and engaging in sustained reflexivity persuaded 
me to analyze the data in more nuanced ways and to even interrogate my affective 
responses. By doing so, I realized that my quest to ‘right’ this injustice and free the 
interviewees sounded eeringly similar to the savior complex for which I had critiqued 
white transnational researchers. Ultimately, using constructivist grounded theory, I re- 
directed my questions to be able to collect data that provided me a greater understanding 
of how structural and affective constraints shape filhas de criação lives and used reflex­
ivity to ensure that my interviewees’ interpretations were taken seriously.
Hordge-Freeman’s statement represents the type of strong reflexivity most 
advocated in constructivist grounded theory. The strong reflexivity under­
girding methodological self-consciousness (Charmaz 2017) means more than 
examining the researcher's methodological decisions. It means openness to 
scrutinizing who the researcher is.
Originality can take varied forms such as offering new insights, providing 
a fresh conceptualization of a recognized problem, and establishing the sig­
nificance of the analysis. Resonance demonstrates that the researchers have 
constructed concepts that not only represent their research participants’ 
experience, but also provide insight to others. To gain resonance, researchers 
must fit their data-gathering strategies to illuminate their participants’ experi­
ence. Once Jennifer Lois (2010) realized her questions of homeschooling 
mothers focused on quantity of time instead of quality, she gained resonance 
by hearing their stories from a new standpoint and revising her questions. Her 
subsequent codes: ‘Sequencing: eliciting nostalgia and anticipating regret,’ (p. 
434) and ‘Savoring: staying present and creating quality time’ (p. 437) not only 
led her to understand these mothers’ actions but also to construct the concept 
of temporal emotions. She reveals how certain present feelings like regret or 
hope can only be felt by crossing timeframes from the present to the past or 
future. Lois theorizes that how people handle temporal emotions influences 
their relative continuity of self over time.
Usefulness includes clarifying research participants’ understanding of their 
everyday lives, forming a foundation for policy and practice applications, 
contributing to creating new lines of research, as well as revealing pervasive 
12
K. CHARMAZ AND R. THORNBERG


processes and practices. Gregory Hadley (in Charmaz 2020, 168) describes the 
usefulness of Glaser and Strauss’s analysis of death and dying in hospitals.
Glaser’s work with Strauss in his book, “An Awareness of Dying”, for example, unlocked 
much of the meaning of what was “going on” in a Japanese hospital during the time 
when my father-in-law was on the trajectory of terminal cancer. That this grounded 
theory could have salience a half century later in a culture thousands of miles away is 
a testament to the genius behind what Glaser and Strauss did using Classical Grounded 
Theory.
In this case, Glaser and Strauss’s extensive research shows how they 
achieved a useful level of generality. Using constructivist grounded theory, 
Snow and Moss (2014) delineate the conditions, including those shaping 
behavior and emotions, in which seemingly spontaneous collective action 
arises. Sensitivity to these conditions can assist social movement organizers 
in mobilizing crowds.
Double victimizing: an example of developing a new concept with 
grounded theory
In their constructivist grounded theory study, Thornberg et al. (2013) exam­
ined how individuals, who had been victims of school bullying, perceived their 
bullying experiences, and how these had affected them. The project began with 
distributing 523 open-ended questionnaires to students in three secondary 
schools and two universities in Sweden. The questionnaire asked about their 
past experiences with bullying and their willingness to be interviewed about it. 
Of 511 responses, 168 students reported having been bullied in the past and 36 
volunteered to be interviewed. The researchers interviewed the 21 students 
who had been bullied for more than one year. Halldin, Bolmsjö and Petersson, 
who conducted all the interviews, were instructed and trained in qualitative 
interviewing. Quality criteria from the literature guided their interviewing, 
such as establishing a comfortable, private, and quiet interview setting build­
ing rapport and emphasizing with the interviewee; taking the role of an 
interested learner; active listening; using open-ended questions and avoiding 
leading questions; probing; and adopting a non-judgmental approach (e.g., 
King & Horrocks, 2010; Kvale & Brinkmann, 2009; Seidman, 2006).
In constructivist grounded theory (Charmaz 2014), interviewing is not 
considered as efforts to mirror reality but as emergent interactions through 
a mutual exploration of the interviewee’s experiences and perspectives. 
Therefore, the interviewer’s approach and way of asking questions, listening 
and following up what the interviewee is telling are crucial in the co- 
construction and quality of data. Such an approach, based on the qualitative 
interview literature, helped Thornberg et al. (2013) to gather vivid, rich, and 
comprehensive data. In accordance with grounded theory, they moved back 
QUALITATIVE RESEARCH IN PSYCHOLOGY
13


and forth between gathering and analyzing data (iterative process). This 
approach has four advantages. It prevented them from (1) gathering data in 
a superficial and random way; (2) feeling overwhelmed due to a huge amount 
of unanalyzed data; (3) being unfocused for lengthy periods; and (4) uncriti­
cally adopting the participants’ view or stock disciplinary categories.
Thornberg et al. (2013) constructed codes that fit the data by engaging in 
initial coding, in which they compared data with data, data with codes, and 
codes with codes, and stayed close to and remained open to exploring what 
they interpreted was happening in the data. The provisional, initial codes were 
carefully compared with each other and with data, further elaborated and 
grouped together based on similarities and differences, leading to fewer but 
more focused and comprehensive codes. As a result of the iterative process, 
coding and constant comparison, their constructed focused codes fit tightly 
with their data. For example, the focused code ‘self-inhibiting’ had its roots in 
initial codes like ‘trying to not stand out’, ‘becoming passive out of social fear’, 
‘keeping oneself down’, ‘believing social invisibility prevents bullying’, ‘inhi­
biting the social presence of self’, and ‘becoming silent’. Another focused code, 
‘self-doubting’, had its roots in initial codes like ‘becoming insecure’, ‘feeling 
self-worthlessness’, ‘loss of self-confidence’, and ‘getting bad self-confidence 
from being bullied’, as indicated in Tables 1 and 2.
Thornberg and colleagues’ comparative approach to data collection and 
analysis (2013) during their focused coding included theoretical sampling as 
a prominent part of the iterative process, which guided their later interviews 
and helped them to fill out emerging categories. As noted by Charmaz (2014), 
their focused codes provided the makings of a frame for the later analysis. 
‘Rather than using these focused codes as the final frame, however, Thornberg 
et al. (2013) developed an analysis of processes involved in becoming a victim 
of school bullying and in extricating self from it’ (Charmaz 2014, 141).
Grounded theory methods of focused coding, theoretical coding, constant 
comparison, memo-writing and theoretical sampling guided Thornberg et al. 
(2013) to merge focused codes such as ‘self-inhibition’, ‘self-isolation’, and 
‘turning off emotions’ into an even more comprehensive focused code devel­
oped into a category that they labeled ‘self-protecting’, in which the former 
focused codes that constituted this category were included as subcategories 
representing different self-protecting strategies. Other groups of focused codes 
were further merged into other more comprehensive categories such as ‘a 
sense of not fitting in’ and ‘self-blaming’. The interplay between co- 
constructing rich data, constant comparison, focused coding, theoretical cod­
ing, memo-writing and theoretical sampling was crucial in order to develop 
a set of conceptual categories anchored in the data.
This iterative process helped Thornberg et al. (2013) to construct a major 
category – what Glaser (1998) and Corbin and Strauss (2015) would refer to as 
the ‘core concept’ of the study – namely ‘double victimizing’. This major 
14
K. CHARMAZ AND R. THORNBERG


category refers to an interplay and cycling process between external victimiz­
ing (i.e., a social process of repeated harmful acts directed at the victims that 
confirmed their victim role among the peers) and internal victimizing (i.e., 
victims incorporated the victim-image produced by external victimizing at the 
same time as they tried to develop strategies in order to protect themselves). 
Like the term core concept (Corbin and Strauss 2015; Glaser 1998), ‘double 
victimizing’ was indeed the most significant and frequent code, and was 
related to as many other codes as possible and accounted for more data than 
other categories.
A grounded theory of this double victimizing was finally constructed in 
which the other categories were parts of the double victimizing or in other 
Table 1. Initial coding.
Initial Coding
Interview Data
Becoming insecure; 
Self-doubting; Loss of self-confidence; Thinking 
bullying depends on wrongness with self; 
Believing bullies’ negative image of you; 
Getting bad self-confidence from being bullied; 
Becoming passive out of social fear;    
Believing of the wrongness with self as a result of 
being bullied; Feeling self-worthlessness; Being 
globally disliked;    
Being bullied because of being different; 
The constant message of being nerdish; 
A sense of not fitting in as a result of being 
bullied; Inferring social deviance of self from 
the experiences of peer victimization; 
A lingering sense of being different;      
Avoiding bullying;  
Inhibiting the social presence of self; Believing 
social invisibility prevents bullying; Inaction 
protects self from embarrassment and teasing     
Standing out leads to more bullying; 
Becoming silent; 
Avoiding attention;
Interviewer: How did the bullying affect you during this 
period? 
Eric: I started to feel very insecure. In other words, I started to 
doubt myself more and more. I lost my self-confidence. 
I thought there has to be something wrong with me, 
because otherwise they wouldn’t have picked me as 
a victim. I believed all the stupid things they said about 
me. So, I really got very bad self-confidence from all the 
bullying. I really didn’t dare to do things I wanted to do 
when other people were nearby. 
Interviewer: The bullying gave you bad self-confidence? 
Eric: Yes, and it made me believe there was something wrong 
with me, that I was stupid. I felt worthless, that no one 
would like to be with me. 
Interviewer: You said before that you thought they bullied 
you because there was something wrong with you. Can 
you tell me more about that? 
Eric: Because I was a different or a bit odd, I wasn’t like them. 
Interviewer: You became bullied because you were different? 
Eric: Yeah, that was what I was told all the time, that I was 
a nerd, I wore ugly clothes and stuff like that. But it was 
only when the bullying started that I began to feel 
different, that I didn’t fit in. I didn’t think like that before. 
But when they started to tease me, push me around, and 
when I was frozen out all the time, I began to 
understand that I was different. I can still remember that 
feeling. 
Interviewer: What did you do when you got bullied at school? 
Eric: I tried to avoid it. 
Interviewer: How? 
Eric: For example, by not putting my hand up during the 
lessons, being quiet and not standing out. I thought if 
I didn’t stand out, if they wouldn’t notice me, then they 
wouldn’t bully me. If I didn’t say or do things when other 
people were around, nothing embarrassing would 
happen, no one would tease me. 
Interviewer: What do you mean? 
Eric: Well, if I said something, if I tried to take some space, 
then they would just say, ‘We have to put him down! We 
have to bully him even more!’ So, the best thing was to 
be quiet and not be noticed.
QUALITATIVE RESEARCH IN PSYCHOLOGY
15


ways related to it. For instance, whereas external victimizing consists of the 
social processes of stigmatizing by being labelled as different and social 
excluding, internal victimizing includes a sense of not fitting in, distrusting 
others, self-protecting, self-doubting, self-blaming, and resignation. Initial 
attacks antedated double victimizing, and when double victimizing ended in 
terms of bullying exit, after-effects of bullying included a lingering internal 
victimizing. In this study, theoretical sampling was necessary to reach theore­
tical saturation in terms of having constructed a grounded theory with theo­
retical completeness (Charmaz 2014; Glaser, 2001).
The carefully conducted interviews and the systematic use of grounded 
theory methods made sure that the final grounded theory of double victimiz­
ing in school bullying reached empirical grounding (Strauss and Corbin 1998) 
as the theory and its concepts clearly fitted with the data (Glaser 1998), and 
demonstrated credibility (Charmaz 2014; Strauss and Corbin 1998) and trust­
worthiness (Strauss and Corbin 1998). In addition, the empirical grounding of 
the theory led to: (a) workability (Glaser 1998) as the developed middle-range 
theory contributes to explain the link between bullying victimization and 
mental health problems in the literature (e.g., Klomek, Sourander, and 
Elonheimo 2015) from the voice and perspectives of the participants; (b) 
abstract understanding (Charmaz, 2017) as it contributes to increase our 
understanding of meanings and actions and how victims construct them; 
and (c) relevance (Glaser 1998) and resonance (Charmaz 2014) as participants 
in the study and other victims of school bullying could recognize most or all 
processes included in the grounded theory of double victimizing. Due to the 
open and exploratory approach, the developed grounded theory showed 
originality (Charmaz 2014) as it contributes to the literature by offering new 
insights and proving a fresh conceptualization of victims’ experiences and path 
through school bullying and beyond. As a result of empirical grounding, fit, 
Table 2. Focused coding.
Focused Coding
Interview Data
Self-inhibiting         
Self-doubting
Eric: For example, by not putting my hand up during the lessons, being quiet and not standing 
out. I thought if I didn’t stand out, if they wouldn’t notice me, then they wouldn’t bully me. 
If I didn’t say or do things when other people were around, nothing embarrassing would 
happen, no one would tease me. 
Interviewer: What do you mean? 
Eric: Well, if I said something, if I tried to take some space, then they would just say, ‘We have to 
put him down! We have to bully him even more!’ So, the best thing was to be quiet and not 
be noticed.  
Ann: I felt that there had to be something very wrong with me because everyone picked on me. 
I felt that I was worthless. I felt that I really must be a boring–, a very boring person because 
everyone avoided me and because they teased me and because of all things they did to me. 
I never thought that I didn’t want to live anymore. I didn’t think that way. I don’t think I did. 
At least I can’t recall I did. I just felt that I must be messed up in my head, and that I was 
much more inferior to the others.
These tables first appeared in Thornberg and Charmaz (2014).
16
K. CHARMAZ AND R. THORNBERG


credibility, relevance, workability and resonance, Thornberg et al. (2013) 
grounded theory of double victimizing also demonstrated usefulness 
(Charmaz 2014) as it contributes to creating new lines of research, clarifies 
bullying victim experiences, and reveals that an inner victimizing seems to 
continue several years after bullying has ended, which has anti-bullying 
implications. In accordance with the pragmatistic view of knowledge and 
knowing, underlying the constructivist grounded theory approach (Charmaz 
2006, 2014; Thornberg 2012), Thornberg et al. (2013) grounded theory of 
double victimizing is considered to be situated, fallible and provisional, and 
therefore embraces modifiability (Glaser 1998). New data may revise and 
elaborate the theory further.
Quality in constructivist grounded theory: checklists and guidelines
We conclude by offering guidelines that graduate and postgraduate students 
and beginners in constructivist grounded theory can use as a checklist while 
conducting their work. Use the list as a resource, not as a substitute for 
rigorous research. As Barbour (2001) observes, using checklists risks becoming 
a methodological crutch that avoids deep engagement with the method. We 
strongly advocate that researchers gain a deep engagement with their method 
and data.
Although we can discern similarities and differences between various ver­
sions of grounded theory, we offer a general set of guidelines. At the same time, 
we recommend you learn more about issues of rigor, credibility and quality 
emphasized in the methodological discourse of the specific version of 
grounded theory that you choose to use. So just take these points as flexible 
guidelines to consider when conducting qualitative inquiry in general or 
constructing a grounded theory study in particular.
(1) Strive to achieve methodological self-consciousness (Charmaz 2017). 
Why have you chosen the specific topic, methodology and methods, 
and how do these fit with who you are and your research objectives 
and questions? What version of grounded theory have you adopted 
and why? What are the ontological and epistemological assumptions, 
and what do these mean for the research process, researcher position, 
findings, and quality issues, including transferability?
(2) Learn everything you can about the type of qualitative inquiry you 
adopt, whether it’s narrative inquiry, discourse analysis, or a version of 
grounded theory. If possible, work with a mentor who is knowledge­
able about your approach.
(3) Take an open, non-committal, critical, analytic view of the existing 
literature in the field. In contrast to Glaserian grounded theory but in 
line with Straussian and constructivist grounded theory, we 
QUALITATIVE RESEARCH IN PSYCHOLOGY
17


recommend that you review the literature to establish a defensible 
rationale for the study, to avoid re-inventing the wheel, and to increase 
theoretical sensitivity. Treat the literature as provisional and fallible, 
not as the Truth (for further reading, see Thornberg 2012; Thornberg 
and Dunne 2019).
(4) Gather rich data. For psychologists, rich data usually means learning 
and collecting the stories of people who have had or are having 
a specific experience. Rich data means an openness to the empirical 
world and a willingness to try to understand the experiences of people 
who may be far different from you.
(5) Be transparent. Describe how you conducted your study, obtained 
your sample and state how and why you have included the partici­
pants, and how you have used grounded theory and data collection 
methods. Include justifications of your choices.
(6) Go back and forth between data and your developing analysis to focus 
your subsequent data collection and to fill out your emerging analytic 
categories.
(7) Tolerate ambiguity while you struggle to gain intimate familiarity with 
the empirical world and to create an analytic handle to understand it.
(8) As you proceed, ask progressively focused questions about the data 
that help you develop your emerging analysis.
(9) Play with your data and your ideas about it. Look for all possible 
theoretical explanations of the data and check them.
(10) Collect sufficient data to (a) make useful comparisons, (b) create 
robust analytic categories, and (c) convince readers of the significance 
of your categories.
(11) Ask questions about your categories: What are their properties? In 
which ways do they subsume minor categories? How are your main 
categories connected? How do they make a theoretical statement? 
What is the significance of this statement?
(12) Always treat your codes, categories and theoretical outlines as provi­
sional and open for revision and even rejection in the light of new data 
and further analysis.
(13) After you have completed your analysis, compare it with relevant 
material from the literature, which may well include case studies and 
perspectives that you did not address during your earlier review. At 
this time, your review will be focused on the ideas that you have 
developed. This review gives you the opportunity to show how your 
analysis fits, extends, or challenges leading ideas in your field.
Disclosure statement
No potential conflict of interest was reported by the authors.
18
K. CHARMAZ AND R. THORNBERG


Notes on contributors
Kathy Charmaz is professor emerita of sociology at Sonoma State University. She has written, 
co-authored, or co-edited several books, including two award-winning works: Good Days, Bad 
Days: The Self in Illness and Time and Constructing Grounded Theory. Charmaz has recently 
published The Social Self and Everyday Life: Understanding the World through Symbolic 
Interactionism with co-authors Scott R. Harris and Leslie Irvine and is a co-editor of 
the Handbook of Current Developments in Grounded Theory with Antony Bryant. She has 
also written or co-authored numerous articles and chapters about qualitative research.
Robert Thornberg is professor of education at Linköping University. His current research is on 
school bullying, especially with a focus on social and moral processes involved in bullying, 
bystander rationales, reactions and actions, and students' perspectives, experiences and expla­
nations. He is also engaged in research on student teachers' experiences of distressed situation 
during teacher training, and research on values education in everyday school life. Thornberg 
has published numerous empirical articles in a range of peer-reviewed journals and written or 
co-authored chapters on grounded theory in various method books.
ORCID
Robert Thornberg 
http://orcid.org/0000-0001-9233-3862
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QUALITATIVE RESEARCH IN PSYCHOLOGY
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Classic Grounded Theory


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Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne


Classic Grounded 
Theory
Applications With Qualitative 
and Quantitative Data


Judith A. Holton
Mount Allison University, Canada
Isabelle Walsh
SKEMA Business School, France


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Names: Holton, Judith A., author. | Walsh, 
Isabelle, author.
Title: Classic grounded theory : applications 
with qualitative and quantitative data / Judith 
A. Holton, Mount Allison University, Canada, 
Isabelle Walsh, SKEMA Business School, 
France.
Description: Los Angeles : SAGE, [2017] | 
Includes bibliographical references and index.
Identifiers: LCCN 2015041977 | 
ISBN 9781483372549 (pbk. : alk. paper)
Subjects:  LCSH: Grounded theory.
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Brief Contents
Preface
xi
About the Authors
xxi
An Overview of Classic Grounded Theory
Chapter 1: 	
Situating Grounded Theory on the 
Research Landscape
1
Chapter 2: 	
Discovering New Theory as the End 
Purpose of Classic Grounded Theory
15
Chapter 3: 	
Foundational Pillars of Classic Grounded Theory
28
Chapter 4: 	
Clarifying Common Sources of Confusion in 
Grounded Theory
46
Classic Grounded Theory in Practice
Chapter 5: 	
Finding Your Data
57
Chapter 6: 	
Analyzing Your Data
76
Chapter 7: 	
Shaping the Theory
99
Writing and Evaluating Classic Grounded Theory
Chapter 8: 	
Writing as an Important Part of Doing Classic GT
120
Chapter 9: 	
Writing Classic GT for Publication
134
Chapter 10: 	 Evaluating Classic Grounded Theory
152


Conclusion
159
Appendix A: Doing Qualitative Research Does Not Mean 
You Are Doing Classic GT
162
Appendix B: Classic GT Using Qualitative Data
167
Appendix C: Doing Classic GT Using Mixed Qualitative 
and Quantitative Data
190
Appendix D: Some Examples of Theoretical Coding 
Using Mixed Qualitative and Quantitative Data
201
Appendix E: Examples of Diagrams as Precursors of a Theory
206
Glossary
210
References
215
Index
228


Detailed Contents
Preface
xi
Our Philosophical Perspective  xii
Some Definitions  xiii
How We Have Organized This Book  xvi
Acknowledgments  xvii
About the Authors
xxi
An Overview of Classic Grounded Theory
Chapter 1: 	 Situating Grounded Theory on the 
Research Landscape  1
The Early Development of Grounded Theory  1
Barney Glaser’s Early Influences and Ideas  2
Glaser and Strauss: From Collaboration to Difference  4
Are Glaserian and Straussian GT Reconcilable?  6
The Nature and Power of Classic GT  10
Emergence of the Classic GT Paradigm  11
In Summary  12
Test Your Knowledge  12
Further Reading  13
Chapter 2: 	 Discovering New Theory as the End Purpose of 
Classic Grounded Theory  15
What Is a Theory?  16
The Drive From Substantive to Formal GT  20
Theorizing in Rupture With Existing Literature  25
In Summary  26
Test Your Knowledge  26
Further Reading  27


Chapter 3: 	 Foundational Pillars of Classic Grounded Theory  28
Emergence  30
Constant Comparative Analysis  33
Theoretical Sampling  37
Developing Your Stance as a Grounded Theorist  41
In Summary  43
Test Your Knowledge  43
Further Reading  44
Chapter 4: 	 Clarifying Common Sources of Confusion in 
Grounded Theory  46
Emergence Versus Preconception  47
Description Versus Conceptualization  50
Substantive Versus Theoretical Coding  52
In Summary  54
Test Your Knowledge  55
Further Reading  55
Classic Grounded Theory in Practice
Chapter 5: 	 Finding Your Data  57
Why “All Is Data”  57
Overlooked Sources of Available Data  62
Collecting Your Own Data  64
Gaining Access  64
Instilling a Spill  67
Field Notes  70
Data Collection and Analysis as an Iterative Process  72
In Summary  73
Test Your Knowledge  74
Further Reading  74
Chapter 6: 	 Analyzing Your Data  76
Conceptual Analysis as Distinct From Descriptive Analysis  78
Using Constant Comparative Analysis  78
Types and Stages of Coding in Grounded Theory  80
Open Coding  81
Selective Coding  83
Theoretical Coding  86
Core Category Emergence  87
The Key Role of Analytic Memoing in GT Analysis  89
The Iterative Nature of Data Analysis in Grounded Theory  94
To Software or Not to Software  95


In Summary  96
Test Your Knowledge  97
Further Reading  98
Chapter 7: 	 Shaping the Theory  99
Theoretical Sampling  100
Theoretical Saturation  103
Theoretical Coding  104
Theoretical Sorting  109
Analytic Rules  113
In Summary  118
Test Your Knowledge  118
Further Reading  119
Writing and Evaluating Classic Grounded Theory
Chapter 8: 	 Writing as an Important Part of Doing 
Classic GT  120
The Process Leading to the Readiness to Write Moment  121
Approaching the Literature in a GT Study  124
Writing GT With Mixed Quantitative and Qualitative Data  125
The First Draft  127
Writing Blocks  128
Personal Blocks  129
Methodological Blocks  130
External/Contextual Blocks  131
In Summary  132
Test Your Knowledge  132
Further Reading  133
Chapter 9: 	 Writing Classic GT for Publication  134
Doing Versus Writing  134
Rhetorical Issues: The Way We Write  135
Ethical Issues: Adjusting Your Work to Fit Established 
Conventions  137
Methodological Issues: Writing to Be Read and Published  138
Writing Classic GT Using Mixed Data  143
Writing Conventions  146
Some Guidelines for Publishing Classic GT in Top Tier Research 
Journals  148
In Summary  150
Test Your Knowledge  150
Further Reading  151


Chapter 10: 	
Evaluating Classic Grounded Theory  152
The Consequences of the Paradigm War  152
Criteria for Evaluating Grounded Theory  154
In Summary  157
Test Your Knowledge  157
Further Reading  158
Conclusion
159
What Is GT, and Why the Continual Remodeling?  161
Appendix A: Doing Qualitative Research Does Not Mean 
You Are Doing Classic GT
162
Appendix B: Classic GT Using Qualitative Data
167
Appendix C: Doing Classic GT Using Mixed Qualitative 
and Quantitative Data
190
Appendix D: Some Examples of Theoretical Coding Using 
Mixed Qualitative and Quantitative Data
201
Appendix E: Examples of Diagrams as Precursors of a Theory
206
Glossary
210
References
215
Index
228


xi
Preface
S
ince the publication of The Discovery of Grounded Theory  (Glaser 
& Strauss, 1967), grounded theory (GT) has grown in popularity 
to become one of the most frequently cited research methodolo­
gies. GT researchers come from a wide range of professional fields, 
such as social work, nursing, medicine, allied health, education, man­
agement, and business. They are practitioners and scholars seeking to 
explain patterns of behavior from within their disciplines. Grounded 
theories offer a “sensitizing recognition” (Glaser, 1998, p. 62) through 
high-impact variables that succinctly yet eloquently explain experi­
enced reality. The wide interest in GT has resulted in many journal 
articles and texts intended to guide researchers in using the methodol­
ogy. This interest in studying the nature and practice of GT—what 
Glaser has so aptly termed “the rhetorical wrestle” (Glaser, 1998, Chap. 
3)—extends to established scholars as well as the novice researcher 
(Walsh, Holton, Bailyn, Fernandez, Levina, & Glaser, 2015a).
The “draw and grab” (Glaser, 1998, p. 62) of GT is highly motivat­
ing, but learning and doing GT is a delayed action learning process 
(Glaser, 1978, p. 6; 2001, p. 1; 2003, p. 78). The importance of learning 
through doing cannot be overestimated. As Glaser has often com­
mented in his troubleshooting seminars, a grounded theory (a theory 
that is grounded in data) is asymptotic; it is very close to what is hap­
pening but never fully captures reality. Its propositional nature allows 
for its modification with additional data and with further skill develop­
ment on the part of the theorist. What one misses in the first grounded 
theory study can be realized and modified in subsequent studies.
Novice classic grounded theorists often find that they are breaking 
new territory at their institutions. They are striving to master a 
­
methodology that not only is new to them but also is often unfamiliar 
to supervisors and colleagues or misinterpreted as a qualitative 
research approach situated within an interpretivist paradigm. While it 


CLASSIC GROUNDED THEORY
xii
has become common practice for many qualitative researchers to apply 
some GT techniques in their data analysis (e.g., constant comparison, 
theoretical sampling)—drawing on the “tenets of GT” so to speak—it 
is important to differentiate such practices from studies that employ 
full classic GT methodology.
Our goal in this text is twofold. First, we take the reader back to the 
roots of GT to illustrate how those roots are important to a full appreciation 
of GT as much more than a qualitative method. Our purpose is in no way 
intended to suggest that the immensely productive collaboration between 
Glaser and Strauss was not an important milestone in the development of 
GT. However, we feel strongly that failing to recognize the significance of 
GT’s early roots has led some scholars to accord to GT a paradigmatic 
position that not only has restricted its use to qualitative data but, in 
doing so, has remodeled and reduced its theory-generating power.
Second, we offer practical guidance in undertaking a research 
study staying true to the classic GT practice paradigm and using the 
full classic GT methodology package. The topics covered in this text are 
grounded in the experiences of many novice researchers who have 
participated in GT troubleshooting seminars or who have contacted us 
with questions and problems. The challenges faced are universal in 
that they are routinely encountered as part of the GT learning curve. 
Offering some practical advice and examples for troubleshooting these 
challenges will help readers develop their own GT expertise.
l
l
OUR PHILOSOPHICAL PERSPECTIVE
In this text, we emphasize the philosophical flexibility of classic GT as a 
“full package” approach that can be applied to any study and any type 
of data where the goal is to discover and generate a conceptually inte­
grated theory. While GT is philosophically unbiased, we believe that no 
methodological book can be fully epistemologically and ontologically 
neutral. Hence, it is essential to state from the outset that both authors of 
this text are critical realists, and this has probably influenced the exam­
ples that we have chosen to illustrate various aspects of the GT process.
A discussion about critical realism is beyond the purpose of this 
book. However, it might be useful to stress that, for a critical realist, real­
ity is multifaceted and may be perceived differently by different indi­
viduals and in different contexts. Critical realists consider, however, that 
an intransitive domain exists independent of our perceptions. In the 
ontological domain, critical realism holds a realist view and, in the epis­
temological domain, it accepts the “relativism of knowledge as socially 


Preface
xiii
and historically conditioned” (Mingers, 2004, p. 91). Through retroduc­
tion, critical realism aims to discover the underlying structures that 
generate specific patterns of events or non-events, and welcomes quan­
titative and/or qualitative data, methods, and techniques. The critical 
realist retroductive approach to knowledge creation, well elaborated by 
Zachariadis, Scott, and Barrett (2013), is fully congruent with the neces­
sary emergent quality of grounded theories.
It is also essential to clarify what causality means for a critical realist, 
as theories are often conceived as centered on causality, that is, the rela­
tionship between a cause and an event (Gregor, 2006). The notion of 
causality as a generative mechanism is a core and defining feature of criti­
cal realism (Bhaskar, 2002). Generative mechanisms are best understood 
as tendencies, as their activation is highly context dependent (Bhaskar, 
2002). In contrast with the Humean vision of causality (A causes B) com­
monly accepted in traditional positivist quantitative circles, a generative 
mechanism can be reformulated as “A generates B in context C” 
(Cartwright, 2003; Smith, 2010). For a critical realist, causality is, thus, a 
process of how causal powers are actualized in some particular context; 
a process in which the generative mechanisms of that context shape 
(modulate, dampen, etc.) the particular outcomes. For instance, wood in 
a camp fire (A) will have a tendency to generate heat (B) if somebody has 
put together the right elements for the fire to burn adequately and has 
put a light to it and if it does not rain on the fire (C). Thus, even though 
some regularity in events may be empirically derived, critical realism 
does not look for universal laws and recognizes the significant role of 
context, social structures, and individual agencies in causal explanations.
	
SOME DEFINITIONS	
l
We refer the reader to Walsh (2014c) for a full discussion on mixed 
design GT and the various works that may be found in the literature to 
fit such a description. Some of these works use, within the same 
research project, an exploratory GT stance with qualitative data and a 
confirmatory hypothetical-deductive stance with quantitative data. In 
this book we are concerned with adopting an exploratory GT stance 
with any data, both qualitative and/or quantitative, and from any 
philosophical/epistemological perspective that may be adopted by a 
researcher. When we mention “mixed GT” or “GT using mixed data,” 
it implies that all data are analyzed as one set and that the constant 
comparative analyses of qualitative and quantitative data are not sepa­
rated but embedded.


CLASSIC GROUNDED THEORY
xiv
At the outset, it is also essential to propose some definitions for 
constitutive elements that may be found in the design of any 
research project. As highlighted by Mingers (2001), certain words 
may be interpreted in many different ways by different researchers; 
this sometimes leads to misunderstandings. To avoid this, it is 
important to define some terms. We do not claim these definitions 
to be correct across domains, but they will be used consistently 
throughout this book.
Data are the necessary basis on which we build theories (Evermann 
& Tate, 2011, p. 634), and in most cases, we need good data to produce 
good theory (Lyytinen, 2009). Creswell (2014) refers to qualitative data 
as consisting of text and images and quantitative data as numbers. The 
term quantitative data is mostly used to describe a type of information 
that may be counted or expressed numerically and is amenable to sta­
tistical analysis whereas qualitative data provides explanation for and 
information about something in the form of words and involves 
­
in-depth description (Monette, Sullivan, & Delong, 2011). However, a 
researcher may choose to quantitize qualitative data (Sandelowski, 
Voils, & Knafl, 2009) or to qualify (Creswell, 2007), or qualitize 
(Sandelowski, 2000), quantitative data. A researcher may also use sec­
ondary data in various ways, with different techniques, regardless of 
the methods used to collect these data.
Methods are the data-collection methods used in the research 
project, such as interviews, observation, filming, or surveys. 
Contrary to commonly accepted beliefs, we do not consider that 
specific methods will produce specific data: Methods traditionally 
understood as quantitative may produce qualitative data (e.g., one 
may include open-ended questions in a survey that will yield 
qualitative data), and methods traditionally understood as 
qualitative may produce data that may subsequently be quantitized 
(Sandelowski et al., 2009), for example, the number of times a term 
is found in an interview.
Techniques are the instruments used in the research project to help 
analyze and make sense of the collected data, such as text analysis, 
cluster analysis, or structural equation modeling. Here again, we 
depart from traditional beliefs considering qualitative and quantitative 
instruments as completely distinct. Some techniques traditionally 
believed to belong to the qualitative realm may be perceived to be 
extremely close to the quantitative realm. For instance, Glaser’s (1978) 
substantive and theoretical coding may be realized through text 
analysis but also through elaboration analysis, factor/cluster analyses, 


Preface
xv
and soft structural equation modeling—traditionally considered as 
quantitative techniques and often used in quantitative studies.
Methodology is the specific combination of research methods and 
techniques used in a research project. Each research project may be 
considered as having a specific methodology, as we understand 
researchers as important stakeholders in their own research, who may 
adapt the methods and techniques they adopt.
Framework is the general set of guidelines proposed by some 
authors that a researcher may choose to follow in a given project, such 
as action research (Baskerville & Pries-Heje, 1999), case-study research 
(Eisenhardt, 1989), quantitative grounded theory (Glaser, 2008), or 
Straussian grounded theory (Strauss, 1987).
Paradigm has been understood and used in social sciences at 
different levels of generality (Morgan, 2007). The four versions of 
paradigms found in the literature and highlighted by Morgan (2007) 
are worldviews, epistemological stance, shared beliefs in a research 
field, and model examples. These are not mutually exclusive but 
“nested within each other” (p. 54). In all four versions, paradigms are 
“shared belief systems that influence the kinds of knowledge researchers 
seek and how they interpret the evidence they collect” (p. 50).
Another issue that has to be addressed up front is the meaning of 
the term grounded theory itself, because it leads to misunderstanding as 
already highlighted by Bryant and Charmaz (2007). When asked the 
question, “What is grounded theory?” Barney Glaser has responded 
that GT is a set of “interrelated grounded concepts based on an 
emergent theoretical code.”1 Walsh, Holton, Bailyn, Fernandez, Levina, 
and Glaser (2015b) elaborate on this definition:
GT is the systematic generation of theory from data that has 
itself been systematically obtained (Glaser, 1978, p. 2). GT’s 
exploratory approach may be adopted irrespective of the 
researcher’s philosophical positioning. It may include 
qualitative and quantitative data, or both.
The term grounded theory describes at the same time both the 
research process (the way the research is conducted) and the end result 
(a new theory that is empirically grounded in data); it also serves as a 
guide for the research design. In this book, and while also contextual­
izing our discourse, we use the abbreviation GT when we mean the 
research process and the full term grounded theory when we mean 

the end result of a GT study.


CLASSIC GROUNDED THEORY
xvi
Chapters 1 to 4 provide an overview of classic GT by situating GT on 
the research landscape (Chapter 1), discussing theory discovery as the 
end purpose of GT (Chapter 2), elaborating the foundational pillars of 
classic GT (Chapter 3), and clarifying common sources of confusion in 
GT (Chapter 4).
Chapters 5 to 7 offer practical guidance in the application of a 
­
classic GT framework using both qualitative and quantitative data. 
Chapter 5 explores the process of data collection, Chapter 6 guides the 
reader through classic GT’s data analysis procedures, and Chapter 7 
elaborates the various procedures applied in classic GT for shaping the 
emergent theory.
Chapters 8 to 10 offer the reader advice on writing as an important 
part of the classic GT process (Chapter 8) as well as on writing for an 
audience, that is, for publication (Chapter 9). Chapter 10 addresses the 
issue of evaluating grounded theories.
Each chapter features a short quiz (Test Your Knowledge) to help 
you quickly check your knowledge and understanding of the mate­
rial covered. We identify additional works (Further Reading) at the 
end of each chapter that may also assist in enhancing your knowl­
edge of classic GT. Also included are five appendices, to which we 
regularly refer as illustrations of some issues examined in several 
chapters.
Appendix A provides comparative examples of data collection and 
analysis using a preformulated, descriptive qualitative approach with 
classic GT’s emergent, conceptual approach.
Appendix B provides an overview and selected excerpts from 
Holton’s (2006) thesis, illustrating the application of classic GT to 
qualitative data.
Appendix C provides selected methodological excerpts from 
Walsh (2014a) illustrating the use of a classic GT approach with mixed 
data, methods, and techniques.
Appendix D is extracted and adapted from Walsh (2014c). It provides 
some examples of theoretical coding using mixed qualitative and quanti­
tative data. It also illustrates how the literature may be used as secondary 
data, once the core category of a grounded theory has emerged.
Appendix E is extracted from Walsh, Renaud, and Kalika (2013) 
and shows an example of diagrams that helped the emergence of a 
theory and guided its writing up.
Finally, while we have no intention in this book to replicate or 
summarize Barney Glaser’s many publications on classic grounded 
l
l
HOW WE HAVE ORGANIZED THIS BOOK


Preface
xvii
This book has been “in the works” for more than 10 years. In 2004, 
Barney Glaser suggested that I write a “troubleshooting book” based 
on the many questions and blocks to doing classic GT that the novice 
researcher encounters. I have since had the privilege of attending 
many of Barney’s troubleshooting seminars as well as offering several 
seminars myself internationally. Many of these seminars were collabo­
rations with Fellows of the Grounded Theory Institute, including Tom 
Andrews, Toke Barfod, Ólavur Christiansen, Foster Fei, Astrid 
Gynnild, Tina Johnston, Andy Lowe, Vivian Martin, Antoinette 
McCallin, Alvita Nathaniel, Anna Sandgren, Helen Scott, Odis 
Simmons, Michael Thomas, and Hans Thulesius. The seminars and 
my 8 years as editor in chief of The Grounded Theory Review have pro­
vided me with a greater appreciation for the power of doing classic GT 
as well as offering valuable insights into the challenges met by those 
new to the GT experience. In conversations with Barney over the ensu­
ing years, I have had my perspective at times validated and at other 
times challenged! All has been wonderful learning for which I owe 
Barney a great debt of gratitude.
A chance e-mail exchange in October 2012 (in response to an 
Academy of Management electronic mailing list posting titled “Whoever 
said that grounded theory (GT) was a qualitative methodology?”) has 
led to another fruitful collaboration with my coauthor, Isabelle Walsh. 
In a very short period of intense exchange, our collaboration has 
included co-organizing both a symposium (2013) and a professional 
development workshop (2014) on GT through the Academy of 
Management, as well as the publication of a special issue on grounded 
theory in a top-tier journal, Organizational Research Methods.
Isabelle and I have had very different GT learning journeys but 
share a common philosophical perspective as critical realists and as 
management scholars. In this collaboration as well, I have at times had 
my perspective on GT both validated and challenged. Our collaboration 
has further stretched my learning and helped to firm up the unique 
concept for this book—a guide to the fundamentals of classic GT 
highlighting applications with both qualitative and quantitative data.
—Judith Holton, September 2015
theory, we make frequent reference to his work. Studying the original 
work of Barney Glaser is a must for anyone seeking to do classic 
grounded theory (CGT).
	
ACKNOWLEDGMENTS	
l


CLASSIC GROUNDED THEORY
xviii
I was not directly trained by Barney Glaser as such. I only attended 
one of his seminars, which I organized in Paris during the spring of 
2014. Beyond the very limited aspects of GT that I learned from some 
methodological seminars I attended during my doctoral years, my 
knowledge of GT is empirically grounded (I learned about GT as I was 
doing it). However, I have to highlight that I was guided by Barney’s 
numerous publications in my methodological endeavors. Through the 
years and as I was conducting my various research projects, every time 
I wondered what I should do methodologically, which methods or 
techniques I could use, I just referred to the seminal book about GT 
(Glaser & Strauss, 1967) and then to Barney’s subsequent writings. 
These freed my creativity, allowed me to produce some theories con­
sidered of some worth, and sent me on the way to becoming a recog­
nized scholar in my own research field.
I started collaborating with Judith and Barney only 3 years ago, after 
putting, out of desperation and loneliness, a post on the Academy of 
Management electronic mailing list: “Whoever said that grounded 
theory (GT) was a qualitative methodology?” This post was like a bottle 
thrown from my own little intellectual island into the ocean of knowledge 
that was out there. It was sent out of desperation as I had been working 
on a GT methodological article for the previous 5 years and was, at the 
time, fighting to have my vision of GT recognized by a top-tier journal 
of my research field. Going against the mainstream in one’s research 
field is difficult enough. Doing it alone is a thankless task that I was 
spared, at least for the past couple of years, thanks to Judith’s help. She 
was one of the many people who picked up and answered the message 
I had posted. Our subsequent meeting and the start of our friendship 
and work together happened at a time when I needed support to be able 
to express my findings. I could not have done it without her. I needed 
reassurance that what I understood as the scope and reach of classic GT 
were indeed what I saw them to be. And then, I wanted to share what 

I had found through the methodological article I had been working on 
for many years and which I finally published in 2014.
This post on the Academy of Management listserv marked a 
turning point in my intellectual journey to the land of theories. It 
brought within my reach fabulously rich intellects and opened the way 
to some deep friendships. The past few years have been interwoven 
with sharp intellectual challenges, and I certainly would not have 
achieved what I have done during these years without Judith and 
Barney’s encouragement and reassurance.
Judith is so close to Barney’s work and Barney’s thinking that, as 
we were cowriting this book, I would call her “Barnette” during our 


Preface
xix
intellectual fights! She has the unique asset of a thorough knowledge 
not only of Barney’s work but also of Barney himself as an exceptional 
man and of his thoughts and their evolution over the years.
The present book aims to give a clear and savory taste of classic GT 
and its fundamentals. Ultimately, it should motivate readers to start 
reading, or to read again with fresh eyes, Barney Glaser’s seminal 
publications.
—Isabelle Walsh, September 2015
Finally, we would be remiss if we did not acknowledge the guid­
ance, encouragement, and support that we have received from the 
editorial team of Sage Publications, in particular, Helen Salmon, Anna 
Villarruel, Veronica Stapleton Hooper, and Colleen Brennan. We are 
also grateful for the feedback of the following SAGE reviewers: Wayne 
A. Babchuk, University of Nebraska–Lincoln; Vivian B. Martin, Central 
Connecticut State University; Sebastien Point, EM Strasbourg Business 
School; and Karthigeyan Subramaniam, University of North Texas.
ENDNOTE
1.	 E-mail exchange with second author, February 12, 2015.
PowerPoint slides to accompany the text are available to instructors at 
study.sagepub.com/holton.




xxi
About the Authors
Judith A. Holton completed her Masters in Leadership at Royal Roads 
University, Victoria (Canada) and her PhD in Management Studies at 
the University of Northampton (UK). During her PhD research, she 
was closely mentored by Barney Glaser. She has written a number of 
methodological papers and coedited books about classic grounded 
theory and was the founding editor of The Grounded Theory Review as a 
peer-reviewed journal dedicated to classic grounded theory research. 
She is currently Associate Professor of Management in the Ron Joyce 
Centre for Business Studies at Mount Allison University, Sackville 
(Canada), where she teaches strategy, leadership, and organizational 
change.
Isabelle Walsh defended her PhD and HDR (Habilitation à Diriger des 
Recherches: Habilitation to Supervise Research) in Paris–Dauphine 
University (France). Beside methodological and research design issues, 
her research themes include information systems usage, cultural issues, 
and change management. Her research has been published in several 
international top tier outlets (the European Journal of Information Systems, 
the Journal of Strategic Information Systems, Organizational Research Methods, 
Systèmes d’Information & Management, etc.). Beyond her research achieve­
ments, she has extensive corporate and consultancy experience and is 
currently a full professor at SKEMA Business School in France and Head 
of the Project Management, Information Systems and Supply Chain 
department; her teaching is aligned with her research themes.




1
1
Situating Grounded Theory 
on the Research Landscape
After studying this chapter, you will:
• understand the origins of grounded theory
• understand grounded theory as a general research paradigm
	
THE EARLY DEVELOPMENT OF GROUNDED THEORY	
l
Barney Glaser has often commented that grounded theory (GT) was 
discovered, not invented (Glaser, 1992, p. 7; 1998, p. 21). He explains 
that after completing their famous awareness of dying study (Glaser & 
Strauss, 1965a), he and Anselm Strauss were frequently asked to 
explain how they had developed their theory. Glaser ­
suggested to 
Strauss that they write up their methodology; the result was the publi­
cation of The Discovery of Grounded Theory (Glaser & Strauss, 1967).1 
Although the book is generally acknowledged as the seminal work on 
GT, Glaser (1992, 1998) reveals that he had begun developing the meth­
odological ideas that would form the foundation of GT during his 
doctoral work at Columbia University. What were these ideas, and 
what precipitated their emergence?


AN OVERVIEW OF CLASSIC GROUNDED THEORY
2
Barney Glaser’s Early Influences and Ideas
Glaser completed his doctoral degree at Columbia University in 
1961, later publishing his dissertation (Glaser, 1964b). It is worth noting 
the societal context of the time as one of tension and turmoil with 
taken-for-granted ideas and accepted norms being challenged every­
where, including the scientific community where the preeminence of 
the positivist, hypothetical-deductive paradigm in scientific research 
was being persistently questioned, particularly in terms of its effective­
ness for the development of theory. This debate reflects the debate 
between the logic of discovery and the logic of verification.2 Gibson 
and Hartman (2014) suggest that Discovery clearly developed from this 
debate with Glaser and Strauss’s particular innovation being the speci­
fication of procedures for the discovery of theory from data (p. 75).
Hans Zetterberg, Glaser’s doctoral supervisor, has described the 
climate at Columbia at this time as a long-standing debate between 
those favoring the advancement of sociology through theory, led by 
Robert Merton, and those favoring its advancement through methodol­
ogy, led by Paul Lazarsfeld (Zetterberg, 1954, as cited in Holton, 2011). 
Theory was logically elaborated with empirical data used simply to 
verify conjectured hypotheses. In this environment, Glaser was exposed 
to departmental tensions and methodological innovations through the 
work of Lazarsfeld, Merton, and contemporaries (Glaser, 2008). He 
describes being steeped in Lazarsfeld’s elaboration analysis and the 
use of secondary data for discovering latent patterns and unanticipated 
relationships among variables. Key elements of GT would later emerge 
from quantitative methodology and “qualitative mathematics,” both 
taught by Lazarsfeld (Glaser, 1998, pp. 22–27).
Paul Lazarsfeld’s focus on methodology over methods and data and 
over ideology—his “quantomania” (Glaser, 2008, p. 4)—would inspire 
Glaser’s focus on theory discovery over verification. His methodological 
focus would aid in systematizing Glaser’s inherent propensity to iden­
tify and integrate ideas, to recognize and conceptualize patterns and 
structures in data. Lazarsfeld’s interest in identifying unobserved vari­
ables through latent structure analysis taught Glaser to avoid precon­
ceiving a research focus and to instead look for the unexpected, the 
unanticipated in the data.
Glaser has specifically acknowledged Lazarsfeld’s influence as 
“seeding” him with important methodological contributions to the 
development of GT: index formation, interchangeability of indicators, 
constant comparative analysis, and core variable analysis (Glaser, 
2005b).3 The first two methods come directly from Lazarsfeld’s work 


Chapter 1    Situating Grounded Theory on the Research Landscape
3
(Lazarsfeld & Thielsens, 1958, pp. 402–407) while Glaser used Lazars­
feld’s method of comparative analysis as a starting point for his own 
discovery and development of constant comparative analysis, a foun­
dational pillar of GT methodology. Glaser has commented that Lazars­
feld “missed this one.  .  .  . The power of this procedure to generate 
theory is phenomenal. What a theoretical yield of discovery. What a 
miss. The constant comparative technique became the influential 

analytic procedure of GT to generate and discover theory” (Glaser, 
2005b, in Holton, 2011, p. 208). Gibson and Hartman (2014) suggest that 
“rather than specifying a concept and then seeking out indicators to 
illustrate it, in the manner of Lazarsfeld, Glaser (1978) reversed the 
relationship and argued that indicators indicate concepts” (p. 75). This 
switch from comparative analysis for testing and verifying theory to 
theory discovery, they suggest, was a major departure in research 
methodology at the time and marked a significant methodological 
innovation in Glaser’s work.
Robert Merton’s approach to theory construction was also a con­
siderable influence on Glaser’s methodological development (Glaser, 
1998, pp. 29–31). From Merton, Glaser learned to read for ideas, under­
lining and noting concepts as they emerged from careful reading of 
text (Glaser, 1998, pp. 29–30). This close reading approach comple­
mented his earlier training in explication de texte (meaning literally, in 
French, “explanation of a text”), which he had acquired during a year 
of study at the Sorbonne in Paris. Merton’s influence on Glaser’s meth­
odological development is perhaps most apparent through Merton’s 
emphasis on conceptual integration (i.e., theoretical coding in classic 
GT) as essential to theory development (Merton, 1968, p. 143).
It was not Merton or Lazarsfeld, however, who supervised Glaser’s 
doctoral studies at Columbia but rather the quiet-spoken Hans Zetter­
berg (1954, 1961, 1962, 1965). Glaser was particularly taken with Zetter­
berg’s focus on the practical value of social theory and the importance of 
empirical research as the basis for theory development. Glaser’s concern 
for the practical value of sociology would surface in the later articulation 
of criteria for evaluating a good grounded theory—that is, a theory that 
fits, works, has relevance, and is readily modifiable on the basis of new 
data (Glaser, 1978). Zetterberg admonished the definitional and descrip­
tive nature of contemporary sociology and called for more effort at mov­
ing past simply setting forth findings and simple propositional statements 
in favor of integrating propositions into systems, or theories (Zetterberg, 
1954). He railed against conjectured theory and called instead for 
“research-grounded theories” (Zetterberg, 1954, p. 20); albeit, like 
Lazarsfeld, his primary concern was with theory verification.


AN OVERVIEW OF CLASSIC GROUNDED THEORY
4
While Glaser has frequently acknowledged the influence of Lazars­
feld, Merton, and Zetterberg on his conceptual ideation of GT, it was 
Glaser’s autonomous creativity (Holton, 2011) that enabled him to 
stand outside the shadow of these great men of science to develop his 
own ideas about science and social behavior, ideas that would progress 
and develop throughout his scholarly career and foster his determined 
advancement of GT. This autonomous creativity enabled him to tran­
scend the dominant positivist tradition, effectively rejecting that para­
digm’s premise of preconceived theoretical bases in favor of developing 
theory from empirical data. Glaser’s ability to conceptually transcend 
the dominant positivist view with respect to theory development 
would later enable him to approach Anselm Strauss’s qualitative 
realm, with its rich possibilities for data, open to discovery without 
imposing the structures of positivism’s logic of justification; this was 
an intellectual stance that not only facilitated the emergence of GT but 
also laid the foundation for GT as a general methodology for research 
open to any epistemological perspective and using any type of data 
(Glaser, 2003, 2005a; Holton, 2008) and as an integrative research “para­
digm for discovery” (Glaser, 2005a, p. 145).
Glaser and Strauss: From Collaboration to Difference
Having completed his PhD studies in 1961, Glaser moved back to the 
San Francisco Bay area, where he met and began to work with Anselm 
Strauss (1916–1996). Strauss had moved from Chicago to the University 
of California at San Francisco (UCSF) in 1960. His expertise was in quali­
tative methods with a symbolic interactionist perspective. The shift in the 
philosophy of science in the 1960s (Gibson & Hartman, 2014) had consid­
erable momentum at the Chicago school during Strauss’s time there, with 
efforts focused on displacing the privileged status of quantitative meth­
odologies (e.g., variable analysis). A leading proponent of the 

Chicago school, Herbert Blumer asserted that these methodologies pro­
duced “disconnected findings” absent of the context that characterizes a 
sociological variable as “an intricate and inner-moving complex” (1956, 
pp. 684, 688). As such, both Glaser and Strauss were sensitive to this 
emerging tension in methodological approaches to theory development.
Glaser would join Strauss at UCSF to commence work on what would 
become their famous study of dying in American hospitals (Glaser & 
Strauss, 1965a).4 The study relied largely on interviews and observational 
data captured through field notes. Their collaboration afforded distinct 
yet complementary roles for the new research partners. While Strauss led 
the fieldwork, Glaser’s focus was methodological as he applied his 


Chapter 1    Situating Grounded Theory on the Research Landscape
5
attention to conceptual coding of the data. What emerged fairly quickly in 
the process was his recognition that the study was that of another index 
(i.e., a core category)—an awareness index—methodologically similar to 
Lazarsfeld’s apprehension index (Lazarsfeld & Thielens, 1958) and his 
own recognition index (Glaser, 1961, 1964a, 1964b).
By affording Glaser a first opportunity to work extensively with 
qualitative data, the Awareness study offered an opportunity to further 
refine methodological ideas that he had begun to develop through his 
doctoral work. An early paper (Glaser, 1965) sets down his procedures 
for taking explicit coding strategies from quantitative methodology 
and combining these with methods for generative theory development 
in contrast to more conventional approaches to qualitative analysis 
through analytic induction. Thus, Awareness became another arena for 
Glaser’s elaboration and refinement of earlier methodological ideas as 
seeded during his days at Columbia—reading carefully the data, con­
ceptually labeling ideas, comparing them for interchangeable indica­
tors, theoretically saturating and integrating them to explain a pattern 
of social behavior. “The process was the same: the data was a little 
softer, but no less rigorously dealt with” (Glaser, 1998, p. 27).
Awareness generated significant interest at the time, not simply for 
its theoretical contribution to understanding the process of dying in 
hospitals but also for its methodological innovation. This interest 
prompted Glaser to suggest publishing their methodology (Glaser, 
1998, pp. 21–22). Strauss had not been trained in methodology but 
encouraged Glaser to proceed. Glaser claims that he authored the first 
draft of Discovery while Strauss was on sabbatical in Europe, later shar­
ing it with Strauss, who then added comments and wrote an additional 
three chapters (pp. 21–22). Suddaby (2006) suggests that “the genius of 
Glaser and Strauss’s original methodology is that it outlines a proce­
dure by which formerly tacit approaches are made explicit” (p. 640). 
However, this first seminal book was more an introduction than a 
­
procedural guide. Hence, Glaser went on to write a second book, 
­
Theoretical Sensitivity (1978), detailing more specifically what has 
become the classic methodology for doing GT.
As is well documented, Glaser and Strauss were to later disagree 
about the precise nature of GT and would discontinue their profes­
sional collaboration. Consistent with their respective backgrounds, 
Strauss would continue his research and writing from within the 
qualitative realm while Glaser denied holding any particular perspec­
tive, instead espousing his relentless dictum: “All is data.” Glaser, 
however, is generally recognized as having retained both the spirit 
and the ­
substance of their original work (Locke, 2001, p. 64).


AN OVERVIEW OF CLASSIC GROUNDED THEORY
6
Glaser and Strauss (1967) start from a common position of GT as a 
general method of comparative analysis for the generation of theory 
from empirical data (p. 1) in opposition to other approaches where data 
were collected and analyzed to verify speculative theory. Indeed, 
­
Glaser (1991) reflected on their common quest and achievement in a 
tribute to Anselm Strauss in which he credits their respective 
contributions—Strauss’s ability for sociological conceptualization and 
his own for theoretical integration and formulation via the central role 
of core categories—as fundamental to the emergence of GT.
Juliet Corbin, the other individual widely recognized for her col­
laboration with Anselm Strauss, acknowledges the contribution that 
Barney Glaser made to Strauss’s methodological advancement in her 
tribute to Strauss in the same 1991 festschrift. She comments that, early 
in his career, Strauss struggled to analyze collected data and that his 
frustration was in how to penetrate the surface of data, what proce­
dures to use to identify and label concepts, and how to relate concepts 
in theoretical formulations. She suggests that it is doubtful that Strauss 
would ever have been able to clarify procedures if he had not worked 
with Barney Glaser.
It is generally accepted that Discovery is more an outline of the 
approach than it is a procedural guide. We can, however, look to sub­
sequent texts to explore more specifically each author’s approach to 
teaching GT, offering insight into their eventual methodological diver­
gence. Glaser’s 1978 publication of Theoretical Sensitivity was based in 
large measure on his experiences of teaching GT to graduate students 
at UCSF. Nine years later, Strauss published Qualitative Analysis for 
Social Scientists (1987), which draws heavily on his experiences of 
teaching qualitative methodology to graduate students, including his 
approach to GT. In these two texts, we glimpse a marked contrast in 
how each author “teaches” GT that may offer us a view to their respec­
tive perspectives.
Glaser’s (1978) text extends the discussion commenced in Discovery 
by setting forth a systematic approach to analysis and also by explicating 
the general and specific nature of GT, integrating and elaborating on 
methodological advances discovered and developed through his years 
of teaching. Beginning with a chapter on the importance of theoretical 
pacing, Glaser leads the reader through the full package of GT 
techniques—sampling, coding, memoing, integrating, and writing up. 
Indeed, his explication of GT methods is so well articulated that 9 years 
later, Strauss quotes extensively from the 1978 text in the introduction 
l
l
ARE GLASERIAN AND STRAUSSIAN GT RECONCILABLE?


Chapter 1    Situating Grounded Theory on the Research Landscape
7
to one of his own texts (Strauss, 1987). As Strauss comments in his 
Preface, “indeed the second half of that chapter is essentially his 
[Glaser’s] except for some amplification” (1987, p. xiv). However, we 
believe that what Strauss proceeds to offer in his text illustrates a 
fundamental difference in how each author perceives GT and what each 
considers essential to doing GT.
Glaser (1978) explains the various GT procedures largely absent 

of specific detailed examples. He writes conceptually, giving the reader 
a sense of the methods and how they might be employed without pre­
scribing precisely how they need to be employed. He does so without 
detailed procedural specification, leaving the researcher more scope for 
creativity and flexibility. Partington (2000) suggests that “Glaser was 
less keen to see grounded theorists following an orthodoxy of 
approach, preferring to direct his attention to ways of enhancing 
researchers’ latent creativity” (p. 94). Options are offered, as in theoreti­
cal coding families, but none is prescribed as required. Apart from sug­
gesting questions for substantive coding of data, the actual mechanics 
of ­
coding, for example, are left largely to the researcher’s discretion. 
Examples are footnoted for the reader’s convenience, but they do not 
interfere with general explanations offered in the text.
By contrast, Strauss (1987) emphasized a need for structure to aid 
analysis—in particular, his coding paradigm and axial coding with 
emphasis on conditions, interactions, strategies, tactics, and ­
consequences 
(pp. 27–28, 32–33). He described GT as
a style of doing qualitative analysis that includes a number of 
distinct features, such as theoretical sampling, and certain 
methodological guidelines, such as the making of constant 
comparisons and the use of a coding paradigm, to ensure con­
ceptual development and density. (p. 5)
His emphasis in theory generation is on change as a constant in 
social life, social interaction and social processes at the center of atten­
tion (p. 6). The approach is much more descriptive. While Strauss 
(1987) embraced the general ideas and the power in doing GT, unlike 
Glaser he was clearly less comfortable trusting in the creative ideation 
that emerges from trusting in the systematic and sustained analysis of 
data. His insistence on structure in advance and throughout the pro­
cess preempts encouraging the researcher to remain open and to trust 
in emergence. Corbin (1991) affirms Strauss’s more structured approach 
with its emphasis on action, interaction, conditions, constraints, and 
consequences.


AN OVERVIEW OF CLASSIC GROUNDED THEORY
8
As a guide to doing qualitative research, Strauss (1987) offers some 
excellent advice and many rich descriptive examples. However, he 
tends to overthink the process, potentially undermining researcher 
autonomy by restraining the creativity and flexibility inherent in GT’s 
theory-generating power. Perhaps Strauss’s many years as a supervisor 
of students had produced in him a need for cautionary control of the 
novice researcher. Apart from the Introduction chapter, which, as noted 
earlier, draws largely on Glaser’s (1978) text, subsequent chapters con­
tain extensive detailed descriptive excerpts from Strauss’s own research 
and that of various students. It would seem that each man valued 
something different as crucial to developing the ability to do GT. For 
Glaser, it was presenting the methodological ideas and trusting in the 
researcher’s ability to understand and work with those ideas, develop­
ing skill over time. For Strauss, what was most important was ensuring 
sufficient descriptive examples, perhaps feeling that with good exam­
ples “how qualitative analysis is actually done is made vivid” and that 
“one can literally see it being taught” (Strauss, 1987, p. xii).
The Straussian approach suggests that the experience, knowledge, 
and interest of the researcher should come into play in the initial focus­
ing of the study (see Strauss, 1987, pp. 10–11). While acknowledging 
that the researcher will be motivated to explore a general area of inter­
est, the Glaserian approach challenges the researcher to set aside pre­
conceived professional ideas and remain open to what emerges as the 
main concern in the area under study. While some may simply reject 
Glaser’s call for openness as impossible, opting instead for the Strauss­
ian approach with its specified analytic tools and coding frameworks, 
the latter can be a slippery slope, particularly for those trained in quali­
tative methods. As is illustrated in many published papers (that 
wrongly and with a very limitative perspective) identify GT as a quali­
tative method, authors mistakenly apply standard precepts of estab­
lished research procedures traditionally applied within academia (e.g., 
initial literature reviews, theoretical frameworks, specified research 
questions, interview protocols, taping and transcribing) as standard 
aspects of the GT package.
This emphasis on preconceiving the research process, a need to 
know in advance and to map out in detail the research journey, became 
so evident in the work of Strauss and Corbin (1990) that it would mark 
a clear methodological divergence from their earlier collaboration. 
­
Glaser’s (1992) response sets forth in great detail the inconsistencies 
with the basic tenets of GT in Strauss and Corbin’s (1990) text, declar­
ing their work to be a completely different methodology. Gibson and 
­
Hartman (2014) affirm Glaser’s concerns, suggesting that


Chapter 1    Situating Grounded Theory on the Research Landscape
9
in the need to make things simple, [Strauss and Corbin] pre­
sented the name of the things that made up grounded theory 
but changed the notion of what grounded theory was. They 
did so without carefully justifying their changes with reference 
to the original texts. (p. 97)
Referring to Strauss and Corbin (1998), Hernandez (2010) asserts 
that
the method described in that book, published after Strauss’s 
death in 1996, deviated completely from original grounded 
­
theory methodology (Glaser & Strauss, 1967) because of its 
descriptive, deductive, and verificational focus—as opposed 
to a grounded theory’s explanatory, inductive, and discovery 
focus. (p. 152)
Hernandez continues: “Corbin (2007) has called their method 
Straussian Grounded Theory and has asserted that it is not a research 
methodology but rather a qualitative data analysis approach” (p. 152). 
Glaser would conceptualize this lexical takeover in his 2009 book, 
­
Jargonizing: Using the Grounded Theory Vocabulary, in which he suggests 
that, perhaps ironically, vocabulary is GT’s greatest gift to the research 
world.
Given the strong positions that appear to exist within the research 
community regarding this famous split, it is worth noting that Basics 
of Grounded Theory Analysis: Emergence vs. Forcing (1992)5 was written 
only after Glaser had made several attempts to convince Strauss to 
withdraw the earlier book that Strauss had coauthored with Juliet 
Corbin (Strauss & Corbin, 1990). Rather than withdraw, Strauss 
encouraged Glaser to publish his objections. While much has been 
made of the famous schism, with supporters on both sides defending 
clearly divergent positions, it is worth noting that Glaser’s criticism 
was confined to asserting an absence of good scholarship on the part 
of Strauss and Corbin in not differentiating their methods for qualita­
tive analysis from the original GT methodology and for not acknowl­
edging the roots of GT in quantitative methodology and qualitative 
mathematics.6
In the opening paragraphs of Strauss and Corbin’s (1998) second 
edition, they relate a story about two famous French artists, Cezanne 
and Monet, in which Cezanne is purported to have said of Monet, “He 
is only an eye—but what an eye!” They suggest that although Monet’s 
way was different from Cezanne’s way, it was indeed just as insightful 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
10
and valuable. This story perhaps epitomizes the “great divide” 
between the Glaserian (i.e., classic GT) and Straussian approaches. 
Here, Corbin (writing after Strauss’s death in 1996) subtly acknowl­
edges that what they (Strauss and Corbin) had produced was indeed a 
different methodology—just as insightful and valuable but different. 
Here we agree with Gibson and Hartman (2014) that Strauss and 
Corbin’s approach had fundamentally changed the notion of GT as it 
had been intended.
l
l
THE NATURE AND POWER OF CLASSIC GT
GT is the systematic generation of theory from data that has itself been 
systematically obtained (Glaser, 1978, p. 2). A grounded theory, the 
result of a GT study is not the reporting of facts but the generation of 
probability statements about the relationships between concepts, a set 
of conceptual hypotheses/propositions developed from empirical data 
(Glaser, 1998, pp. 3, 22). GT offers a transcending view of a main con­
cern in a substantive area and the social behavior that explains how 
that concern is processed, managed, or resolved.
To understand the nature of the classic GT methodology, one must 
understand the distinction between conceptualization and description. 
Classic GT is not about accurate, descriptive reporting of findings in a 
study, nor is it an act of interpreting meaning as ascribed by the partici­
pants in a study; rather, it is an act of conceptual abstraction. For a clas­
sic grounded theorist, what matters are the concepts, the relationships 
among those concepts, and their power to offer the reader a conceptual 
explanation of significant behavior within the social setting under 
study, free of the particularistic details of specific empirical incidents 
(indicators). “Without abstraction from time, place and people, there 
can be no multivariate, integrated theory based on conceptual, hypo­
thetical relationships—descriptions cannot be related to each other as 
hypotheses as there is no conceptual handle” (Glaser, 2002, p. 267).
While descriptive findings in qualitative research are most certainly 
valuable, they do not provide conceptual abstraction. A GT must explain, 
not merely describe, what is happening in a social setting. This ability to 
abstract the conceptual idea from empirical indicators (incidents in the 
data under analysis), without the burden of descriptive detail, is what 
distinguishes classic GT methodology from qualitative data analysis. This 
abstraction to a conceptual level theoretically explains rather than 
describes behavior that occurs conceptually and generally in many 
diverse groups with a common concern (Glaser, 2003).


Chapter 1    Situating Grounded Theory on the Research Landscape
11
With this goal of conceptual abstraction, classic GT methodology 
uses data of all types and media and accommodates a range of episte­
mological and ontological perspectives without having to espouse any 
one perspective; that is, the methodology is epistemologically and 
ontologically flexible. Whether data are viewed as interpreted or objec­
tive is immaterial in classic GT methodology, as it is not the descriptive 
detail or the way in which the data are “constructed” that concerns the 
grounded theorist; rather, it is the discovery of abstract concepts that 
lie within the data. Conceptual abstraction frees the researcher from the 
emphasis on detailed description and elucidation of multiple perspec­
tives. The skill of the grounded theorist is to abstract concepts by leav­
ing the detail of the data behind, lifting the concepts above the data and 
integrating them into a theory that explains the latent social pattern 
underlying the behavior in a substantive area (Locke, 2001). Research­
ers’ philosophical stance will certainly come into play in whatever 
substantive area of interest they may choose as their initial focus and 
what they consider to be appropriate data sources. This aside, how­
ever, the full package of GT can be applied to any data, and indeed, this 
full package needs to be applied to merit its distinction as a grounded 
theory.
	
EMERGENCE OF THE CLASSIC GT PARADIGM	
l
Despite the widespread embrace of GT by qualitative researchers, a 
growing number of theorists are adopting Glaser’s view that GT is a 
general research methodology occupying its own distinct paradigm on 
the research landscape (Holton, 2008). The use of any and all types of 
data in classic GT, together with its ontological and epistemological 
flexibility, clearly distinguishes GT from the precepts of other research 
paradigms. Many qualitative studies labeled as grounded theories sim­
ply fall short of the criteria of a classic grounded theory. Such studies 
have been “remodeled” to meet the criteria of a qualitative framework 
(Glaser, 2003; Glaser & Holton, 2004). In the process, researchers sacri­
fice the power of emergence that is fundamental to classic GT.
As a general research methodology, GT uses any and all types of 
data. For grounded theorists, using both qualitative and quantitative 
data opens a vast realm of additional empirical possibilities for gener­
ating theory. Embracing GT with mixed qualitative and quantitative 
data (Walsh, 2014a, 2014b, 2014c) will also encourage further scholar­
ship among established GT researchers eager to advance their analytic 
skills by learning to work with quantitative indices as sources of data.


AN OVERVIEW OF CLASSIC GROUNDED THEORY
12
Perhaps it is inevitable that GT’s tremendous power and its inher­
ent flexibility invite methodological evolution and plurality. That being 
said, when evolution and its consequent plurality create “drift” 
whereby the key tenets of GT are not sustained as they were intended, 
can we still accept claims to grounded theory? While much excellent 
research is done under the banner of some qualitative frameworks, only 
studies that have employed the full GT methodological package should 
be accepted as grounded theory. It is our position in this text that recog­
nizing this distinction is important to advancing methodological schol­
arship. This distinction, of course, raises a key question that we address 
in this text: What are the essential elements of methodology that must be evi­
dent for a study to claim that it is a grounded theory in the classic sense?
IN SUMMARY
GT is about the discovery of theory from data systematically collected 
and analyzed without structured forcing through predetermined theo­
retical frameworks. Contrary to popular belief, GT is not a qualitative 
research method. It is a general research methodology that uses any 
and all types of data, both quantitative and qualitative. As such, GT is 
not confined to any one philosophical perspective. It is ontologically 
and epistemologically flexible, accommodating any philosophical per­
spective as espoused by the researcher. Classic GT is a “full package” 
methodology that progresses a study from initial data collection and 
analysis through to the presentation of an integrated theoretical expla­
nation of a latent pattern of social behavior. Classic GT originated with 
the early work of Barney Glaser using quantitative data and was fur­
ther developed and articulated through Glaser’s famous collaboration 
with Anselm Strauss. More recently, a growing community of classic 
GT scholars has espoused GT as a distinct research paradigm with its 
own distinct perspective and practices.
TEST YOUR KNOWLEDGE
In the following questions, one or several answers may be correct.
1.	 GT is about
a.	 Theory verification
b.	 Theory building
c.	 Deductive reasoning


Chapter 1    Situating Grounded Theory on the Research Landscape
13
d.	 Inductive reasoning
e.	 None of the above
2.	 GT originated
a.	 From Glaser and Strauss’s 1967 book
b.	 From Glaser and Strauss’s collaboration
c.	 From Glaser’s doctoral work
d.	 From Strauss’s doctoral work
e.	 None of the above
3.	 GT can be applied
a.	 Only with qualitative data
b.	 Only with quantitative data
c.	 With any type of data
d.	 Only with data collected from interviews
e.	 None of the above
4.	 In classic GT it is important to
a.	 Describe in detail a substantive area
b.	 Report on facts in a detailed manner
c.	 Maintain an interpretive stance
d.	 Conceptualize from empirical indicators
e.	 None of the above
FURTHER READING
Glaser, B. G. (1998). The roots of grounded theory. In Doing grounded theory: 
Issues and discussions (pp. 21–33). Mill Valley, CA: Sociology Press.
Holton, J. A. (2011). The autonomous creativity of Barney G. Glaser: Early influ­
ences in the emergence of classic grounded theory methodology. In 
­
A. Gynnild & V. Martin (Eds.), Grounded theory: The philosophy, method and 
work of Barney Glaser (pp. 201–223). Boca Raton, FL: BrownWalker Press.
Walsh, I. (2014). Using quantitative data in mixed-design grounded theory 
studies: An enhanced path to formal grounded theory in information sys­
tems. European Journal of Information Systems. Advance online publication. 
doi:10/1057/ejis.2014.23
ENDNOTES
1.	 Hereafter referred to as Discovery.
2.	 Gibson and Hartman (2014) offer valuable insight into this context. See 
in particular pp. 5–13.


AN OVERVIEW OF CLASSIC GROUNDED THEORY
14
3.	 See also Glaser and Strauss (1967, Chap. 8).
4.	 Hereafter referred to as Awareness.
5.	 Hereafter referred to as Basics.
6.	 Despite Glaser and Strauss’s obvious methodological divergence, 
Glaser has continued to acknowledge and respect Anselm Strauss as a 
close personal friend.
7.	 The 
Creative 
Commons-Attribution-NonCommercial-ShareAlike 
License 4.0 International applies to all works published by the 
International Journal of Qualitative Methods. Copyright for articles 
published in the International Journal of Qualitative Methods remains 
with the first author.


15
2
Discovering New Theory as 
the End Purpose of Classic 
Grounded Theory1
After studying this chapter, you will:
• be aware of the different meanings attached to the word theory as they 
relate to different philosophical assumptions
• understand GT as a process aimed at theory discovery and congruent 
with any philosophical perspective
M
any social sciences are still lacking in the area of theorizing, 
and we need a plurality of theories in order to make sense of 
the complex and dynamic world that surrounds us (Junglas 

et al., 2011). “Developing theory is what we are meant to do as 
­
academic researchers and it sets us apart from practitioners and 
­
consultants” (Gregor, 2006, p. 613). Hence, the ultimate aim of GT 
being theory building, there is little doubt of its importance and fit in 
any academic career. However, it is first essential to stop and reflect on 
the meaning of the word theory.


AN OVERVIEW OF CLASSIC GROUNDED THEORY
16
As we saw in the introduction, we recently asked Barney Glaser, 
“What is a grounded theory?” His answer was “interrelated 
grounded concepts based on an emergent theoretical code” (per­
sonal communication, February 12, 2015). However, many different 
views of what a theory is have been given in the literature from etic 
(i.e., nomothetic) or emic (i.e., idiographic) perspectives; we need to 
examine these views in detail in order to truly understand the vari­
ous ways in which GT is being applied all over the world and in 
many different disciplines. These perspectives are often linked to 
the researcher’s background, training, and philosophical assump­
tions. For instance, Bacharach (1989) views a theory “as a system of 
constructs and variables in which the constructs are related to each 
other by propositions and the variables are related to each other by 
hypotheses”’ (p. 498), whereas Weick (1995) views theories as 
“approximations” of a complex reality that one has to make sense 
of. However, these definitions are not philosophically neutral. We 
accept the all-encompassing definition provided by Gregor (2006), 
which is as philosophically neutral as possible and which views 
“theories as abstract entities that aim to describe, explain, and 
enhance understanding of the world and, in some cases, to provide 
predictions of what will happen in the future and to give a basis for 
intervention and action” (Gregor, 2006, p. 616).
Gregor investigates the structure and form of theories. She argues 
that the four primary goals of theory are analysis and description, 
explanation, prediction, and prescription (Gregor, 2006). She shows 
that combinations of these goals lead to five types of theories that are 
interrelated, answer different types of questioning, and which should 
be combined: analytic (type I: What is?), explanatory (type II: What 
is? How? Why? When? Where?), predictive (type III: What is? What 
will be?), explanatory and predictive (type IV: What is? How? 
­
Why?  When? Where? What will be?), and theory for design and 
action (type V: How to do?). Usually, one must analyze before one can 
explain, predict, or act. And, preferably, one must attempt to explain 
and ­
predict, if at all possible, before one actually acts. This is 
­
summarized in Table 2.1.
When doing classic GT, type I is not sufficient, and at least type II 
must be achieved. As we emphasize in this book, classic GT may be 
applied with any data, qualitative and/or quantitative, and is compati­
ble with any philosophical assumptions. Different data may be collected 
l
l
WHAT IS A THEORY?


17
Chapter 2    Discovering New Theory as the End Purpose of Classic Grounded Theory
Table 2.1    Different Types of Theory (Adapted from Gregor, 2006)
Theory type
Answers the following 
questions . . .
Description
I
Analytical
What is it? 
What is happening?
The theory analyzes and 
describes phenomena 
but no causal 
relationship is 
highlighted.
II
Explanatory
What is it? What is 
happening? How? Why? 
When? Where?
The theory explains but 
does not predict. It does 
not highlight 
propositions.
III
Predictive
What is it? What is 
happening? What will be? 
What will happen?
The theory predicts and 
highlights testable 
propositions but does 
not develop justified 
causal explanations.
IV
Explanatory 
and 
predictive
What is it? What is 
happening? How? Why? 
When? Where? What will 
be? What will happen?
The theory predicts and 
also highlights testable 
causal explanations.
V
Prescriptive
What should be done? 
How should it be done?
The theory explicitly 
prescribes (methods, 
techniques, principles).
through various methods (e.g., interviews, focus groups, photograph­
ing, filming, surveys, etc.) and analyzed with the help of different tech­
niques (e.g., text analysis, cluster analysis, etc.) through the constant 
comparative method. The resulting theory may differ from one researcher 
to the next, depending on researchers’ philosophical assumptions, the 
data they choose as appropriate, and their previous training.
Over the past 20 years, an important debate has ensued about the 
rationale for combining methods and techniques previously consid­
ered as incompatible due to philosophical assumptions and world­
views presumed to be linked to these (Bryman, 1998; Hall, 2012). This 
dispute has led to philosophical caricatures (Bryman, 1998) at each end of 
a continuum (see Figure 2.1).


AN OVERVIEW OF CLASSIC GROUNDED THEORY
18
At one end, quantitative purists espouse a positivist philosophy 
and adopt the “natural science model” (Bryman, 1998) with indepen­
dent and dependent variables and quantitative data (Lee & Hubona, 
2009): The observer is separate from the object of study; science is 
objective and aims to uncover laws that are time- and context-free; and 
the focus is on deduction, confirmation, hypothesis testing, and quan­
titative analysis (Johnson & Onwuegbuzie, 2004). Causality is under­
stood from a Humean perspective (“A causes B”).
At the other end of the continuum, qualitative purists espouse an 
interpretive philosophy associated with ethnography, hermeneutics, and 
some forms of case research, interpreting settings and contexts to take 
the “natives’ point of view” (Lee & Hubona, 2009, p. 238): Realities are 
multiple and socially constructed so that knower and known cannot be 
separated; research is subjective and value-bound; causes and effects 
cannot be differentiated; and the focus is on induction, exploration, 
­
discovery, and qualitative analysis (Johnson & Onwuegbuzie, 2004).
However, many researchers have now reached an agreement on 
some major issues (Johnson & Onwuegbuzie, 2004) regarding theoriz­
ing, whether they use qualitative or/and quantitative data and 
­
techniques and even if their philosophical assumptions may vary 

1- Data and techniques:
 
Quantitative
2- Reality: Governed 
 
by laws that are
 
time- and context- free
3- Focus:
 
Deduction,
 
confirmation, 
 
hypothesis testing
4- Causality:
 
A causes B
Interpretivism
1- Data and techniques: 
 
Qualitative
2- Reality: Multiple
 
and Socially
 
constructed
3- Focus: 
 
Induction, exploration, 
 
and discovery
4- Causality: 
 
Causes and effects 
 
cannot be separated
Post Positivism
Critical Realism
Pragmatism
A
z
A
B
p
Y
w
c
A
a
A
n
o
w
B
w
U
2
1
9
7
5
4
8
3
5
6
4
3
8
8
9
2
6
1
3
4
3
2
5
6
7
3
1
A
U
c
z
n
A
A
M
M
T
Philosophical Continuum
Caricature
Positivism
Caricature
Figure 2.1    
Caricatures at Each End of the Philosophical Continuum 
(Adapted from Walsh, Holton, Bailyn, Fernandez, Levina, 
& Glaser, 2015a)


19
Different befitting theories
for one set of data
Probabilistic evidence
is not final proof
Relativity of reasoning
Observation as an
approximation of reality
Beliefs are embedded
in assumptions
Figure 2.2    Consensual Perspective
(see Figure 2.2): Reasoning is relative and varies among people; obser­
vation is an approximation of reality; a single set of empirical data can 
yield different befitting theories; hypotheses are linked to assumptions; 
probabilistic evidence is not final proof; and researchers’ beliefs are 
embedded in the assumptions of their respective communities.
One reason these caricatures developed is that philosophical assump­
tions have been mapped mainly as an opposition between nomothetic 
perspectives and more idiographic perspectives. This element, and the 
coupling of quantitative data and techniques with a nomothetic approach 
versus qualitative data and techniques with an idiographic approach, 
completely ignores another dimension that opposes confirmatory, theory-
driven research and exploratory data-driven research.
The Possibility of Using Any Type of Data With Any Philosophical 
Stance to Do GT
In Awareness (Glaser & Strauss, 1965a), and through the use of qualitative 
data, Glaser and Strauss highlight an awareness index that explains the social 
interactions surrounding a dying person. This index makes sense at the 
same time in Glaser’s post-positivist worldview and in Strauss’s symbolic 
interactionist perspective. Walsh (2010, 2014a) uses mixed qualitative and 
quantitative data/techniques in a critical realist stance to propose a digital 
culture index that explains information technology usage; that is, why people 
use (or do not use) information technologies, for what purpose, and how 
they do so through the emergence of their digital needs.
Chapter 2    Discovering New Theory as the End Purpose of Classic Grounded Theory


AN OVERVIEW OF CLASSIC GROUNDED THEORY
20
When theorizing, researchers produce theories at different levels of gen­
eralization; these are sometimes linked to the theory’s level of abstrac­
tion. For instance, a metatheory is at a high level of abstraction and 
provides “a way of thinking about other theories” (Gregor, 2006, p. 616); 
a grand theory is abstract, scarce in observational details, and 
“‘unbounded in space and/or time’ (p. 616), whereas some empirical 
generalizations are rich in observational details but often “bounded in 
space and time” (Bacharach, 1989, p. 500). There are, however, different 
conceptions of generalizability, which “refers to the validity of a theory 
in a setting different from the one where it was empirically tested and 
confirmed” (Lee & Baskerville, 2003, p. 221). Lee and Baskerville (2003) 
identify different types of generalizability and generalizing beyond sta­
tistical, sampling-based generalizability: generalizing from empirical 
statement to empirical statement (i.e., from data to description), from 
empirical statement to theoretical statement (i.e., from description to 
theory), from theoretical statement to empirical statement (i.e., from 
theory to description), and from theoretical statement to theoretical 
statement (i.e., from concepts to theory). This is summarized in Table 2.2.
In the case of a grounded theory, the generalization is done from 
data to theory through conceptualization, even though one often starts 
from data to description in order to see through and understand the 
data, that is, what they are telling us (i.e., indicating) in order to con­
ceptualize. It must, however, be highlighted that one cannot claim to 
have done classic GT if one remains at the descriptive level, short of 
producing a conceptual theory.
l
l
THE DRIVE FROM SUBSTANTIVE TO FORMAL GT
Generalization
Toward empirical elements
Toward theoretical 
elements
From empirical 
statements
Description of data
From description to theory
The researcher measures, 
observes, describes
The researcher 
conceptualizes from her/
his data
From theoretical 
statements
From theory to description
From concepts to theory
The researcher applies to a 
given context a theory 
developed and confirmed 
in another context
The researcher highlights 
propositions linking 
concepts
Table 2.2    
Different Types of Generalization (Adapted from Lee and 
Baskerville, 2003)


21
Grounded theories may be developed at both the substantive and 
formal levels. A substantive grounded theory reaches beyond observed 
incidents and analyzed data but applies to the substantive area of inquiry 
(Urquhart, Lehmann, & Myers, 2010). A formal grounded theory is 
abstract in terms of time, place, and people until it is applied (Glaser, 
2007); it has been defined by Glaser (2007) as “a theory of SGT [substan­
tive GT] core category’s general implications, using, as widely as possible 
other data and studies in the same substantive area and in other substan­
tive areas” (p. 99). A formal GT has no predetermined level of abstraction, 
as this will be determined by the theoretical sampling and the data used. 
It allows generalizing on a core category from different substantive areas 
“with more multivariate conceptual complexity” (Glaser, 2007, p. 1). 
Hence, and in order to reach formal GT, a substantive GT first has to be 
developed. Formal GT makes substantive findings “meaningful on a 
larger scale” (Kearney, 2007, p. 131) and extends the applicability of a 
substantive grounded theory across contexts, settings, or both.
The formalization of a substantive grounded theory involves start­
ing with an existing substantive theory; its aim, however, is to “enhance 
the theory, widen its scope or in other ways improve it—but not to 
verify or falsify it” (Urquhart et al., 2010, p. 4). The formalization of a 
substantive grounded theory will therefore always involve the full GT 
process. To sum up, theorizing is a continuum (Runkel & Runkel, 1984; 
Weick, 1995): A substantive grounded theory can be understood as what 
Weick (1995) terms an “interim struggle,” an early stage of formal theo­
retical development leading toward further development and stronger 
From Substantive to Formal Theory
In their famous collaboration, Glaser and Strauss first developed a general 
substantive theory related to dying patients (Awareness, 1965). They then 
extended their core category (i.e., patients’ changing status from living to 
dying) to build a formal theory of status passage (Glaser & Strauss, 1971) 
related to many different settings (e.g., career changes, mobility within 
organizations, etc.).
Walsh, Kefi, and Baskerville (2010) proposed a substantive theory related 
to the process of acculturation to information technologies and a digital 
culture index developed from qualitative data collected in some given con­
texts (corporate and societal) but bounded by qualitative data. This index 
was then amended and the theory formalized into a general substantive 
theory by extension to other substantive contexts through supplementary 
quantitative data slices (Walsh, 2014a; Walsh & Gettler-Summa, 2010).
Chapter 2    Discovering New Theory as the End Purpose of Classic Grounded Theory


AN OVERVIEW OF CLASSIC GROUNDED THEORY
22
formal theory at a “higher gradation of abstractness and generality” 

(p. 385). Glaser (2011) stresses that the procedures required to generate 
formal GT are the same as those to generate substantive GT. The 
­
multiple differences between the two are in the theoretical sampling:
In SGT [substantive GT], one samples within a substantive 
chosen site or population. In doing FGT [formal GT], one 
samples widely in other substantive sites and populations both 
within and outside the substantive area in order to make the 
theory more general, as one constantly compares, adding new 
properties and categories to the core category being general­
ized. (Glaser, 2011, p. 257)
Doing GT does not imply an idiographic (i.e., local) or nomothetic 
(i.e., universal) perspective. Both are possible, depending solely on the 
researcher’s philosophical stance. Idiographic, substantive theories 
lead to different types of nomothetic, formalized theories, using any 
kind of data (qualitative and/or quantitative) while always remaining 
in an exploratory, data-driven stance (Figure 2.3). As highlighted by 
Glaser (2007), a formal theory may be a so called middle-range theory 
or “grand theory” in Merton’s (1967) sense.
Glaser and Strauss (1967) differentiated between substantive and 
formal GT through their differing degrees of abstraction and generality; 
however, they cautioned that they lacked hindsight and experience in 
this area. Glaser (1978) suggested that between a theory (e.g., the 
change of status of patients in one hospital) in a specific area (e.g., 
dying patients) and a formal theory (e.g., status passage) in its full 
generality (e.g., career changes, mobility within organizations), “there 
is a type of theory in between which we call general substantive 
theory” (p. 52), for example, the awareness of dying related to patients 
in any hospital. To fully understand the process of formalization of a 
substantive theory, and in a critical realist stance, we would propose 
to replace the term generality as used by Glaser and Strauss (1967) by 
substantive formalization and “abstraction” by ­
conceptual formalization. 
The substantive formalization of a substantive theory may be obtained 
through sampling different substantive groups, contexts, and/or 
social units within the same setting/­
substantive area; it increases the 
scope of a grounded theory. The conceptual formalization, which can 
mostly be achieved once the substantive formalization has been 
ensured, may be obtained through sampling in different settings/
substantive areas; it increases the reach of a grounded theory. This is 
summarized in Figure 2.4. The two examples provided in the previous 
boxed text are illustrated in Figure 2.5.


23
Nomothetic
Perspective
Theory-driven
Data-driven
Idiographic
Perspective
Universal laws
Exploration
Confirmation
Settings and
contexts
GROUNDED
Formalizing substantive theories
THEORIES
Figure 2.3    
The Land of Theories (Adapted from Walsh et al., 2015a)
Substantive
Theory
–
Reach and scope
+
General Substantive Theory
(Middle Range)
Data
Conceptual formalization
Substantive formalization
Emergence of core
category and
related categories 
Slices of data in
a given group/social
unit/context
Formalizing a theory
Theoretical sampling to formalize a theory
Slices of data in different settings
Slices of data in different
substantive groups/social units/contexts
within the same setting
Formal Theory
(Grand) 
Substantive formalization
Conceptual formalization
Figure 2.4     
From Substantive to Formal Grounded Theory (Translated 
and adapted from Walsh, 2015)
Chapter 2    Discovering New Theory as the End Purpose of Classic Grounded Theory


24
Substantive Formalization
Conceptual Formalization
General substantive
theory about the
awareness of dying 
Data related to
dying patients 
Data related to 
different
settings/domains
Conceptually formalized 
theory about status
passage
Substantive theory about
IT culture creeping. 
Digital culture index 
(Walsh et al., 2010)
General substantive theory of IT
culture. Amendment of the digital
culture index (Walsh, 2010; 2014a)
Data (qualitative)
related to IT culture
Data (qualitative
and quantitative)
related to IT culture
Figure 2.5    
Examples of Substantive and Conceptual Formalization


25
Beyond the various classifications aimed at describing theories 
proposed by different authors, we propose a further consideration that 
is acutely needed to understand fully the contribution of classic GT to 
the scientific research process. When researchers theorize, they can do 
so through what we term incremental theorizing (theorizing while using 
existing concepts/constructs) or rupture theorizing (theorizing while 
using new concepts/constructs; Walsh, 2014c). Incremental theorizing, 
although essential to help a research field mature and grow, represents 
additional information and gradual developments on existing 
concepts/constructs. Rupture theorizing uses nascent concepts/
constructs, which were previously unrevealed and unstudied in the 
literature, or which were previously applied in, and adapted to, 
completely different domains.
Rupture theorizing may involve defining and specifying these new 
concepts/constructs and/or investigating relationships between these 
and other previously established and studied concepts/constructs. For 
instance, in a hypothetical-deductive stance, the literature is usually 
first investigated for clues to lay down hypotheses in a linear research 
approach (Creswell, 2014; Zachariadis, Scott, & Barrett, 2013). Hypo­
thetical-deductive research has led to data poverty (as data collected 
are used for theory testing and not for theory building; Evermann & 
Tate, 2011; Lyytinen, 2009) and, consequently, to incremental theoriz­
ing. In the case of rupture theorizing, if a concept/construct has never 
been previously studied in a field, the literature will be of little help to 
describe or explain it or to develop propositions or hypotheses involv­
ing this new construct. As all preconceptions are to be set aside when 
doing GT (Glaser, 1978), adopting a classic GT exploratory stance natu­
rally leads to the emergence of new concepts/constructs and to theo­
rizing in rupture with existing literature. Thus, as the main concern 
emerges while doing GT, it may lead the researcher to investigate 
either new phenomena, never before investigated in the literature, or, 
alternatively, the GT process will lead to studying phenomena already 
investigated in past literature but from different and alternative 
perspectives.
	
THEORIZING IN RUPTURE WITH EXISTING LITERATURE	
l
Chapter 2    Discovering New Theory as the End Purpose of Classic Grounded Theory


AN OVERVIEW OF CLASSIC GROUNDED THEORY
26
IN SUMMARY
Classic GT may accommodate any philosophical perspective. The ulti­
mate purpose of classic GT is to go beyond description and to discover 
new theories grounded in empirical data; either a new theoretical per­
spective on a well-established and theoretically developed phenome­
non or a theory in rupture with existing literature. A grounded theory 
may be substantive and framed in an idiographic perspective, or it may 
be formal (i.e., with greater reach and scope) and framed in a more 
nomothetic perspective. Depending on a researcher’s philosophical 
stance, the potential resulting grounded theories may vary widely.
TEST YOUR KNOWLEDGE
	
In the following questions one or several answers may be correct.
1.	 A theory
a.	 Is the same thing whatever your philosophical stance
b.	 May be only descriptive in classic GT terms
c.	 Always involves causal elements whatever your philosophi­
cal stance
d.	 None of the above
2.	 A substantive grounded theory
a.	 Reaches beyond analyzed data and observed incidents
b.	 Applies to the substantive area of inquiry
c.	 Generalizes from empirical data to theoretical statement
d.	 None of the above
3.	 A formal grounded theory
a.	 Has no predetermined level of abstraction
b.	 Allows generalizing on a core category from different sub­
stantive areas
c.	 Develops further a substantive GT
d.	 Is at a greater level of abstraction and/or generality than a 
substantive GT
e.	 None of the above


27
FURTHER READING
Gibson, B., & Hartman, J. (2014). Rediscovering grounded theory (Chapter 2). 
London: Sage.
Glaser, B. G. (1978). Theoretical sensitivity (Chapter 1). Mill Valley, CA: Sociology 
Press.
Glaser, B. G. (2007). Doing formal grounded theory. Mill Valley, CA: Sociology 
Press.
Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies 
for qualitative research (Chapters 1 & 2). New York: Aldine de Gruyter.
Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 
30(3), 611–642.
ENDNOTE
1.	 Some materials in this chapter were previously published in Walsh 
(2014c).
Chapter 2    Discovering New Theory as the End Purpose of Classic Grounded Theory


28
3
Foundational Pillars of 
Classic Grounded Theory
After studying this chapter, you will:
• understand the foundational pillars of grounded theory
• appreciate the necessary frame of mind for doing grounded theory
I
t is common to read in published accounts of grounded theory that 
doing GT is not easy, that it is a “difficult subject” (Suddaby, 2006, 
p. 633), “worryingly messy” (Partington, 2002, p. 155), “problematic” 
(Backman & Kyngas, 1999, p. 148), and a source of “methodological con­
fusion” (Goulding, 1999, p. 866). Reading such accounts would be 
enough to discourage any novice from adopting GT as a methodological 
choice! If one looks beyond such assessments, it is generally the case that 
the authors have conflated GT with some of the precepts of traditional 
qualitative research. Such precepts are often misaligned with GT’s pre­
cepts, and therein frequently rest the struggles. Partington (2002) suggests 
this conflation has resulted in a loss of attention to key principles of GT.
Suddaby’s (2006) editorial in the Academy of Management Journal is 
a leading example of this qualitative assumption applied to GT. He 
confuses GT’s emergent paradigm with a lack of systematic procedure 
when he suggests that “totally unstructured research produces totally 
unstructured manuscripts that are unlikely to make it past the desk 


Chapter 3    Foundational Pillars of Classic Grounded Theory
29
editor at any credible journal of social science” (p. 633). Suddaby also 
confuses the way GT research is actually conducted and the way it is 
written to fit within the constraints of established academic publishing 
standards (de Vaujany, Walsh, & Mitev, 2011). Similar to Partington 
(2002), Suddaby advocates for a clear research question and theoretical 
framework up front. Furthermore, he misinterprets Glaser and Strauss 
(1967, p. 79) as calling for grounded theory to be generated from extant 
theory. Here, he confuses their reference to generating formal theory as 
applying more broadly to substantive theory generation. What Glaser 
and Strauss are actually proposing is that a formal grounded theory is 
best generated when it follows from a substantive grounded theory 
using its core category as the basis for more formal theory develop­
ment. They are not suggesting that to develop a grounded theory, one 
must start from existing theory.
What Glaser and Strauss (1967) actually said about the use of extant 
theory:
A substantive theory generated from the data must first be formulated 
in order to see which of diverse formal theories are, perhaps, applicable 
for ­
furthering substantive formulations . . . allowing substantive concepts 
and hypotheses to emerge first, on their own, enables the analyst to 
ascertain which, if any, existing formal theory may help him to generate 
his ­
substantive theories. (p. 34)
We agree with Suddaby’s advice that GT is not an excuse to ignore 
the literature. GT is not about ignoring the literature but rather about 
staying open by holding the literature in abeyance until the core cate­
gory of the theory has emerged. Doing so avoids the potential of forc­
ing of the study’s theoretical direction; instead, it enhances the potential 
to discover a true main concern in the area under study and a novel 
way to explain it through a core category. As a prominent scholar, 
Suddaby is influential, but his conception of GT as a qualitative 
method only adds to the confusion for those wishing to pursue the 
classic GT approach.
The purpose of this chapter is to help clarify some of this confusion 
and hopefully relieve the angst of “doing it wrong” by being very clear 
about the foundational pillars of classic GT and how these shape the 
necessary mindset for doing GT. We offer three components of GT as 
foundational and essential to its application: emergence, constant 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
30
comparative analysis, and theoretical sampling. By emergence, we refer to 
the result of GT’s open and exploratory stance in relation to the research 
field. Constant comparative analysis refers to the process through which 
all data are analyzed as they are collected and together with all previously 
collected data. Theoretical sampling is the process through which empirical 
data are selected and collected as guided by the emerging theory.
l
l
EMERGENCE
The GT approach is a general methodology for the discovery of theory. 
The researcher enters the field and explores a substantive area by 
allowing the chief concerns of those actively engaged therein to guide 
the emergence of a core issue or problem. The conceptualization of that 
issue (or problem) becomes the basis for the articulation of a grounded 
theory that explains how the issue is processed, managed, or resolved. 
As such, a defining characteristic of GT is its rejection of preconceived 
theorizing. Contrary to the traditional quantitative approach, which 
implies the articulation of hypotheses in advance, or the traditional 
qualitative approach, which implies a theoretical framework to be 
applied, GT does not require as a starting point the identification of a 
gap in the literature from which precise research questions are framed 
to guide the study. A GT study begins with the researcher being open 
to discovering latent patterns that can be theorized to explain social 
behavior.
Those who have been trained in other research methodologies 
often find it a challenge to set aside learned habits, but doing so is 
fundamental to undertaking a grounded theory study where 
the focus of the study and its theorization cannot be decided at 
the outset of the study by preconceived notions resident within the 
researcher’s worldview, an initial professional problem, or an extant 
theory and framework. Such preconception is simply forced 
conjecture—what Glaser and Strauss (1965b) refer to as “logical 
speculation” (p. 11). Preconceiving a study based on extant theory 
blocks the emergent potential with “categories that do not seem to 
emerge in the data but, rather, are imposed on it as existing theory 
which ought to be applicable. Their relevance is implicitly assumed” 
(Glaser, 1966, p. 332).
Our philosophical stance does preconceive our research approach 
to some degree as it can limit what we consider to be appropriate data 
and how we approach the analysis of those data. As an example, 
constructivism privileges interview and focus group data and would 


Chapter 3    Foundational Pillars of Classic Grounded Theory
31
commonly reject the use of observational or documentary data as not 
appropriate for conveying the perspectives of the participants. GT’s 
openness to all data creates a form of triangulation in data sources, 
thereby reducing the potential for a perspective bias (whether that of the 
researcher or the participants) being introduced through restricted use 
of data types.
In a GT study, if a researcher chooses to use quantitative data and/
or techniques traditionally considered by some as confirmatory, it is 
not the technique in itself that is important but how it is used and 
applied with an exploratory purpose. As early as 1980, Tukey 
highlighted that “exploratory (quantitative) data analysis is an attitude 
and a flexibility” (p. 24). For instance, factor analysis that helps to 
group emerging variables, and/or cluster analysis that helps to classify 
objects/people/concepts based on their similarities and differences, 
could fruitfully be used in an exploratory fashion. Structural equation 
modeling (SEM) in the soft exploratory version of the partial least 
squares (PLS) approach can also help theorizing. Researchers may use 
many other techniques, if they always take care not to impose 
hypotheses on the data. When one uses quantitative data, it may be 
useful to remember that the numbers themselves are not what matters 
in social sciences: It is the meaning behind the numbers that is 
important. Hence, quantitative techniques may be used; they should, 
however, never be used in a mechanical fashion without fully 
understanding the meaning behind the techniques (Glaser, 2008). It 
might be useful for the reader to refer to Appendix C as an illustration 
of some quantitative techniques used in an exploratory GT stance.
While doing GT, concepts come from the data; they are not 
preconceived or imposed on data. This is particularly important when 
data, methods, and techniques are mixed. The quantitative and 
qualitative components are not used for verification or testing but for 
further elaboration of the theory. There should be no forcing, where it is 
assumed that particular constructs will occur in the data. Thus, and 
whatever the data that are being used, exploration and emergence of 
the theory from the data have to remain central.
Our preconceptions about the social worlds we wish to study can 
creep in unawares as this statement in one methodology guide 
illustrates: “In management research, you will often find that the more 
senior the manager the more useful the interview data” (Partington, 
2002, p. 144). Concepts in vogue (e.g., context) are often assumed and 
forced upon a study, but in GT they are simply potential variables that 
may or may not emerge as relevant through constant comparative 
analysis. How such variables may influence a latent pattern within 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
32
your data must emerge as relevant to the substantive area that is 
investigated and not be assumed and forced into the analysis (Glaser, 
1992, 2013). Studies in disciplines that are highly theorized (e.g., 
management, economics, psychology) are particularly susceptible to 
preconception; this is what Alvesson and Sandberg (2014) refer to as 
“‘boxed-in research’. . . encouraging specialization, incremental adding-
to-the-literature contributions and a blinkered mindset” (p. 967). 
Undertaking a grounded theory study in such disciplines necessitates 
that researchers appreciate from the outset the undermining effect of 
preconceiving the research focus through the lens of their professional 
research interests, extensive preliminary literature reviews, precise 
research questions, defined sample populations, structured interview 
protocols, and other factors. Emergence as a foundational pillar 
necessitates that researchers remain open to discovering a main 
concern motivating the interest or action within the area under study 
and offers a conceptual theory to explain the behavior.
Glaser (2013) has identified several types of preconception that 
frequently derail GT intentions. Among those commonly adopted are 
the following:
• Conducting an extensive preliminary literature review to 
­
identify a gap in the research around which to focus a proposed 
study
• Assuming the relevance of a “pet theoretical code” (Glaser, 
2005a) such as social network, basic social process, conditional 
matrix, and so on, as a lens through which the data are 
analyzed
• Specifying research questions around which all data collection is 
focused
• Assuming the relevance of face sheet data (e.g., gender, age, 
income, etc.) to the analysis and theory development
Misconceptions such as all grounded theories are basic social 
processes (Langley, 1999) or actions (Creswell, 2013) lead to forcing a 
theory to fit the misconception. While Glaser (1978) highlighted basic 
social processes as an important theoretical code, he did so to empha­
size the patterned nature of grounded theory in contrast to unit 
analysis, which invariably leads to descriptive findings rather than 
conceptual theory. His inclusion in the same text of several alternative 
theoretical coding families should clarify the misconception of 
grounded theory as always being a basic social process. Forcing the use 


Chapter 3    Foundational Pillars of Classic Grounded Theory
33
of a specific theoretical code, such as using a basic social process to 
organize and model a theory, often results in rather flat and thin theory 
and prevents the theorist from seeing a more interesting, novel, 
emergent theoretical code around which to integrate the theory. 
Similarly, preconceiving the study from the outset with a “skeletal 
framework . . . focusing the inquiry at an early stage . . . provid[ing] 
internal structure to the study” (Morse & Mitcham, 2008, p. 32) is 
another preempting of GT’s emergent potential. Such strategies can be 
particularly problematic for an inexperienced theorist who lacks the 
confidence to stay open and reverts to the assurance of a structure in 
advance, especially under supervisor pressures to adopt the precepts 
of qualitative research.
Similarly, extensive literature reviews in advance of a study, often 
with the goal of identifying a gap in the literature, also present the 
potential for preconception—what Morse and Mitcham (2008) refer to 
as “the pink elephant syndrome.” At best, identifying such a gap is a 
stab in the dark because the literature reviewed may not correlate to 
the emergent main concern and may even inhibit the researcher’s 
ability to see the main concern. Such practices, however, are often 
encouraged by PhD supervisors unfamiliar with classic GT and can 
even influence standard requirements of the PhD thesis at many 
universities and of peer reviewer expectations for mainstream journal 
publication. As such, the undermining potential for GT studies is 
persistent and significant but can be overcome with a solid 
understanding and appreciation for emergence as foundational to GT 
development.
If, in accordance with classic GT precepts, the literature review is 
conducted after the core category has emerged and around this core 
category, the literature may be used as data. It may help in theoretical 
sampling for full saturation of emergent concepts as well as in 
theoretical coding for the emergence of relationships between concepts 
and overall integration of the theory.
	
CONSTANT COMPARATIVE ANALYSIS	
l
The goal of GT is the generation of theory that conceptually explains 
latent patterns of social behavior:
Conceptualization is the medium of GT for a simple reason: 
without the abstraction from time, place and people, there 
can be no multivariate, integrated theory based on conceptual, 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
34
hypothetical relationships  .  .  .  Because GT operates on a 
conceptual level, relating concept to concept, it can tap the 
latent structure which is always there and drives and organizes 
behavior and its social psychological aspects, all of which are 
abstract of objective fact. (Glaser, 2002, p. 241)
Thus, the conceptual abstraction of empirical data using constant 
comparative analysis is fundamental to doing GT. Constant comparative 
analysis is a strategy for directing the collection and analysis of data in 
tandem with theoretical sampling as a means of guiding the direction 
of further data sampling (Figure 3.1).
The analytical processes that underlie the constant comparative 
method have their roots in latent structure analysis as developed at the 
University of Columbia’s School of Applied Arts under the leadership 
of Paul Lazarsfeld (Lazarsfeld & Henry, 1968). As a student at 
Columbia, Glaser saw the practical value in theory generation using 
empirical data that had been collected as preparatory to large-scale 
survey work. His own doctoral work employed secondary analysis of 
such data. As a student of Lazarsfeld, Glaser was exposed to Lazarsfeld’s 
index formation process but saw the stripped-down, summing-up 
quality as a loss (Glaser, 1998, p. 23). One had an index but without 
meaning. Glaser’s idea was rather than simply summing up indicators, 
he would instead compare indicator to indicator thereby generating 
conceptual properties and dimensions; this would be an index with 
Theoretical
Sampling:
Data Slice
Theoretical
Sampling:
Data Slice
Theoretical
Sampling:
Data Slice
Discovery of
Patterns:
Emerging
Theory 
Figure 3.1    
The Constant Comparative Analysis Process (Walsh, 
2014c)2


Chapter 3    Foundational Pillars of Classic Grounded Theory
35
meaning (Glaser, 1998, p. 24). Another aspect of constant comparative 
method is explication de texte,3 which Glaser studied at the Sorbonne. 
Explication de texte refers to “reading closely line by line to ascertain 
what exactly the author is saying without imputing what is said, 
interpreting it or reifying its meaning” (Glaser, 1998, p. 24). Glaser 
explains how explication de texte stirred his interest in latent patterns of 
behavior in social settings. Applying explication de texte to constant 
comparative analysis helps to generate concepts that closely fit what is 
going on in the substantive area without imputing meaning to the 
individual action; one simply names the concept as an abstraction.
The concept-indicator model requires that concepts and their 
properties and dimensions earn their way into a theory by systematic 
generation of data. GT’s concept-indicator model begins with substan­
tive coding in which empirical incidents are compared, incident 
­
to  incident, to generate concepts. When many indicators become 
interchangeable, they lead to a pattern that is named (concept). 
Additional empirical incidents are compared to the emergent concepts, 
to build and substantiate these with property and dimensional definition. 
When further data yield no new concepts or additional elaboration of 
their properties and dimensions, theoretical saturation has been 
achieved. Then the concepts are related to each other to generate 
hypotheses. Constant comparison continues through theoretical coding 
whereby concepts are compared to other concepts to establish conceptual 
levels and their theoretical integration. Thus, the constant comparative 
analysis of data threads throughout the entire GT process, taking full 
advantage of the interchangeability of indicators to develop a broad 
range of acceptable indicators of concepts and their properties (Glaser & 
Strauss, 1971, p. 184), from the initial data collected through to the full 
integration of the generated theory that is “integrated, consistent, 
plausible and close to the data” (Glaser, 1965, pp. 437–438).
Glaser’s (1965) seminal paper on the constant comparative method 
outlines four stages to the process—comparing incidents applicable to 
each category, integrating categories and their properties, delimiting 
the theory, and writing the theory—while reminding the reader that 
the process is not lockstep and linear but rather that the stages, once 
commenced, continue in tandem as the theorist cycles back and forth 
through the various stages. In addition, he specifies two defining rules 
of constant comparison: (1) “while coding an incident for a category, 
compare it with the previous incidents in the same and different 
groups coded in the same category” (p. 439), and (2) “stop coding and 
record a memo on your ideas” (p. 440). This latter rule establishes the 
central role of memoing in constant comparative analysis.


AN OVERVIEW OF CLASSIC GROUNDED THEORY
36
Memos are theoretical notes about the data and the conceptual 
connections between categories. By memoing throughout the constant 
comparative process, the researcher captures her emergent ideation of 
substantive and theoretical categories:
Bits of data and early codes are systematically examined, 
explored and elaborated on. Through memo writing, the 
theorist takes his emerging ideas apart, checks them and 
outlines further data collection  .  .  .  looks at the data from a 
variety of perspectives and analyzes them  .  .  .  The theorist 
gains further insights and creates more ideas about the data 
while writing. Hence, writing and rewriting are crucial phases 
of the analytic process. (Charmaz, 1990, p. 1169)
Later in the process, having achieved theoretical saturation of the 
core, its properties, and its related concepts, the researcher proceeds to 
review, sort and integrate the numerous memos captured along the 
way. The sorted memos generate a theoretical outline (a conceptual 
framework) for the full articulation of her grounded theory through an 
integrated set of hypotheses, thus achieving the final stage of the 
constant comparative process.
Constant comparative analysis shifts the theorist’s attention from 
verification of facts to ideas generation, thereby enabling maximum 
creativity in following emerging theoretical leads, unconstrained by 
interview protocols or a preconceived theoretical framework. “Since no 
proof is involved, the constant comparative method requires only 
saturation of data” (Glaser & Strauss, 1967, p. 104). Comparing 
additional incidents in the data to emerging concepts has four 
purposes:
(1) It verifies the concept as a category denoting a pattern in 
the data. (2) It verifies the fit of the category nomenclature to 
the pattern. (3) It generates properties of the category. (4) It 
saturates 
the 
category 
and 
its 
properties 
by 
the 
interchangeability 
of indicators. (Glaser, 1998, p. 139)
As such, GT’s verification is conceptual and propositional, not 
factual. Categories suggested are always tentative, and although they 
cannot be disproved, they can be modified by further data collection. 
Data collection continues until concepts are theoretically saturated, 
meaning that nothing new is being added to the elaboration of the 
concept or property. If conceptual ideas suggested in initial data do not 


Chapter 3    Foundational Pillars of Classic Grounded Theory
37
pattern out with further data collection, then the theorist must decide 
whether the initial ideas have earned relevance in the emerging theory 
and decide what prominence, if any, they hold as the theory continues 
to develop. In so doing, the constant comparison method ensures the 
earned relevance of emergent conceptual ideas, helping to overcome 
what Morse and Mitcham (2008) refer to as “conceptual tunnel vision” 
in assigning more data to a category than actually belong.
Memoing in tandem with coding using Glaser’s (1978, p. 57) 
questions4 will control this tendency as the theorist must explain how 
the data indicate a concept or a property of a concept and how they 
relate to other concepts, and so on. Earned relevance and theoretical 
saturation of emergent concepts also help to control for “pink elephant 
paradox” (Morse & Mitcham, 2008) of thin data sets in which 
insufficient incidents fail to offer up enough conceptual indicators. The 
constant comparative method requires the theorist to keep moving 
through the data to see conceptual indicators over and over, constantly 
comparing and memoing her ideas and deciding what further data are 
needed to elaborate and saturate emerging conceptual patterns.
Different slices of data, including quantitative and qualitative 
slices, feed information into each other. If one uses mixed data, one 
should not analyze or interpret them in isolation but rather as data 

are collected and together with all previously collected data. As an 
illustration the reader may refer to Sections 1.2 and 1.3 in Appendix C 
and to Appendix D to see how all data (primary and secondary, 
qualitative and quantitative) were constantly compared in order to 
theorize. As one theoretically samples quantitative data to supplement 
qualitative data (or vice versa), one should not wait until all quantitative 
data are collected but keep analyzing the data set as new data are 
collected in order to systematically check for possible emerging new 
patterns and/or categories.
	
THEORETICAL SAMPLING	
l
As noted earlier, the explicit coding and analytic procedures of constant 
comparative analysis work in tandem with the third foundational 
pillar of GT—theoretical sampling. In GT, theoretical sampling refers to 
the collection of data as guided by the emerging theory as opposed to 
some predefined sample or unit. In theoretical sampling, the theorist 
“jointly collects, codes and analyzes data and decides what data to 
collect next and where to find them in order to develop his theory as it 
emerges” (Glaser & Strauss, 1967, p. 45). Glaser (1978) refers to the 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
38
process as deductive (conceptual) elaboration as opposed to logical 
elaboration found in hypothesis testing research. He defines conceptual 
elaboration as
the systematic deduction from the emerging theory of the 
theoretical possibilities and probabilities for elaborating the theory 
as to explanations and interpretations. These become hypotheses 
which guide the researcher back to locations and comparative 
groups in the field to discover more ideas and connections from 
data. The data constantly check deductions that lead nowhere, as 
the analyst takes his directions from the emerging relevancies. 
Deduced hypotheses are not forced on the data when they fit 
poorly, they are discarded and others emerge in their place by 
constant comparative analysis. (p. 40)
Glaser and Strauss (1971) on selecting comparison groups:
The basic criterion governing the selection of comparison groups for 
generating theory is their theoretical relevance for furthering the devel­
opment of emerging categories, properties, hypotheses, and integration 
of the theory. Any groups may be selected that will help generate these 
elements of the theory. (p. 183)
Theoretical sampling sharpens the definition of concepts, helps to 
define their properties, and tests tentative theoretical propositions 
(Draucker, Martsolf, Ross, & Rusk, 2007). Sampling is driven by concep­
tual emergence and relevance and limited by theoretical saturation, not 
by a preconceived theoretical framework or by statistical significance. 
Consequently, selection of data sources is neither random nor totally 
predetermined. Additional data are selected in service to the emerging 
theory as the theorist gathers additional data on emerging patterns by 
asking specific questions in subsequent data collection or by returning 
to data already collected and reviewing it, selectively sampling there 

for the emerging pattern. As Glaser (1978, pp. 37–38) explains, the 
deductive nature of theoretical sampling complements GT’s overall 
inductive approach.
Theoretical sampling as a means of guiding the direction of the 
emerging theory has little efficacy if it is separated from the processes 
of coding and analysis. Memoing is part of the decision process around 
theoretical sampling, as it assists theorists to identify gaps in their 


Chapter 3    Foundational Pillars of Classic Grounded Theory
39
analysis and points to possible new directions for further data 
collection, coding, and analysis. If the data are collected by theoretical 
sampling at the same time that they are analyzed, the integration of the 
theory is more likely to emerge by itself as this taps to the fullest extent 
the in vivo patterns of integration in the data itself. Questions guide the 
collection of data to fill in gaps and to extend the theory as a further 
integrative strategy. Theoretical sampling involves an iterative process 
(Figure 3.2) and requires time to learn.
Remaining analytical and flexible while also
making theoretically sensitive judgments about saturation is 
never precise. The researcher’s judgment becomes confidently 
clear only toward the close of his joint collection and analysis, 
when considerable saturation of categories in many groups to 
the limits of his data has occurred, so that his theory is 
approaching stable integration and dense development of 
properties. (Glaser & Strauss, 1967, p. 64)
Theoretical sampling also reduces the amount of data needed 
(Glaser & Strauss, 1967, pp. 109–111). At the same time, inadequate 
sampling will be evident in theory that is thin and not well integrated 
(p. 63). As Glaser and Strauss (1967) state,
The basic question in theoretical sampling . . . is: what groups 
or subgroups does one turn to next in data collection? And for 
what theoretical purpose?  .  .  .  The possibilities of multiple 
comparisons are infinite, and so groups must be chosen 
according to theoretical criteria. (p. 47)
Data collection
and analysis
Theoretical
sampling
Figure 3.2    An Iterative Process 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
40
Comparisons provide diverse and similar indicators of conceptual 
categories and their properties. Discovering similarities and differences 
are important because they can indicate conditions under which 
categories and their properties vary and to what degree they may 
vary. Theoretical sampling encourages the theorist to employ multiple 
“slices of data” (Glaser & Strauss, 1967, p. 65) to explore a range of 
perspectives. Glaser (1978) offers a number of strategies for focusing 
data collection in theoretical sampling, including changing the 
location, participants, or interviewing style to elicit different slices of 
data; following up on emerging patterns in the data; and selectively 
sampling for additional data on specific concepts that appear to be 
significant to the emerging theory and, where possible, going back to 
key participants to ask for more information on these particular 
concepts. As Glaser and Strauss (1967) explain, the variety in data 
slices should be based on theoretical understanding of the conceptual 
category under diverse conditions and not simply on triangulated 
data types (p. 66). Sampling is determined only by the necessity of 
theoretical relevance and coverage. If the need to mix different types 
of data (quantitative and qualitative) emerges during the research 
process, the purpose is not to test or correct what has been found 
previously but to extend understanding of the phenomena under 
scrutiny and of the scope of the emerging theory, as well as to densify 
the concepts toward theoretical saturation: “Different kinds of data 
give the analyst different views or vantage points from which to 
understand a category and to develop its properties” (Glaser & 
Strauss, 1967, p. 65).
When theoretically sampling quantitative data, statistical validity 
may be set aside because data collection should be guided only by the 
emerging theory. All cases, including deviant minority cases, can be 
taken into account, as they are all potentially important and might 
require further qualitative analysis in order to discover unobserved 
variables and causal statements (Glaser, 2008). When software are used 
for some of the analyses, researchers can reflectively ensure that the 
mathematical algorithms in the software do not eliminate these 
statistically insignificant cases or groups of cases without investigating 
them, with the help of qualitative data previously collected or 
specifically collected for this purpose. Mixing quantitative and 
qualitative data whenever needed, and analyzing and interpreting 
them in an embedded manner, will allow researchers to identify 
whether or not so-called statistically insignificant results should in fact 
be taken into account. The reader may refer to Appendix C, Section 1.3 
for an illustration of theoretically sampling mixed data.


Chapter 3    Foundational Pillars of Classic Grounded Theory
41
	
DEVELOPING YOUR STANCE AS A GROUNDED THEORIST	
l
As mentioned at the outset of this chapter, many published accounts 
suggest that GT is a difficult methodology to master. Indeed, the 
expectation of joint collection, coding, and analysis of data with all 
three operations being done together, blurring and intertwining 
continually, from the beginning of the investigation to its end (Glaser & 
Strauss, 1967, p. 43) may seem a daunting quest to undertake, 
particularly for those trained in highly structured, linear, lockstep 
methodological approaches. What some may view as exciting and 
freeing can be seen by others as a terrifying jump into the abyss of 
endless and overwhelming data. Glaser (1992) cites the requisite skills 
for doing GT as the ability “to absorb the data as data, to be able to step 
back or distance oneself from it, and then to abstractly conceptualize 
the data” (p. 10). Glaser (1998) describes skill development in GT as a 
delayed action learning process (p. 220). He cites the importance of 
three attributes in the GT researcher: “an ability to conceptualize data, 
an ability to tolerate some confusion, and an ability to tolerate 
confusion’s attendant regression” (Glaser, 2010, p. 4).
Glaser also repeatedly insists that GT is often best achieved by novice 
researchers, who are not as yet invested in a favored discipline perspective 
or theoretical code (e.g., power dynamics, social networks, etc.) but who 
are motivated and open to learning (Glaser, 2003). Over and over, in his 
writing, Glaser emphasizes the importance of staying open and not forcing 
preconceived ideas or frameworks upon data but rather fostering the 
emergence of conceptual theorizing from the empirical data. This dictum 
runs throughout all of his writings and must be seen as fundamental to 
developing the necessary frame of mind for doing GT.
Glaser and Strauss (1967) cite, as well, the crucial role of theoretical 
sensitivity in one’s ability to develop good grounded theories. Much has 
been written about theoretical sensitivity; indeed, Glaser’s second book 
is titled Theoretical Sensitivity, which may suggest its centrality to doing 
GT from his perspective. Glaser and Strauss (1967) describe theoretical 
sensitivity as the capacity to conceptualize and formulate a theory as it 
emerges from data; a continuous skill development process that involves 
both a personal and temperamental bent as well as the ability to “have 
theoretical insight combined with ability to make something of these 
insights” (p. 46). Suddaby (2006) also recognizes the subtle but significant 
interplay between technique and creativity in doing GT:
Successful grounded theory research has a clear creative 
component. Glaser and Strauss were aware of this component 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
42
and the tension it would create with those who find comfort in 
trusting an algorithm to produce results. Glaser (1978) used the 
term “theoretical sensitivity” to describe the essential tension 
between the mechanical application of technique and the 
importance of interpretive insight. (p. 636)
Klag and Langley (2013) describe theoretical sensitivity as the 
capacity to generate conceptual leaps and suggest that “life events, 
background reading and unique training and work trajectories—may 
offer resources for insight” (p. 157). Glaser, however, warns not to 
allow personal perspectives and assumptions to taint one’s openness to 
what may emerge in a GT study. Fundamental to one’s theoretical 
sensitivity, he suggests, is the ability to “enter the research setting with 
as few preconceived ideas as possible—especially logically deducted, a 
priori hypotheses” (Glaser, 1978, pp. 2–3). Hernandez (2010) agrees 
with Glaser, suggesting that researcher preconceptions in the form of 
“personal beliefs or biases regarding a particular substantive area or 
ideas gleaned from published articles within this substantive area” 

(p. 154) are major blocks to developing theoretical sensitivity.
Glaser (1978) describes theoretical sensitivity as the ability to take 
a transcending view of data; to become a “non-citizen” (p. 8) by not 
privileging any particular perspective or view but rather letting the 
data speak for themselves. This is particularly the case with extant 
theory where Glaser suggests one need simply to footnote the work of 
others and integrate their ideas into one’s emerging theory where 
relevant through “emergent fit” (pp. 8–9). Here, he is not suggesting 
that the theorist reject extant theory or that one must acknowledge and 
address all possible theoretical perspectives in one’s analysis but rather 
that the theorist apply sensitivity and judgment in how to balance 
scholarship of extant theory and analysis of emergent conceptual ideas 
in service to theory with parsimony and scope (pp. 10–12).
From such statements we can see that theoretical sensitivity is not 
about bringing one’s knowledge of extant theory into framing a GT 
study through theoretical frameworks and extensive literature reviews 
at the outset of a study. Glaser (1992) elaborates further on theoretical 
sensitivity by stating that it “refers to the researcher’s knowledge, 
understanding and skill, which foster his generation of categories and 
properties and increases his ability to relate them into hypotheses, and 
to further integrate the hypotheses, according to emergent theoretical 
codes” (p. 27). Kelle (2005), however, complains that Glaser and Strauss 
have not provided sufficiently clear methodological rules for developing 


Chapter 3    Foundational Pillars of Classic Grounded Theory
43
one’s theoretical sensitivity suggesting that “it remains unclear how a 
theoretically sensitive researcher can use previous theoretical 
knowledge to avoid drowning in the data” (para. 9). McCallin (2006) 
suggests that the grounded theorist must embrace a particular style of 
thinking that is creative, inquisitive, critical, analytical, and comfortable 
with complexity—a style that is not necessarily inherent in all 
researchers and one that can be challenging to the novice.
Developing grounded theory—and one’s stance as a grounded 
theorist—takes time and requires that the theorist is comfortable in 
tolerating confusion (Glaser, 1998, pp. 100–102), developing a rhythm 
of personal pacing as one cycles through the various analytic stages 
(Glaser, 1978, pp. 18–33), and assuming autonomy and ownership of 
one’s emerging theory and its contribution to knowledge (Glaser, 1998, 
pp. 98–100). Perhaps most important to developing the stance of a true 
grounded theorist, Glaser suggests, is the ability to trust in GT’s 
emergent power (1998, pp. 235–254).
IN SUMMARY
GT’s three foundational pillars are exploration and emergence (i.e., con­
cepts and relationships come from the data and are not preconceived or 
imposed on data), constant comparative analysis (i.e., data are continu­
ously compared with previously collected and analyzed data, looking 
for similarities and differences to help toward conceptualization and 
theorization), and theoretical sampling (i.e., sampling is directed by the 
emerging theory and continues until theoretical saturation). Doing GT 
requires researchers to acquire and build up their theoretical sensitivity, 
which is the capacity to conceptualize and formulate a theory as it 
emerges from data and without any preconception.
TEST YOUR KNOWLEDGE
In the following questions one or several answers may be correct.
1.	 When you do GT
a.	 You have to do a literature review before you start collecting 
data
b.	 You analyze your data when you have finished collecting 
them


AN OVERVIEW OF CLASSIC GROUNDED THEORY
44
c.	 If you use quantitative data, your sampling has to be 
­
statistically significant
d.	 None of the above
2.	 Theoretical sensitivity is
a.	 The capacity to adapt an existing framework
b.	 The capacity to apply a theory
c.	 The capacity to conceptualize a theory
d.	 The capacity to formulate a theory
e.	 None of the above
3.	 GT’s foundational pillars are
a.	 Emergence, constant comparative analysis, and sampling
b.	 Emergence, content analysis, and theoretical sampling
c.	 Verification, explication de texte, and theoretical sampling
d.	 Emergence, constant comparative analysis, and theoretical 
sampling
e.	 None of the above
4.	 Memos
a.	 Are to be written at the end of the process of doing GT
b.	 Should be written during the GT process
c.	 Are important when you do GT
d.	 Are not important in the GT process
e.	 None of the above
FURTHER READING
Breckenridge, J. (2009). Demystifying theoretical sampling in grounded theory 
research. Grounded Theory Review, 8(2), 113–126.
Glaser, B. G. (1978). Theoretical sampling. In Theoretical sensitivity: Advances in 
the methodology of grounded theory (pp. 36–54). Mill Valley, CA: Sociology 
Press.
Glaser, B. G. (2013). No preconceptions: The grounded theory dictum. Mill Valley, 
CA: Sociology Press.
Glaser, B. G., & Strauss, A. L. (1967). Theoretical sampling. In The discovery of 
grounded theory: Strategies for qualitative research (pp. 45–77). Hawthorne, 
NY: Aldine de Gruyter.
Gynnild, A. (2006). Growing open: The transition from QDA to grounded 
­
theory. Grounded Theory Review, 6(1), 61–78.
Roderick, C. (2009). Learning classic grounded theory: An account of the jour­
ney and advice for new researchers. Grounded Theory Review, 8(2), 49–63.


Chapter 3    Foundational Pillars of Classic Grounded Theory
45
ENDNOTES
1.	 The 
Creative 
Commons-Attribution-NonCommercial-ShareAlike 
License 4.0 International applies to all works published by the 
International Journal of Qualitative Methods. Copyright for articles 
published in the International Journal of Qualitative Methods remains 
with the first author.
2.	 The double arrows in Figure 3.1 illustrate the constant comparative 
analysis and “inextricable link between data collection and analysis” 
(Birks, Fernandez, Levina, & Nasirin, 2013, p. 3) leading to the discovery 
of patterns, which in turn guide further theoretical sampling until 
­
theoretical saturation is reached.
3.	 Literally translated, this French expression means “explanation of 

a text.”
4.	 What are these data a study of? What category or property of category 
does this incident indicate? What is going on? What accounts for the 
main issue, and how is this issue processed or resolved?


46
4
Clarifying Common Sources 
of Confusion in Grounded 
Theory
After studying this chapter, you will:
• identify, understand, and avoid common sources of preconception that 
limit emergent discovery in grounded theory
• differentiate between describing research findings and conceptualizing 
data
• differentiate between substantive and theoretical coding
M
ost people carry assumptions about how to do research. These 
assumptions result from their university background and PhD 
training and sometimes prove to be a real challenge to recon­
cile with those of classic GT. Indeed, some might prove irreconcilable. 
Among the common assumptions are the need for a thorough literature 
review at the outset of a study to identify a gap that justifies pursuing 
the study, to establish a theoretical framework for data collection and 
analysis, and to formulate hypotheses to be tested or precise research 
questions developed ex ante to be answered in the study. Such assump­
tions are a major source of confusion and frustration for those wishing 


Chapter 4    Clarifying Common Sources of Confusion in Grounded Theory
47
to follow a classic GT approach. In this chapter, we address three com­
mon sources of methodological confusion that will undermine your GT 
efforts: emergence versus preconception, description versus conceptu­
alization, and substantive versus theoretical coding.
	
EMERGENCE VERSUS PRECONCEPTION	
l
Research studies are often initiated to explore or confirm a specific 
research question or to test formulated hypotheses that are of interest to 
the researcher. GT’s discovery stance, however, rejects the formulation 
of preconceived notions regarding the focus of a study in favor of 
exploring openly (i.e., without preconception) what is actually going on 
in the area under study rather than what we might decide at the outset 
should be the focus for inquiry. “GT listens to participants’ own . . . con­
cerns and then subsequently crafts questions from emergent concepts 
for subsequent theoretical sampling” (Glaser, 2003, ­
p. 118). Remaining 
open to discovering what is really going on in the field of inquiry is 
often blocked, however, by what Glaser (1998) refers to as the forcing of 
preconceived notions resident within the researcher’s worldview (i.e., 
ontological and epistemological perspective), an ­
initial professional 
problem to be studied, or an extant theory and framework around 
which to focus the inquiry—all of which preempt the researcher’s 
­
ability to suspend preconception. An experienced grounded theorist, 
Ólavur Christiansen describes professional research problems as
problems “thought-out” and “thought-up” by the researcher, 
problems logically derived by the researcher and his/her 
seniors, problems found methodologically convenient and 
methodologically appropriate for the researcher, problems 
made apparently relevant by the availability of certain data 
(frequently quantitative data), problems made apparently 
relevant by fitting into some received concepts from preexistent 
theory, or problems made apparently relevant by fitting into 
some preconceived or spuriously drawn borders between sub-
disciplines. Such “professional problems” are typically not 
grounded in the underlying and essential agenda of those being 
studied. (Personal communication, July 19, 2004)
In GT the intention is to remain open to what emerges as a main 
issue in the substantive area of inquiry. To do so requires that the 
researcher be theoretically sensitive to discovering this main concern as 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
48
opposed to pursuing a specific, predetermined research interest. “The 
first step in gaining theoretical sensitivity is to enter the research setting 
with as few predetermined ideas as possible—especially logically 
deduced, a priori hypotheses . . . to remain open to what is actually hap­
pening” (Glaser, 1978, p. 3). To be sure, a researcher’s professional train­
ing and experience will often stimulate an initial research interest 
providing the motivation for pursuing a study, but a GT study must set 
aside the preconceiving practices of traditional training and established 
research methodology that condition the researcher to approach a study 
through an extant theoretical framework, ad hoc hypotheses, defined 
research questions, interview protocols, or precise coding schemes.
Preconception: A Classic Example!
Glaser recalls how meeting Alvin Gouldner (1920–1980) impressed upon 
him just how far off a preconceived interest can be from what is really going 
on in a research setting. He recounts Gouldner’s using the example of a 
study in which a student was sent out to study risk-taking behavior among 
steeplejacks in New York City. The student was frustrated by the difficulty 
he had in getting the “jacks” to talk about risk taking in their line of work 
until one day, he observed them drawing straws before beginning a job and 
he was certain they were using the process to allocate the potential risk. 
To his surprise, the tactic had nothing to do with risk; rather, they were 
drawing straws for the best vantage points in terms of window peeping 
while on the job! The preconceived professional concern with risk taking 
was displaced with a focus on what was really taking place—a study in stra­
tegic positioning. (Holton, 2011, p. 213)
The fundamental premise of avoiding preconception has been dis­
missed by some scholars as naive and unrealistic (Dey, 1993, 1999; Kelle, 
2007). Charmaz (2014) insists that “guiding interests, sensitizing con­
cepts, and disciplinary perspectives” can serve as “points of departure 
for developing, rather than limiting, our ideas” (p. 31), but this is a slip­
pery slope. When our inquiry is pre-framed through such heuristics, we 
can inadvertently shape how we approach our study and the resultant 
data that we use (see “Staying Open” in Appendix B, pp. 168). The adage 
“start as you mean to go” is particularly relevant to GT. The intention 
here is not to dismiss extant knowledge but rather to suspend its privi­
leging in the initial design and framing of the study. “To preconceive 
relevance is to force data, not to discover from data what really works as 
a relevant explanation” (Glaser & Strauss, 1967, pp. 142–143).


Chapter 4    Clarifying Common Sources of Confusion in Grounded Theory
49
Yet, to remain truly open to GT’s principle of emergence can be one 
of the most challenging issues to those new to GT. This is a challenge 
that is often complicated by institutional requirements that routinely 
and without question require such pre-formulation at the study pro­
posal stage. Such assumptions are so engrained in the scholarly com­
munity that they have become pro forma requirements in thesis 
proposals at many universities as well as in applications to ethics 
review boards and research funding agencies.1 While compromises are 
necessary (Xie, 2009), all too often, mixing GT with other research 
approaches unfortunately reduces the outcome to conceptual descrip­
tion, presented thematically or evidentially, rather than as an inte­
grated conceptual explanation. While such practices can render 
perfectly acceptable research findings, the absence of a core GT pillar 
(i.e., emergence) invalidates the claim of grounded theory.
A persistent preconception regarding GT is that it is a qualitative 
research method (Clarke, 2005; Goulding, 2002; Locke, 2001; Shah & 
Corley, 2006). We have already addressed this misconception in previ­
ous chapters. To reiterate, as a general research paradigm, GT can use 
any and all data and can adopt any philosophical perspective appro­
priate to the data and the ontological stance of the theorist. Consistent 
with the misguided assumption that GT is a qualitative method, many 
published guides to doing GT advocate the use of a preconceived theo­
retical framework (Goulding, 2002; Partington, 2002; Suddaby, 2006). 
Conducting an initial literature review is another dimension of precon­
ception that is frequently advocated, but that again undermines the 
basic premise of classic GT, that is, that theory emerges from the con­
ceptualization of data and not from extant theory. Such extensive 
review of the literature at the outset of a study can stifle a researcher’s 
efforts to remain open to discovering what concepts are in the data 
regardless of what extant theories might suggest should be the focus of 
inquiry.
GT does not ignore extant literature, however. Rather, it simply 
holds review of the literature in abeyance until the core category of the 
grounded theory has emerged. Doing so avoids the possibility of forc­
ing the theoretical direction of the study and instead enhances the 
potential to discover a true main concern in the area under study and 
to theorize from a new perspective and in rupture with existing 
literature.
Practically speaking, reviewing literature at the outset may simply 
result in the researcher spending valuable time on an area of literature 
that proves to be of little significance to the discovered grounded 
theory.


AN OVERVIEW OF CLASSIC GROUNDED THEORY
50
Indeed the analyst may be hard put to know which conceptual 
field [to invest in] until a theory [with its core category] 
emerges. If there is a particularly good theory in the field, one 
may . . . look for emergent fits. The result is usually extending 
and transcending the extant theory. (Glaser, 1978, p. 31)
Extensive engagement with the literature prior to data collection 
and analysis may also constrain the researcher’s ability to remain open 
to theoretical ideas that may not have figured prominently in the litera­
ture. In classic GT,
the literature is just more data to be coded and integrated into the 
study through constant comparative analysis but its analysis and 
integration happens only after the core category, its properties 
and related categories have emerged, and the basic conceptual 
development is well underway, not in advance as is commonly 
presented in published research. Unless pre-empted by precon­
ception, emergence is natural with the resultant grounded theory 
often charting new theoretical territory. (Holton, 2007a, p. 272)
l
l
DESCRIPTION VERSUS CONCEPTUALIZATION
Some Firsthand Experiences of Novice Grounded Theorists
“At times I feel as if I am lingering on the ‘ground level of description’ wait­
ing for the wings of conceptualization to pop up on my back and enable me 
to fly.” (“The Conceptualization of Data,” 2010)
“I have a lot of categories and relationships between them but I am 
afraid that I am using too much of the wording from existing theory.” 
(E-mail excerpt from PhD student)
“My supervisor had her own preconceived ideas and was bent on full 
description. She kept asking me which group said what and to give percent­
ages on the initial coding results.” (E-mail excerpt from PhD student)
“Quite early in the research process, however, a ‘story’ presented by 
a participant was so compelling, and original, that just to ‘code’ the inter­
view transcript without a full inclusion of this particular participant’s story 
would have seriously diminished the research outcome.” (Bryant & Lasky, 
2007, p. 179)


Chapter 4    Clarifying Common Sources of Confusion in Grounded Theory
51
As the comments in the previous box suggest, those new to GT 
often struggle to transcend the descriptive level. Transcending descrip­
tive detail to achieve a conceptual explanation rather than simply 
describing research findings can be a particular challenge for those 
who are research-trained in evidence-based professional disciplines 
that value solely empirical detail. However, moving to explanation and 
the subsequent explanatory power of conceptual theory has much to 
contribute to these fields where GT’s theoretical completeness offers a 
“powerful, predictive ability” (Guthrie, 2000, in Glaser, 2002, p. 342).
Even though some studies might start with description before con­
ceptualization is brought into play, unlike descriptive methods, the 
goal of GT is to discover and conceptually explain a main concern in a 
substantive area of inquiry and how that concern is addressed or 
resolved. Grounded theory is not about the accuracy of descriptive 
findings. It is about tapping into the latent structures in social settings. 
In abstracting these patterns to a conceptual level, GT theoretically 
explains rather than describes behavior that occurs generally in many 
diverse groups with a common concern (Glaser, 2003). While empiri­
cally grounded in one substantive area, the conceptual nature of a 
grounded theory offers general implications as theoretical propositions 
that extend beyond its empirical grounding.
In contrast, some qualitative research produces descriptive find­
ings from which generalizations may be suggested but which are 
always limited by the sample (unit, case, etc.). At the other end of the 
spectrum, some quantitative research aiming at uncovering universal 
laws, considered as true until proved otherwise, overgeneralizes with­
out taking structures, context, and human agency into consideration. 
“The concern is always accuracy, that is, the researcher has worrisome 
accuracy to constantly contend with. Generalizations are difficult to 
make from one unit to another and even within the unit studied” (Gla­
ser, 2007, p. 42). Description bogs down conceptualization and reduces 
the level of abstraction with the concepts buried within the detailed 
description. As such, it is difficult, if not impossible, to see the simple 
elegance in the relationships between concepts that constitute the 
propositions or hypotheses that together integrate the (grounded) the­
ory into a rich, multivariate conceptual theory (Glaser, 2003).
A further limitation of qualitative description is that it is confined 
to the unit studied, with emphasis on accurately depicting that unit but 
offering no claims to generalizing findings beyond the study sample. 
“The theory remains ‘trapped’ in the unit and is read as description” 
(Bigus, Hadden, & Glaser, 1994, p. 44). Description is also stale dated 
because as the unit of analysis changes over time, the description 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
52
becomes less accurate and relevant. By contrast, GT’s focus on latent 
patterns of social psychological and social structural processes, action, 
and interaction gives it an enduring quality, and its propositional (not 
evidentiary) presentation suggests general implications of the theory 
that apply beyond the unit(s) theoretically sampled (and more gener­
ally, with some modification based on further theoretical sampling, 
thus eliminating the risk of hasty generalization).
A certain mystery seems to surround the notion of conceptualization. 
Klag and Langley (2013) refer to the “conceptual leap” when theorizing 
from empirical data as “a consciously realized and abstract theoretical 
idea in an empirical study . . . beyond the specific context . . . connect[ing] 
situated particularities with conceptual understandings . . . grounded 
in abductive reasoning” (p. 150) and theoretical sensitivity as the 
capacity to generate such conceptual leaps (p. 157). Although they 
recognize GT’s creative and iterative nature, they conflate GT with 
qualitative research and characterize the process as one of wading 
knee-deep in data while leaping from insight to insight. In so doing, 
they capture the qualitative researcher’s experience of “data 
overwhelm” (Glaser, 2003, p. 24) but appear to miss the systematic 
methodology inherent in classic GT through constant comparative 
analysis, theoretical sampling (for core category emergence), and 
theoretical coding (for theoretical integration). GT’s full package goes 
well beyond simply leaping from data to theorizing.
l
l
SUBSTANTIVE VERSUS THEORETICAL CODING
Coding is a rather ubiquitous term, often applied to describe the itera­
tive and complex procedures at the heart of GT’s constant comparative 
method of analysis. In GT, coding refers to both the substantive coding 
of empirical data and, later, to the theoretical integration of emergent 
concepts through theoretical coding. While coding and memoing in GT 
proceed in tandem, substantive and theoretical coding do not. Theo­
retical coding is subsequent to substantive coding.
Substantive coding is the process of conceptualizing the empirical 
substance of the area under study, that is, the data in which the theory is 
grounded. Incidents in the empirical data are coded for indicators of 
concepts from which a grounded theory is then generated. Substantive 
coding begins with the initial open coding of data where the analyst 
codes each incident for as many concepts as possibly relevant. Open 
coding proceeds (in tandem with memoing) until the analyst perceives 
a pattern emerging that may indicate a potential core category. Once a 


Chapter 4    Clarifying Common Sources of Confusion in Grounded Theory
53
core category has been tentatively identified, the analyst switches from 
open coding to selective coding by delimiting data collection and 
analysis to just the core category and any potentially related concepts. 
At this point, the analyst focuses on theoretically saturating the core 
category and related concepts and then proceeds to the final stage of 
GT coding: theoretical coding. Theoretical codes conceptualize how the 
substantive codes may relate to each other as hypotheses to be 
integrated into the final theory, reminding us that the essential 
principle of earned relevance in GT is not restricted to conceptual 
emergence but is equally important in the conceptual integration of the 
theory through the emergence of relevant theoretical codes.
In theoretical coding, the analyst constantly compares and ques­
tions the many analytic memos that have been generated through the 
earlier stages of substantive coding. This conceptual sorting (Glaser, 
1978, p. 116) generates further memoing as the analyst explores the 
relationships among the core category and other categories. This pro­
cess is done manually and thoughtfully with the analyst exploring 
connections and conceptual orderings among the concepts until a pat­
tern emerges that organizes the concepts as a theoretical model with 
full explanatory power of the main concern. The underlying impera­
tive, however, is that theoretical coding is not selected and imposed on 
the theory but that it must emerge and fit to earn its relevance as a theo­
retical integrator of the core category and related concepts.
Common misunderstandings regarding theoretical coding include 
(a) that it refers to the use of theoretical labels when coding empirical 
data and (b) that the analyst selects a specific theoretical code and 
applies it to the overall analysis of the data (Kelle, 2005). Various schol­
arly accounts of GT methodology have offered confusing and ultimately 
misleading advice regarding coding. Locke (2001), for example, describes 
GT coding as substantive and theoretical but then suggests that theoreti­
cal codes are discipline-related and applied by the analyst “through a 
kind of top down process” (p. 66). She goes on to suggest that while the 
general rule is that substantive coding occurs early in the study and 
theoretical coding at a later point, “these are not hard and fast 
rules . . . researchers will be working with a mix of in vivo and theoretical 
categories throughout their analysis” (p. 66). It would appear that she 
has not understood the transition from substantive coding to theoretical 
coding through core category emergence and theoretical saturation.
Gibson and Hartman (2014) offer a somewhat vague account of 
theoretical codes as selected and applied during selective coding, with 
their particular preference being the use of typologies to explore relation­
ships between concepts. They make no mention of the hand sorting 


AN OVERVIEW OF CLASSIC GROUNDED THEORY
54
process in theoretical coding that Glaser has repeatedly emphasized as 
crucial to ensuring that theoretical codes are emergent and not forced 
(Glaser, 1978, pp. 116–127; 1998, pp. 187–192; 2005a, pp. 33–40). Dey 
(1999) suggests that the distinction between substantive and theoretical 
coding is unclear. He poses three questions that suggest his level of con­
fusion is considerable: “Is theoretical coding an aspect of substantive 
coding or a separate activity? How do we select from among theoretical 
codes that all fit the data? Are the empirical cues guiding selection based 
only on observation?” (p. 109). Here again, such questions indicate that, 
while offering advice on GT, the author has not understood the progres­
sive nature of GT coding or the emergence and appropriate fit of 
­
theoretical codes through hand sorting of conceptual memos.
Another common misconception regarding theoretical coding is 
that grounded theory is always a basic social process (Clarke & Friese, 
2007). To be sure, the basic social process is undoubtedly the most 
widely used theoretical code in grounded theory. Glaser (2005a) 
acknowledges this popularity and frequency but at the same time 
notes, “I have seen many GTs that have no process in them, so process 
must be earned, NOT assumed” (p. 96). As another example of over­
reliance on one theoretical code, Glaser (1978) notes the tendency of 
some theorists to conflate behavioral consequences with intentional 
strategies and to rely too frequently on the strategy family of theoreti­
cal codes (p. 76). He has offered a wide range of potential theoretical 
codes (Glaser, 1978, pp. 72–82; 1998, pp. 163–175; 2005a, pp. 17–30) and 
encourages researchers to read widely from theories in many disci­
plines as a means of identifying and adding new theoretical models to 
their repertoire. The purpose here is not to select (i.e., preconceive) a 
model but to simply enhance theoretical sensitivity by opening the 
researcher up to the numerous ways in which theory can be modeled.
IN SUMMARY
Common sources of confusion in grounded theory can be streamlined 
to three main areas: emergence (i.e., remaining open to what is actually 
going on in the data) versus preconception (i.e., forcing preconceived 
notions on the data), description (i.e., describing research findings con­
fined to the unit studied) versus conceptualization (i.e., conceptually 
explaining how a concern is addressed and solved in a substantive 
area), and substantive (i.e., conceptualizing the empirical substance of 
the area under study) versus theoretical coding (i.e., conceptualizing 
relationships between substantive codes).


Chapter 4    Clarifying Common Sources of Confusion in Grounded Theory
55
TEST YOUR KNOWLEDGE
In the following questions one or several answers may be correct.
1.	 When you conduct a research using classic GT
a.	 You start with a research question
b.	 You must use qualitative data
c.	 You must conduct an extensive literature review ex ante
d.	 None of the above
2.	 When you conduct research using classic GT
a.	 Extensive description is necessary
b.	 You must make theoretical propositions
c.	 You should not read about theories in other domains
d.	 None of the above
3.	 Substantive coding
a.	 Includes open and selective coding
b.	 Explains the main concern
c.	 Is about searching the core category
d.	 None of the above
4.	 Theoretical coding
a.	 Helps the researcher to sort conceptually
b.	 Explains the main concern
c.	 Involves making propositions linking concepts
d.	 None of the above
FURTHER READING
Gibson, B., & Hartman, J. (2014). Rediscovering grounded theory (Chapter 12). 
London: Sage.
Glaser, B. G. (1978). Theoretical sensitivity: Advances in the methodology of grounded 
theory (pp. 2–3, 31–33, 44–46). Mill Valley, CA: Sociology Press.
Glaser, B. G. (1998). Doing grounded theory: Issues and discussions (Chapters 

5 & 6). Mill Valley, CA: Sociology Press.
Glaser, B. G. (2001). The grounded theory perspective: Conceptualization contrasted 
with description (Chapters 3, 4, & 6). Mill Valley, CA: Sociology Press.
Glaser, B. G. (2011). Getting out of the data: Grounded theory conceptualization. Mill 
Valley, CA: Sociology Press.
Glaser, B. G. (2013). No preconceptions: The grounded theory dictum. Mill Valley, 
CA: Sociology Press.


AN OVERVIEW OF CLASSIC GROUNDED THEORY
56
ENDNOTE
1.	 For some helpful advice on managing ethics review, see http://www
.groundedtheoryonline.com/getting-started/ethical-review-irb.
2.	 The 
Creative 
Commons-Attribution-NonCommercial-ShareAlike 
License 4.0 International applies to all works published by the 
International Journal of Qualitative Methods. Copyright for articles 
published in the International Journal of Qualitative Methods remains 
with the first author.


57
5
Finding Your Data
After studying this chapter, you will:
• understand grounded theory’s use of data
• know how to collect data for a grounded theory study
• understand the iterative nature of data collection and analysis in doing 
grounded theory
	
WHY “ALL IS DATA”	
l
As grounded theorists, we have the wonderful freedom to identify and 
utilize in our analyses any and all data obtained from any relevant, 
available source. Doing so enhances the power of our theories to 
explain what is really going on. We use the data as we choose and find 
it to explain the pattern of behavior at issue. This explanatory power is 
a primary reason that many of us are drawn to GT. Its conceptual 
power renders GT valuable not only for theorists but also for practitio­
ners and the participants in our studies through its focus on explaining 
“high impact main concerns” (Glaser, 1995, p. 4). The GT process 
ensures that our research, when finished, will be “actionable” (Schön, 
1983).
In many methodological books, and as highlighted in Walsh (2014c), 
much is said about methods and techniques and little about data. 


CLASSIC GROUNDED THEORY IN PRACTICE
58
However, methods and techniques do not mean much without data, as 
“data is a necessary basis on which to build theories” (Evermann & Tate, 
2011, p. 634) and, in most cases, you need good data to produce good 
theory (Lyytinen, 2009). Creswell (2014) describes qualitative data as text 
and images and quantitative data as numbers. The term quantitative data 
is mostly used to describe a type of information that may be counted or 
expressed numerically and is amenable to statistical analysis (Monette, 
Sullivan, & Delong, 2011), whereas qualitative data provide explanation 
for, and information about, something in the form of words that describe 
that phenomenon in depth (Monette et al., 2011). However, a researcher 
may choose to quantitize qualitative data (Sandelowski et al., 2009) or to 
qualify (Creswell, 2007) or qualitize (Sandelowski, 2000) quantitative data.
A researcher may also use secondary data in various ways, with 
different techniques, regardless of the methods used to collect these 
data. While many GT studies rely largely on qualitative data and inter­
views as a primary method for data collection, GT uses any and all data 
and data collection methods, including observations, visual and audi­
tory media, or surveys from any sources (e.g., reports, newspapers, or 
questionnaires). As Glaser and Strauss (1967) explain,
in field studies, theoretical sampling usually requires reading 
documents, interviewing, and observing all at the same time, 
since all slices of data1 are relevant. There is little, if any, 
systematic interviewing of a sample of respondents, or 
interviewing that excludes observation. At the beginning of 
the research, interviews usually consist of open-ended 
conversations during which respondents are allowed to talk 
with no imposed limitations of time. Often the researcher sits 
back and listens while the respondents tell their stories. Later 
when interviews and observations are directed by the emerging 
theory, he can ask direct questions bearing on his categories. 
These can be answered sufficiently and fairly quickly. Thus, 
the time for any one interview grows shorter as the number of 
interviews increases, because the researcher now questions 
many people, in different positions and different groups, about 
the same topics. Although the time taken by most interviews 
decreases as the theory develops, the sociologist still cannot 
state how long all his interviews will take because a new 
category might emerge at any time; this emergence will call for 
lengthy open-ended conversations and prolonged observations 
within some groups. Also, theoretical sampling aimed at 
following an incident or observing over a period of time 


Chapter 5    Finding Your Data
59
requires sequential interviews, with no clear notion of when 
the sequence will be terminated. (pp. 75–76)
Furthermore, when developing emerging concepts/constructs that 
have not been extensively investigated in past literature, it is usually 
important to start your investigations with qualitative data. Additional 
slices of quantitative data may be needed to enhance theoretical satura­
tion for generalizability purposes and for formal theory generation. A 
good example of this may be found in Leonardi and Bailey (2008). 
Although the authors never clearly state they conducted their research 
in a GT stance, an informal interview with the second author of this text 
confirmed that they had adopted a GT stance; they did not state this 
clearly in their work because the journal in which they wished to publish 
was not familiar with mixed data GT. You will find more about this issue 
of fulfilling research outlets’ conventions, while not forfeiting your basic 
philosophical assumptions and methodological choices, in Chapter 9.
So this idea of “all is data” (Glaser, 2001) helps us to get away from 
an overemphasis on interview data and opens us up to fully exploring 
a wide range of data sources and slices. For example, one GT study of 
interagency working was completed by observing group meetings and 
using meeting minutes (Wood, 2009). Classic GT studies such as Jones 
and Kriflik (2006) and Sandgren, Thulesius, Fridlund, and Petersson 
(2006) explain their use of personal interviews, informal conversations, 
observation, and document analysis. Hämäläinen (2014) describes 
using opportunistic sampling to access entrepreneurs through his 
social networks and through chance encounters when traveling. 
Snowballing sampling through referrals led to further opportunities 
for data collection. His self-interviews and field observations over a 
period of years as a practicing entrepreneur provided yet more data. 
Glaser and Strauss (1965a) relied extensively on field observation and 
interviews from comparison groups across a range of hospital settings 
in their formulation of awareness theory, including geriatric, preterm 
birth, cancer, intensive care, pediatric, neurology, urology, and 
emergency wards. Walsh (2014a) used secondary qualitative data from 
a previously published GT and from the literature, primary quantitative 
data collected through a survey, and primary qualitative data collected 
through in-depth interviews (see Appendix C, Section 1.3). Focus 
groups are another valuable way to collect data from a number of 
individuals. They can be especially valuable for exploring a potential 
core concept as they allow you to gain the perspectives of a number of 
individuals at one time. Stjernsward and Ostman (2008) used both 
interviews and focus groups in their study of families living close to a 


CLASSIC GROUNDED THEORY IN PRACTICE
60
depressed individual. Focus groups allowed them to capture data 
around mutual experiences and identities and to explore different 
perspectives through group interaction (p. 359). Focus groups can also 
allow you to gain a perspective by observing and noting the dynamics 
of the group conversation.
As with interviews, it is important not to preconceive the direction of 
the group’s conversation by applying a set of preplanned questions; 
rather, open the group up with a “grand tour question” (Simmons, 2010). 
For example, if you were interested in exploring the impending status 
passage (Glaser & Strauss, 1971) of university students in their final year 
of study, you might open the discussion by saying, “I am supposed to be 
studying how students in their final year of study prepare for what’s next, 
but I don’t know what to ask you. What do you think we should talk 
about?” and then wait to see how individuals respond, how the conversa­
tion builds. You might probe by asking what they are doing to prepare for 
the next stage (job hunting, grad school, etc.), how it feels, and so on. 
Again, you can observe whether probing strikes a chord and generates a 
lively exchange or whether something else seems to be of more interest. 
Do different concerns emerge? What appears to be the main concern?
Helpful Hint: Focus groups are more challenging in that a lot can be 
happening at once given the number of individuals involved. Therefore, 
it is usually recommended to have a second person help you facilitate 
and make field notes.
While for grounded theorists, all can serve as data, in conceptualiz­
ing, it is important that we are able to recognize what kind of data we are 
getting. Here we are not referring to the methods of data collection 

(i.e., interviews, observations, surveys) but rather to the intention of the 
data—what Glaser (1998) refers to as types of data—baseline data 

(i.e., honestly describing what is), proper line accounts (i.e., filtered data 
where only what is considered proper or correct is revealed), profession­
ally interpreted (i.e., a third-party expertise perspective), and vague 
responses (i.e., concealing or evading; p. 9). Recognizing the type of data 
that you are getting is also data and is important to discovering what is 
really going on in the situation at hand. Glaser’s (1978) questions for cod­
ing data will help you to think about what types of data you are gathering; 
in particular, his question, “What is actually happening in the data?” (p. 57).


Chapter 5    Finding Your Data
61
Memoing as you code your data will also help you to discover 
what is actually happening in the situation at hand; for example, is the 
interviewee offering an account of his or her own experience (baseline 
description), a reflection on that experience (vague or distanced), a 
projection of someone else’s experience (professionally interpreted), or 
a massaged corporate version (proper line)? All are possibilities, and it 
is important to know what type of data you are getting and why you 
are getting this type of data. What is this really telling you about the 
phenomenon you are studying, and, more precisely, what may be 
emerging as the main concern?
For instance, in the focus group example presented earlier, you can 
observe who appears to dominate the conversation, how others react, 
what the tone is, and what the nature is of the interactions among 
group members; in other words, what is really going on with the group 
in their discussion. Is there a pattern in the way the group interacts? It 
is quite conceivable that using a series of focus groups, you might dis­
cover an interesting GT around, for instance, surfacing sensitive topics, 
silencing, or influencing and persuasion. If you are open to what 
emerges, you may have more than one GT study from the same focus 
group data.
Indeed, observation is an important source of data for doing GT. 
We are surrounded by data but often neglect to observe and note what 
is happening. As one student wrote, “I am a nurse on leave (50%) to do 
my PhD research. I have discovered, however, that when I go into work 
to pick up my mail or to just visit, I find myself observing what I see 
and hear and making note in my memos.” Here is someone who has 
grasped GT’s all is data dictum for leveraging valuable data about 
what’s really going on; these are data that she may not have gained in 
a formal interview setting.
Perhaps You Are Wondering . . . 
How many interviews do I need for a GT study?
There is no magic number of interviews required. While it is common 
for some qualitative or quantitative methods to specify sample size at 
the outset of the study, for a GT study the extent of data 

collection is governed by the emergence and theoretical saturation of 
a core concept. Students are often required, however, to state a specific 
number in their research proposals, and some supervisors may have a 
(Continued)


CLASSIC GROUNDED THEORY IN PRACTICE
62
specific number or range in mind to ensure adequate coverage of a 
population or sufficient data depth. So, how should you address this 
in your proposal? You might consider including a statement like this: 

I expect my field work to involve approximately 30 participants. In accor­
dance with classic grounded theory methodology, however, sample size 
cannot be definitively established in advance of the field work; rather, it 
emerges as the researcher engages in the constant comparison of concep­
tual indicators/incidents articulated by interviewees and proceeds through 
further theoretical sampling to theoretical saturation of emergent concepts. 
Here, you’ve satisfied the requirement by offering a proposed sample 
size while also qualifying the specific number to allow for what 
emerges as necessary for theoretical saturation which you can later 
justify in the methods section of your research paper or thesis.
(Continued)
A frequently overlooked source of data for doing GT is secondary data, 
that is, data that have been collected for some other purpose. Data collec­
tion can be expensive as well as time-consuming. Accessing extant data 
can accelerate and densify conceptual elaboration. As one example, Barney 
Glaser’s doctoral research was based on the secondary analysis of survey 
data conducted by the Survey Research Center at the University of Michi­
gan. Glaser explains that the data consisted of responses to 1,952 survey 
questionnaires completed by research staff at a large government medical 
research organization from which he selected a subset of 332 respondents 
and 100 questions that seemed relevant to his research interest of recogni­
tion in scientific careers (Glaser, 2008, pp. 34–35).2 In addition, Glaser and 
Strauss (1967) devote a full chapter to the use of available qualitative data 
for generating theory. Furthermore, in our times of big data, the Web is also 
a valuable source of already collected data (secondary data, both qualita­
tive and quantitative) that are mostly free and available to all, through 
data mining. Thus, readily available data can be a rich source for theoreti­
cal sampling as a GT study progresses.
Analysis of secondary data can yield unexpected emergent core 
concepts that trigger theorizing. As one example, while conducting a 
qualitative thematic analysis to assess leadership development needs 
in an organization, one author of this text was struck by what she 
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Chapter 5    Finding Your Data
63
observed to be a persistent disconnect between what managers 
expressed about the organization’s efforts to enhance healthy work­
place practices (proper line data) and what they shared about their 
own experiences of the organization’s culture (what was really going 
on). This disconnect emerged in interviews as an unexamined inner 
dialogue where managers struggle to resolve the tension they experi­
ence while still attempting to honor espoused organizational values 
(Holton & Grandy, 2015). While this emergent concept of an inner dia­
logue was outside the objectives of the original research project, it 
offered important insight and explanation as to how middle managers 
in organizations struggle to process conflicts in attempting to honor 
and reconcile personal and organizational values.
Of course, extant literature can also be used as data, particularly if 
the literature is empirically grounded. A major issue in using literature 
as data for a GT study is when to use it. While it is common in many 
research methodologies to initiate a study with a literature review, as 
discussed in Chapter 4, in a GT study we suspend our engagement 
with extant theory so as to avoid the risk of preconceiving our work, 
thereby missing important concepts that may emerge from our data 
simply because we are too focused on what the literature has deemed 
as important. In GT, we start with empirical data and remain open to 
what emerges rather than being influenced by what comes from the 
literature. Once our core concept has emerged, we can use the literature 
as an additional data source to theoretically sample and constantly 
compare concepts from the literature with emergent concepts. As such, 
literature based on empirical data can help to elaborate our emerging 
concepts and assist us in knowing where to direct our attention in fur­
ther theoretical sampling.
Perhaps You Are Wondering . . . 
My university (or funding agency) requires that I do an ini­
tial literature review as part of my research proposal. How 
do I manage this without undermining my GT study?
Standardized approaches to research proposals frequently follow the 
traditional hypothetical-deductive approach where extant literature is 
searched and assessed so that the proposed study can be situated 
within, and contribute to, that body of knowledge. The literature review 
is intended to point to a gap in that knowledge and the proposed study 
(Continued)


CLASSIC GROUNDED THEORY IN PRACTICE
64
Gaining Access
GT studies that rely on qualitative data often feature interviews as a 
primary source of data. Individuals, however, often exhibit reticence to be 
interviewed, especially if you wish to tape-record the interview. Some 
may politely agree but then guardedly engage. It is often much easier to 
engage in informal conversations with participants, simply making note 
of key points, incidents, and responses that you can later write up, code, 
and memo. It is often enough to simply capture a few key words during 
the conversation; you may not even need to write them down at the time 
but simply store key words or phrases in your memory. They will serve as 
triggers for you to sit down as soon as possible after the conversation to 
elaborate in a more extensive field note that you can then code and memo.
Audio taping often sets up a completely different dynamic between 
the researcher and participant. Taping can change the data as the 
researcher relies on the assurance of a taped interview that can be con­
sulted later and, as such, the researcher may relax his or her listening 
rather than staying fully engaged in capturing key words and thoughts 
in situ. In addition, interviewees may respond very differently to ques­
tions when they know their answers are being taped. Many become 
nervous at the prospect of being formally interviewed and taped. Such 
nervousness can be a form of performance anxiety (e.g., Will I be able to 
answer the questions? Will I be asked something I prefer not to answer? If so, 
how do I handle the situation without feeling embarrassed? What if I make the 
researcher unhappy? And so on . . .).
Researchers often worry that by not taping, they will miss impor­
tant data. What you are discovering in a GT study is a pattern that is 
going on all the time, so there are endless opportunities to capture it; 
to contribute to addressing that gap. One strategy for addressing this 
requirement is to keep the initial literature review broad in scope, per­
haps by offering a contextual or thematic overview of the substantive 
area of research interest, setting the stage for an exploratory study 
rather than focusing on a specific gap to be addressed. The latter 
assumes a focus that may not be relevant to the proposed GT study 
and, as such, not only consumes researcher time and energy but may 
well derail the potential to discover an emergent grounded theory.
(Continued)
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COLLECTING YOUR OWN DATA


Chapter 5    Finding Your Data
65
no one interview is that precious. If something is relevant to your the­
ory, you will find multiple indicators in subsequent data as your study 
progresses. Furthermore, taping and then later transcribing leads to a 
volume of data that may render little of relevance in terms of identify­
ing a core concept. Researchers are often required, however, to tape 
interviews as a condition of ethics review or, in the case of doctoral 
students, to demonstrate competence in interviewing. The experience 
of Sandgren, Thulesius, Petersson, and Fridlund (2007) describes an 
approach that accommodates such requirements without unduly 
undermining the GT research approach:
The first ten interviews were tape recorded and transcribed, 
but for the later interviews, only field notes were taken accord­
ing to classic grounded theory (Glaser, 1998, 2001). Interviews 
lasted between 45 and 90 min. By the end of the study, inter­
views were shorter owing to the delimiting properties of 
grounded theory. (p. 228).
Conducting a research study within an organizational context can 
carry its own challenges to gaining access and can take considerable 
time. There are, of course, the ethics review requirements of universities 
and granting agencies that must be met in terms of full and informed 
consent and voluntary participation of the individual participants. To 
gain access to individual participants within an organizational context, 
however, also necessitates determining the appropriate organizational 
gatekeeper, ascertaining their concerns with your request and considering 
what you may be able to do to address these concerns.
One way to initiate the relationship is to request a meeting to dis­
cuss your research project and to seek their advice in how their organi­
zation might be involved in your study. Be humble, polite, and 
respectful in your interactions with the organization and the gate­
keeper, as you will most certainly be shut out if you respond otherwise. 
Once you have been able to allay, or at least reduce, any apprehension 
through an open discussion, you will have a better chance to gain the 
desired access. The more prepared you are to allay any concerns, the 
more likely your chances will be to proceed with data collection with­
out undue delays.
Helpful Hint: Be sure to make notes during the conversation with the 
organizational gatekeeper, as your notes may reveal early indicators of 
a main concern within the organization.


CLASSIC GROUNDED THEORY IN PRACTICE
66
Of course, there will be times when you simply will not be granted 
access or your access will be restricted in such a way that you may not 
gain the data you had hoped for. In such situations, what you gain may 
be proper line data, where the individuals you are permitted to inter­
view and the situations you are permitted to observe will be carefully 
selected and controlled to present the organization in a favorable light. 
These are still data that you can code and memo, but it is important to 
remember what these data are and to capture this in your memos. What 
are you really hearing in these data? What is the context in which you have 
gained these data? What data are you not being given access to? What’s really 
going on in this situation? One way to encourage a breakthrough when 
you think you are being proper-lined is to probe with questions such as 
What do you mean by that? I hear a lot of managers say that, but I’m not sure 
what they really mean. What does it mean to you? Why is that important?
Collecting Quantitative Data
While doing GT, you may feel compelled to use quantitative data slices and 
wish to collect these through a survey, for instance, in order to theoretically 
saturate your concepts. Then, it is often essential during pretests to also inter­
view some of the respondents in order to investigate how the questions asked 
in your survey are understood and if they address the category that you are 
trying to saturate.  While administering the final survey, it is often necessary 
to informally reassure participants and confirm that anonymity will, indeed, 
be maintained and that they will not be traced through their answers. Beyond 
ethical issues, if respondents are convinced you will keep their information 
confidential, it will increase the chances that you are collecting baseline data as 
opposed to proper-line data, more especially if data are self-reported by par­
ticipants and hence subjective. (See Appendix C, Section 1.4 as an illustration 
of the careful choice of measures in a quantitative survey within a GT study.)
While collecting data for a survey, it might also be important to carefully 
note the context of the survey administration. (As an illustration, you may 
refer to Appendix C, Section 1.3.) For instance, if students are respondents 
to a questionnaire, one could legitimately inquire and note if both the ques­
tionnaire administered and the research core investigations are related to a 
course attended by participants. Along the same lines, it might also be neces­
sary to note if student participation to the survey is mandatory as a required 
part of a course, based on partially voluntary participation (extra grades for 
those who answer the questionnaire) or totally voluntary participation (no 
specific incentives). 
Y
our emergent theory may vary with the social structural 
conditions under which the data are collected.


Chapter 5    Finding Your Data
67
In GT, it is also often helpful to reinterview some participants as 
your study progresses. Reinterviewing is a form of theoretical sampling 
that can add important depth to your emerging conceptualization and 
can be as important as the breadth offered by interviewing a range of 
individuals. Reinterviewing can be especially helpful where your access 
to data may be limited to a very specific population or a sensitive area 
of research interest (e.g., transgender youth, marathon runners over the 
age of 80, illegal immigrants seeking health care, etc.). In particular, 
once your core category has emerged, you may wish to theoretically 
sample by returning to earlier interviewees and probing more specifically 
around the emergent core. You might say something like “I recall when 
we chatted previously that you mentioned . . . (core concept). . . . I’ve heard 
from others some similar experiences so I’d like to know more about what you 
experienced.” Or you might say, “In some of my conversations with others, 
they have mentioned. . . . Has this been your experience? Can you tell me more 
about what happened?” Given that reinterviewing is so often a valuable 
way of theoretically sampling to elaborate the properties and dimensions 
of your emergent core concept, it is important to identify your intention 
to employ multiple interviews for theoretical sampling purposes as part 
of your research ethics proposal.
Helpful Hint: In preparing your research ethics proposal, be sure 
to consider potential issues related to gaining access and articulate 
how you plan to address each to ensure the key principles of ethical 
conduct in research (i.e., free and informed consent, voluntary partici­
pation, confidentiality, etc.). It is much easier to address these issues 
at the outset rather than having to later return to the ethics review 
board with amendments. Addressing potential issues at the outset of 
your study forces you to think through what you intend to do and, as 
much as possible, problem solve in advance. It also demonstrates to 
the review board that you have thought through the research process 
and can conduct your study in a scholarly and ethical manner, thereby 
enhancing the confidence of the review board in your overall proposal.
Instilling a Spill
In other research approaches, it is generally researcher interests 
that shape a study. Researchers explore and answer questions that 
come from their own professional interests or, as is often the case with 
novice researchers, topics that have been suggested by a supervisor 


CLASSIC GROUNDED THEORY IN PRACTICE
68
based on their knowledge of the research field and the related litera­
ture. As we have previously explained, in classic GT the research aim 
is to discover a main concern that is being processed, managed, or 
resolved in the substantive area under study. The best way to discover 
this main issue is to avoid, as much as possible, any preconceptions 
regarding what the main issue may be. As Nathaniel (2008) explains, 
“the researcher must be open to hearing the story from the informant’s 
perspective” (p. 61). This means setting out to collect your data without 
a theoretical framework or specific research questions, interview proto­
cols, or predetermined coding schemes.
In collecting interview or informal conversation data for a GT study, 
all that is needed is one “grand tour” question (Simmons, 2010), 
something as simple and open as “Tell me about your job/family/hobby/what 
it’s like to work here, etc.” Sandgren et al. (2006) opened their interviews 
with palliative care nurses by asking, “Tell me what it is like to care for 
palliative cancer patients” (p. 81). Jones and Kriflik (2006) used two 
broad questions to open their interviews: “Tell me what leadership 
means to you,” and “How do you feel about your job?” (p. 160). 
Hämäläinen (2014) found that, in many instances, simply starting the 
conversation with, “Hi. I’m doing [a] PhD about entrepreneurship” 

(p. 17) was all that was needed. Questions such as these open the space 
for interviewees to tell you what they want to talk about. As the researcher, 
your challenge is to wait to see where they wish to take the conversation 
and then to ask follow-up questions that sustain the openness while 
probing enough to have the interviewees elaborate on what they have 
raised. Sandgren et al. (2007) describe using more specific questions such 
as “Tell me about a difficult caring situation,” “How do you handle 
difficult situations?” and “Tell me about a caring situation that went well” 
to encourage interviewees to elaborate on the range of their caring 
experiences (p. 228). As Glaser (1998) points out, the goal in GT is to 
“instill a spill” (p. 111) to find the trigger that starts the person talking 
naturally and exhaustively about what concerns or interests him or her.
Perhaps You Are Wondering . . . 
Can my opening question be too broad? 
One novice researcher asked My field of interest is to research emo­
tions in organizations. I have conducted 10 interviews so far asking 
people how they are feeling about their work. I have coded and memoed 
after each interest. The only issue is that I do not seem to derive 


Chapter 5    Finding Your Data
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Try to set the climate for the interview as one between equals and 
not one of interviewer and interviewee. Your goal is to set the tone for 
the conversation so that your interviewee feels comfortable in opening 
up and sharing with you. Use your intuition; you’ll get a sense from the 
energy in the conversation when the interviewee is finished spilling. 
Continuing to probe may render little of value. Remember, you are not 
seeking to capture descriptive detail; rather, your data are in service to 
identifying concepts.
It may be helpful to begin by talking about yourself and by swap­
ping stories to reduce the sense of formality. However, it is important 
not to dominate the conversation. The less you talk, the more likely you 
are to get to their main concern. If there is a pause in the conversation, 
don’t panic and attempt to fill the void. Offer soft encouragement for 
the individual to continue, for example, a nod or a smile and a simple 
“Please go on.” You might also just repeat the last few words the indi­
vidual has said and then wait. Give the interviewee time to process his 
or her responses. Be patient. As Glaser (2001) says, be a “Big Ear” 

(p. 175), an active and engaged listener. Probe and encourage your 
interviewees to tell you more. Don’t rush them or they may shut down, 
common patterns or processes or even indicators thereof. . . . I am find­
ing that the variables for feelings are endless and are so tightly coupled 
with the individual. I was wondering if this has to do with the fact that 
my inquiry is too broad.
Here we have a novice who is trying to stay open to emergence of 
a main concern but feeling worried as no potential core category (pat­
tern or process) is emerging.  
What should he do? Well, his area of 
interest—emotions in organizations—is clearly a broad topic. 
There are 
several options that he might consider: First of all, he might narrow his 
population to a certain type of organization (public service, communi­
cations, health care, etc.) or to a certain occupation (auditors, recep­
tionists, nurses, etc.) across organizations. He might also narrow his 
opening question somewhat; for example, “How did you feel when you 
arrived at work this morning?” “Tell me about a time when . . . ,” or “What 
feeling would best describe how you feel about your job?” Offering a little 
context for your question should help to elicit focused responses from 
your interviewees without preconceiving and encouraging responses 
around a suggested emotion. Be patient with the first few interviews. 
Stay open and permit your interviewees to decide where they wish to 
take the conversation. Follow their lead with more probing questions.


CLASSIC GROUNDED THEORY IN PRACTICE
70
assuming you are in a hurry or simply not interested in what they want 
to share. This is especially important if what is being shared is in any 
way sensitive. Ensure that your interviewees have time to feel comfort­
able in opening up to you. As one researcher wrote, “I have found that 
once you break the ice with them, they go on and on—literally! They want to 
talk and they want to tell you their experience.”
Staying Open
Glaser (1978) offers useful ideas for staying open during field work. These 
include changing the style of your interviewing (e.g., sharing a walk with the 
interviewee rather than sitting down to converse, etc.) and the place(s) where, 
or time of day when, you normally do interviews (e.g., a park rather than a 
coffee shop; weekends rather than weekdays, etc.). Hans Thulesius, a medical 
doctor and experienced grounded theorist, draws on the advice of famous 
Canadian physician 
William Osler for assessing patients in offering the following:
As a GP, I’ve learned that listening to the patient will give you the diagnosis 
in most cases. One of the worst mistakes a doctor can make is interrupting 
the patient too early because they are already busy formulating a hypothesis 
about the patient’s problem. So in GT interviewing, if you’re doing the same 
things as doctors, you want to go from one part of the story to the next 
so you’re busy thinking about the next question to move the interview 
along but you’re not focusing on what the interviewee is saying. If you 
just wait, almost always the interviewee will give you the rest of the story, 
the explanation you were looking for, so your “thinking up” energy is just 
wasted. Let your interviewee talk. Listen carefully and just give encouraging 
nods or hums, keep eye contact, and eventually repeat the last word in 
what he just said to encourage him to go on with his story—[it] almost 
always works! (Personal communication, October 20, 2014)
Field Notes
GT does not require capturing the type of detailed descriptive data 
that may be considered as important in some circles. However, you 
may find that these descriptive details may be required in order to 
publish your theories in some top tier research outlets (see Chapter 9). 
As far as GT methodology is concerned, however, field notes suffice as 
GT is a concept-indicator methodology that relies on the patterned 
presence of conceptual indicators through multiple incidents as cap­
tured across your data. What you are attempting to do is capture 


Chapter 5    Finding Your Data
71
incidents (experiences, stories, gossip, confessions) that may suggest a 
concept (i.e., a pattern of behavior) that helps to explain what is going 
on (i.e., a main concern) in the situation that you are studying.
Novice researchers will often ask about the right way to do field notes, 
but there is no one right way. Variations on field notes are common to 
many professions; for example, the proceedings of a business meeting 
may be captured as minutes, journalists will make notes during inter­
views, lawyers keep case notes, and doctors and nurses observe and make 
note of a patient’s symptoms. Field notes in GT are not intended to serve 
as detailed evidentiary notes as is common to the health or legal profes­
sion; instead, GT field notes are simply in the moment reminders of inci­
dents that may indicate potential concepts. There is no need to try to 
capture detailed descriptions. You need only capture enough detail about 
the incidents being shared or observed so that you will be able to later 
code your notes for those concepts and elaborate your ideas in memos. For 
instance, when you are interviewing, simply jot down key words as you 
listen and complete your field notes as soon as you have an opportunity 
to code and memo your ideas (see Figure 5.1 for a sample of quick notes 
taken in the field). Or, when you are developing a questionnaire based on 
verbatim data where exploratory factor analysis points at an item as 
double loading on two categories previously identified as different, take 
some brief notes as you go along, until you have time to go back to your 
qualitative data and investigate further. Waiting too long, however, may 
mean that you lose some of the insights that triggered the initial note.
Figure 5.1    Field Notes Taken During an Interview


CLASSIC GROUNDED THEORY IN PRACTICE
72
While doing GT, “any data (qualitative and/or quantitative), films, pho­
tographs, literature etc. may be used. They may be collected by methods, 
and analyzed with the help of techniques that appear suitable . . . to 
facilitate theory emergence, while congruent with . . . [your] philosophi­
cal stance” (Walsh, 2014c, p. 19). Whether you begin data collection with 
interviews, focus groups, observations, social media accounts, organiza­
tional documents, or newspapers, it is important to remember that data 
collection is incremental and iterative. GT enables the stimulation of 
parallel processes of analysis, synthesis, and conceptualization of data. 
Data are coded as they are collected, and memos are written in conjunc­
tion with coding. Memoing opens up the data to reveal subtle patterns 
of social behavior. It is also very important not to hinder the emergence 
Perhaps You Are Wondering . . . 
Will I miss important data if I use field notes rather than tap­
ing and transcribing interviews? 
One novice researcher wrote, “I know that using field notes helps to 
delimit the data for analysis. My supervisor is worried, however, that 
this leads to important things being left out. Also, what determines 
what I remember to include in the field notes? 
We compared one field 
note that I had done with the transcript for that interview and found 
that some of the subtleties and nuances from the interview had been 
lost in the field note. So, should I rely solely on the field notes or start 
selectively coding sections of the interview transcripts to ­
complement 
them?”
This researcher is worried about missing something important by 
relying on field notes. It is important to remember that GT is the study 
of a concept, that is, a pattern of social behavior. Any social setting will 
contain many patterns, but the goal of GT is not to identify and explain 
every pattern in the data but, rather, just one pattern that appears to 
account for much of what is going on.  As such, worries about ensuring 
full and accurate coverage in data collection are inconsistent with GT 
methodology.  The full scope of social behavior in any substantive set­
ting is simply too complex to be fully captured in any one grounded 
theory study. Anything that is relevant to that pattern will surface in 
several incidents (i.e., interchangeable indicators), so there is really no 
need to worry about missing something in a transcript.
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l
DATA COLLECTION AND ANALYSIS AS AN ITERATIVE PROCESS


Chapter 5    Finding Your Data
73
of theory but rather to allow your creativity some freedom, while at the 
same time following classic GT’s essential guidelines.
Adhering to the simultaneous and sequential nature of data collec­
tion, coding, and memoing is fundamental to classic GT and will save 
you in so many ways! GT’s constant comparative analysis and theoreti­
cal sampling will save you time as you only need to collect the data 
necessary to discover and elaborate your theory. Doing so will save 
you from that overwhelming feeling that so many novice researchers 
experience when they have spent considerable time collecting moun­
tains of data “because it was there” and then have no idea where to 
start in making sense of what they have collected. Instead, collect your 
data one opportunity at a time and focus your efforts on coding and 
memoing as you go, building up your memo bank. Doing so will reveal 
the subtle patterns that begin to emerge from your data to explain what 
is really happening in your area of research interest. If you need quan­
titative data to help you make sense and see more clearly the emerging 
patterns, then you should not hesitate to collect these data as well.
Remember also that GT is a delayed action phenomenon (Glaser, 
1978, p. 34; 1998, p. 6). Frequently, several days after you have collected 
a slice of data, coded it, and written memos, new thoughts will arise in 
your mind that bring moments of clarity to what is emerging from your 
data. Glaser (1978) calls this preconscious processing (p. 23). At such 
moments, stop and capture your thoughts in another memo! You may 
want to explore these new insights by theoretically sampling; you may 
want to go back to the last interviewee and ask more questions about this 
issue, review earlier data to see if there are indicators of this new idea, or 
pursue it in your next interview. The goal is to develop more under­
standing of conceptual ideas as they emerge in your analysis.
IN SUMMARY
GT is based on a concept-indicator model of theory building. Concep­
tual indicators can be found in any and all types of data. The cardinal 
rule in GT is not to undermine the discovery of latent patterns in data 
by preconceiving what to look for or what type of data to use. The goal 
when initiating your research is to remain open to what emerges as a 
significant concern in the area under study and to then build an empir­
ically grounded theoretical explanation of how that main concern is 
handled. In GT, data are analyzed as they are collected. Additional 
data are theoretically sampled based on emerging conceptual ideas as 
captured in field notes and analytic memos.


CLASSIC GROUNDED THEORY IN PRACTICE
74
TEST YOUR KNOWLEDGE
In the following questions one or several answers may be correct.
1.	 While doing classic GT
a.	 You have to use only qualitative data
b.	 You have to use qualitative and quantitative data
c.	 You may use quantitative data
d.	 You may use any data
e.	 None of the above
2.	 In classic GT, primary data
a.	 Are the data that are the most important
b.	 Can only be used once
c.	 Are the data specifically collected for your research
d.	 Should be used first
e.	 None of the above
3.	 While conducting interviews in a classic GT stance
a.	 You must tape all interviews
b.	 You should never tape interviews
c.	 You can tape some or all interviews
d.	 None of the above
4.	 In classic GT
a.	 Detailed description is essential
b.	 Field notes are essential
c.	 Capturing incidents to identify patterns is essential
d.	 None of the above
FURTHER READING
Andrews, L., Higgins, A., Andrews, M. W., & Lalor, J. G. (2012). Classic 
grounded theory to analyze secondary data: Reality and reflections. 
Grounded Theory Review, 11(1), 12–26.
Glaser, B. G. (2001). The grounded theory perspective: Conceptualization contrasted 
with description (Chapters 11 & 12). Mill Valley, CA: Sociology Press.
Nathaniel, A. (2008). Eliciting spill: A methodological note. Grounded Theory 
Review, 7(1), 61–65.


Chapter 5    Finding Your Data
75
ENDNOTES
1.	 For an excellent discussion of “slices of data,” see Gibson and Hartman 
(2014), pages 125–131.
2.	 For a full elaboration of Glaser’s secondary analysis of data, see Glaser 
and Strauss (1967), Chapter 8.


76
6
Analyzing Your Data
After studying this chapter, you will:
• appreciate the distinction between descriptive analysis and conceptual 
analysis
• articulate how to employ constant comparative analysis
• distinguish between the various types and stages of coding used in GT
• appreciate the key role of analytic memoing in GT analysis
• articulate how to identify a core category
• appreciate the iterative nature of data analysis in GT
• evaluate the impact of software applications on GT analysis
I
n this chapter we focus on the techniques of data analysis as 
employed in classic GT. The conceptualization of data through cod­
ing and memoing is the foundation of GT analysis, the goal being the 
discovery of a latent pattern of social behavior that explains a main 
issue or concern within an area of research interest. This latent pattern 
analysis approach unravels the complexity in a social setting to reveal 
a main concern motivating predictable action. Such patterns of behav­
ior are often subtle and deeply embedded, requiring careful attention 
to what is revealed in the data. GT focuses on one issue—a main 
­
concern—and the pattern of behavior that explains how that concern is 
perpetually processed, managed, or resolved.


Chapter 6    Analyzing Your Data
77
This pattern—the core category—emerges as data are open coded 
and conceptualized. Once the pattern emerges, the analyst shifts atten­
tion to concentrate on theoretically sampling and selectively coding 
further data to elaborate, and eventually saturate, this core category 
and any concepts that appear to have some relationship to the core. As 
a pattern, the core is modeled via theoretical coding to organize and 
integrate the relationships between and among the core and related 
concepts. This theoretical coding is not selected but rather emerges 
through intensive hand sorting of analytic memos, aided by creative 
insights through the analyst’s preconscious processing of analytic ideas 
that have percolated outside conscious efforts to analyze the data—
what Glaser (1998) calls the drugless trip.
Incidents can be described as indicators of phenomena or experi­
ences as observed or articulated in the data. These are labeled and 
analyzed using the constant comparative method to generate initially 
substantive, and later theoretical, codes. The process begins with sub­
stantive coding through careful reading of the data to identify inci­
dents as indicators of concepts emergent within the data. As the 
theorist proceeds to compare incident to incident in the data, then 
incidents to emerging concepts, a main issue or concern surfaces across 
the coded data, and a category, which appears to account for most of 
the variation around this issue or concern, becomes the focus of the 
study. This category is referred to as the core category and becomes 
the focus of further selective data collection and coding efforts until the 
theorist has sufficiently elaborated and integrated the core category, its 
properties, and its theoretical connections to other relevant categories. 
At this point, the theorist has achieved theoretical saturation of the core 
concept and is ready to articulate the emergent theory.
Worth Noting: Study participants may not specifically articulate a main con­
cern in your collected data.  
The main concern is a latent pattern that they 
simply act on without consciously identifying.  
The grounded theorist sees the 
concern emerge as a latent pattern through systematic coding and memoing.
As Suddaby (2006) suggests, GT is not simply the routine applica­
tion of formulaic technique to data but has a clear creative component. 
This creativity is particularly evident in the memoing of conceptual 
ideas that parallels the coding process. Indeed, without this parallel 
processing, key conceptual realizations will be lost and the resulting 
analysis flat. It is this grab that offers such explanatory power.


CLASSIC GROUNDED THEORY IN PRACTICE
78
l
l
CONCEPTUAL ANALYSIS AS DISTINCT FROM
DESCRIPTIVE ANALYSIS
Barney Glaser has been known to comment that description runs the 
world, with conjecture a close second. His point is that while empiri­
cally descriptive and hypothesized research studies outnumber 
grounded theories, they often fail to offer high-impact explanatory 
variables in the same way that a good grounded theory can offer. As 
we have discussed in Chapter 4, description captures a moment in 
time, but the essentially linear nature of descriptive writing hinders the 
theorist’s ability to produce a complex yet parsimonious multivariate 
theory. Concepts remain buried amid detailed empirical accounts, and 
it is difficult if not impossible to see the simple elegance in the relation­
ships between concepts that together integrate a grounded theory and 
provide its explanatory power. GT provides a systematic way to con­
ceptualize and integrate a theory to achieve that full explanatory 
power.
As discussed in Chapter 3, constant comparative analysis is a 
foundational pillar of classic GT. After first discussing here the 
various types of coding in GT, we explore constant comparative 
analysis, memoing, and core category emergence and development. 
Next we discuss some cognitive attributes of the classic GT approach 
that enhance theorizing. While we touch on theoretical sampling 
and theoretical coding in this chapter, we reserve their full discussion 
for Chapter 7, as both are key techniques in shaping a grounded 
theory.
l
l
USING CONSTANT COMPARATIVE ANALYSIS
Conceptualization is fundamental to GT for “without the abstraction 
from time, place and people, there can be no multivariate, integrated 
theory based on conceptual, hypothetical relationships” (Glaser, 2002, 

p. 261). Coding and memoing are the key heuristic techniques in constant 
comparative analysis. While initial attempts at data analysis may very 
well be more empirically descriptive than conceptual, the classic grounded 
theorist moves past description to fracturing the data for their conceptual 
essence through constant comparative analysis. The purpose of constant 
comparison is to see if the data support and continue to support emerg­
ing concepts. Moving through the data to see incidents over and over, 
constantly comparing and conceptualizing, forces the theorist into


Chapter 6    Analyzing Your Data
79
confronting similarities, differences and degrees of consis­
tency of meaning between indicators which generates an 
underlying uniformity which in turn results in a coded cate­
gory and the beginnings of properties of it. From the com­
parisons of further indicators to the conceptual codes, the 
[conceptual] code is sharpened to achieve its best fit while 
further properties are generated until the [concept] is verified 
and saturated. (Glaser, 1978, p. 62)
By moving through the data to see conceptual indicators repeated 
in incident after incident and constantly comparing these, concepts are 
developed and their properties and dimensions elaborated.
Concept validity is established through the interchangeability of 
indicators in multiple incidents. This means that although incidents are 
different, they indicate the same concepts. As such, GT’s constant com­
parative analysis is a gradual building up of conceptual codes into 
concepts and then concepts into categories.2 Doing so requires the theo­
rist to grapple with both chaos and control. The chaos is in tolerating 
the uncertainty of not knowing in advance but remaining open to what 
emerges through the diligent application of constant comparison for 
interchangeability of indicators and theoretical sampling for core emer­
gence and saturation. At the same time, constant comparison helps 
alleviate the “data overwhelm” (Glaser, 2003, p. 24) that results from 
simply collecting data in a linear fashion without taking the time for 
analysis and shaping further data collection through theoretical sam­
pling. Used in conjunction with theoretical sampling, constant com­
parison generates theory that is “integrated, consistent, plausible and 
close to the data” while also “allow[ing], with discipline, for some of 
the vagueness and flexibility that aid the creative generation of theory” 
(Glaser & Strauss, 1967, p. 103).
The constant comparative approach begins with the first data col­
lected and continues throughout the analytic process through to theo­
retical saturation and integration, illustrating the nonlinear, spiraling 
nature of GT with the boundaries between the various steps not clearly 
delineated. Glaser (1998) describes the GT process as “subsequent, 
sequential, simultaneous, serendipitous and scheduled” (p. 15). The 
process begins with the analyst first coding and comparing incidents in 
the data—first, incident to incident; later, incidents to concepts (i.e., 
emergent concepts from earlier comparisons) whereby concepts and 
the properties and dimensions3 of those concepts are elaborated and 
eventually saturated (i.e., no new properties or dimensions are emerg­
ing from additional data); and then, concept to concept to establish 


CLASSIC GROUNDED THEORY IN PRACTICE
80
conceptual levels between relevant concepts in relation to the core 
category.
Glaser uses the phrase interchangeability of indicators to describe 
the process of comparative analysis of conceptual indicators in data. 
The phrase comes from his background in quantitative analysis and the 
work of Paul Lazarsfeld in index formation:
Lazarsfeld used the traditional psychological index formation 
approach. He would take questions that bore on the same topic 
and sum up their aggregate values for a group. Then he cuts 
the range of values in half or thirds to construct an index with 
various values. For example several indicators on degree of 
recognition produced an index of high, medium and low rec­
ognition. Then every person in the sample got a value. What 
was not done was to compare with its meaning each indicator 
to each indicator . . . When the comparison is made . . . a cate­
gory is generated (an index with meaning). To continually 
compare each incident to incidents and categories generates 
meaningful properties of categories. This constant comparison 
is a far richer yield of concepts and relationships . . . than is the 
yield from just summing. (Glaser, 1998, pp. 23–24)
Constant comparison requires concepts to earn their way into a 
grounded theory through this interchangeability of indicators. This 
earned relevance through emergence is a fundamental premise of GT 
and is grounded in the coding of data. (See Appendix B for an illustra­
tion of the constant comparative method.)
l
l
TYPES AND STAGES OF CODING IN GROUNDED THEORY
All coding in GT is in aid of conceptualization. There are, however, two 
distinct types of coding: substantive and theoretical. By substantive 
coding we are referring to the process of conceptualizing the empirical 
substance of our data. Theoretical coding refers to the integration of 
our emergent concepts as a model that explains the pattern of relation­
ships between the core category and related concepts. Substantive cod­
ing begins with the first data collected, whereas theoretical coding is 
reserved for the later stage of the GT process once a core category has 
emerged and been fully developed (saturated) and any concepts that 
have a relationship to the core category have been fully saturated. At 
this stage, it is time to explore the modeling of those relationships as a 
fully integrated grounded theory.


Chapter 6    Analyzing Your Data
81
Open Coding
The substantive coding process begins with open coding of empir­
ical data. Open coding means to identify incidents in the data that 
appear to indicate one or more concepts and labeling these using one 
or two salient words per code—preferably verbs to capture the action 
in concept(s) indicated. Glaser (1978) refers to two types of substantive 
codes: in vivo and analytic. He describes in vivo codes as capturing 
exactly what is going on in the incident (e.g., remaining composed) 
and analytic codes as explaining theoretically what is happening (e.g., 
identity maintenance). As such, in vivo codes are a first effort at 
­
concept identification, and analytic codes raise the level of conceptual 
abstraction.
Descriptive labeling of incidents is fairly straightforward, but it can 
be a challenge for some to move from the descriptive to the conceptual 
level. In close reading of the data, concepts are identified and eventually 
saturated. Close reading also ensures that no important concepts are 
overlooked and that concepts earn their relevance rather than being 
accorded an assumed importance based on extant theory. It is important 
here to avoid numerous one incident codes. Constant comparison of inci­
dents, noting similarities and differences among incidents and looking 
for patterns emerging in the coded data, will help to reduce the potential 
for drowning in one incident codes. The more you code, the more you 
begin to see the same conceptual indicators (codes) across your data—a 
pattern emerges, raising the conceptual level of your coding.
A common source of confusion for novice grounded theorists, 
especially those familiar with qualitative methods, arises with Glaser’s 
(1978) reference to line by line coding (p. 57). Many novice researchers—
particularly PhD students—are required to tape and transcribe 
interviews as a demonstration of research skill development and 
possibly as a condition of graduate studies or research ethics approval. 
However, in most instances, coding this type of data may not lead the 
researcher beyond description due to the volume of data produced in 
transcripts. However, Glaser (1978) is not referring to the line-by-line 
coding of interview transcripts but rather to the coding of field notes—
abbreviated notations captured during periods of field observation, 
interviews, conversations, focus groups, and so on. Field notes help the 
researcher to begin the conceptualization process while in the field as 
she listens for the main concern and notes indicators of how that 
concern is processed or resolved. Quick notes can then be written up in 
more detail and then coded and elaborated through memoing. (See 
Figure 6.1 for a sample of coded field notes.) Coding of field notes, in 
comparison to lengthy transcripts, helps the analyst to stay focused on 


CLASSIC GROUNDED THEORY IN PRACTICE
82
what is really happening in the substantive area—what appears to be 
the main concern— while the absence of detailed conversational or 
descriptive passages facilitates coding and memoing on a higher 
conceptual level.
Figure 6.1    
Coded Field Notes
It goes without saying that a grounded theorist must code her own 
data. Coding unlocks the conceptualization of data and initiates theory 
generation. A fundamental criterion for coding is the suspension of any 
preconceived notions about what the data should reveal. Accordingly, 
a grounded theorist doesn’t employ a predefined set of codes assumed 
to be relevant. As Glaser (1992) suggests, “the analyst should just not 
know as he approaches the data” (p. 50). Instead, the goal is to fracture 
the data conceptually by posing questions such as the following:
What are these data a study of?
What category does this incident indicate?
What is actually happening in the data?
What is the issue facing the participants?
What is their main concern, and how do they manage or resolve it?


Chapter 6    Analyzing Your Data
83
Asking these questions of the data as you code incident by incident 
facilitates the generation of a core category (Glaser, 1978, p. 57).
Labeling incidents with conceptual codes and, later, with categories 
“help[s] the theorist to build an analysis of the data rather than remain at 
the level of ethnographic description” (Charmaz, 1990, p.  1167). Glaser 
(1992) reminds us that in open coding we must guard against “slipping 
into preconception instead of listening carefully to each incident in order 
to figure out what the research is truly a study of” (p. 45). Open coding 
incident by incident in the data with the list of previously presented ques­
tions keeps the analyst close to the data rather than lapsing into ungrounded 
theorizing and corrects any forcing of “pet” themes on the data by requir­
ing “the analyst to verify and saturate categories, and minimiz[ing] miss­
ing an important category, produc[ing] a dense, rich theory, and giv[ing] a 
feeling that nothing has been left out” (Glaser, 1978, p. 58).
Keep moving through the data rather than pondering too long on 
any one incident. Doing so will facilitate your seeing emerging patterns 
in your coding. As concepts begin to take shape, additional constant 
comparisons help to clarify the logic in emerging concepts and their 
properties as well as reducing the emerging theory through a smaller 
set of higher-level interrelated concepts. Given this constant, emergent 
refinement of concepts and categories, it is important not to fret about 
the specific labels at the open coding stage. Use labels that help to cap­
ture the idea while keeping in mind that there will be time later in the 
process to refine the labels of your concepts and categories.
Glaser on Data Collecting
Glaser (1978) suggests that staying too long “in the field” collecting data 
is a common problem. He recommends no more than half a day at a time 
in data collection. He also reminds us that while open coding can gener­
ate many codes very quickly, it is important to interrupt coding to memo 
ideas. Coding and memoing as data are collected is important because 
this is when ideas about the data are freshest in your mind. Doing so also 
facilitates theoretical sampling by allowing you to see where you need 
to go next to elaborate on ideas that emerge through your coding and 
memoing.
Selective Coding
In selective coding, the analyst delimits coding to only those 
concepts (i.e., variables) that relate to the core category in sufficiently 


CLASSIC GROUNDED THEORY IN PRACTICE
84
significant ways to produce a parsimonious theory. If a concept, 
regardless of its novelty or the personal preference of the analyst, 
does not have relevance in relation to the core category, it is dropped 
from subsequent analysis and theoretical elaboration. In this way, 
the core category becomes a guide to further data collection and 
theoretical sampling. During the initial open coding, the analyst will 
have coded many incidents with many of these being coded for more 
than one concept. Once a potential core category is selected, the 
coding technique shifts from open to selective. Because not all data 
pertain to the core category (or related concepts), it is necessary to 
“delimit” coding to just that which pertains to the core and related 
concepts.
Initiating Selective Coding
Delimiting occurs at two levels. First, as the theory integrates, it solidifies 
with fewer modifications needed as the researcher compares the next inci­
dents of a category to its properties. Later modifications are mainly about 
clarifying the logic of the theory and integrating elaborating details of prop­
erties into the major outline of interrelated categories. As the researcher 
begins to discover an underlying uniformity in the categories and proper­
ties, the theory is reformulated with a smaller set of higher level concepts. 
This second level of delimiting the theory reduces the original list of cat­
egories for coding.
As the theory develops, becomes reduced, and increasingly works bet­
ter in ordering a mass of data, the researcher becomes committed to it. 
This allows for a delimiting of the original list of categories for subse­
quent collecting and selective coding of additional data, according to the 
newly established boundaries of the theory. By delimiting the focus to 
one category as the core variable, only those categories related to that 
core are now included in the theory. This list of categories, now delim­
ited for additional selective coding, is subsequently (and continuously) 
delimited through theoretical saturation of each category. (Holton, 2007a, 
pp. 280–281)
Any incidents that have been coded for several possible 
concepts are reviewed and their relevance and relationship to the 
core category affirmed in memos. As concepts are saturated 


Chapter 6    Analyzing Your Data
85
(i.e., ­
additional data fail to yield any new properties or dimensions 
of the concept), additional data collection and coding are further 
delimited as the fully saturated concepts are set aside while the 
analyst continues to theoretically sample and saturate the remaining 
concepts. Delimiting speeds up the analysis by focusing theoretical 
sampling and constant comparison on just the core category and 
related concepts and reduces the potential for the analyst to be 
overwhelmed with excessive caches of data that bear no relevance 
to the emerging theory.
Another Aspect of Delimiting
In studies with a heterogeneous sample (e.g., senior managers, mid­
dle managers, and frontline staff), in which each segment of the sample 
appears to have a different main concern, the analyst should consider 
delimiting the study to focus on one subset of the sample at a time. 
Subsequent stages of analysis can focus on the other subsets with the 
potential for different core categories emerging from each subset ana­
lyzed. A good example is Sandgren (2010). The author undertook three 
separate GT studies in the same substantive area (palliative cancer 
care): (1) acute care nurses’ striving for emotional survival, (2) home care 
nurses’ doing good care, and (3) patients’ and relatives’ living on hold. Sand­
gren then used data from the three grounded theories to generate an 
overarching grounded theory of deciphering unwritten rules. These rules 
explain how patients, relatives, and nurses deal with the uncertainty of 
how to act and behave within the sensitivities associated with a palliative 
care setting.
When doing open and selective coding, researchers sometimes feel 
the need to collect quantitative data. They have done some open cod­
ing of collected qualitative data and have identified the core category 
and some related categories through further selective coding. How­
ever, some categories are not saturated, and some data are double 
coded. To help produce parsimonious results and fully saturated cate­
gories, quantitative data may be collected. A questionnaire may be 
developed with items grounded in the qualitative data previously 
collected. Exploratory factor analysis of resulting quantitative 

data may then help the researcher to achieve parsimony and reach 


CLASSIC GROUNDED THEORY IN PRACTICE
86
theoretical saturation of the various categories previously highlighted 
with qualitative data. The process of selective coding is sometimes 
limited by the researcher’s interpretation of the data and capacity to 
synthesize parsimoniously all the data obtained. In some instances, 
this drawback may be considerably lessened by using quantitative data 
and techniques.
Mixed Methods Example
The theory of information technology (IT) culture creep (Walsh et al., 
2010) was grounded in qualitative data. Using qualitative data in a 
Straussian approach to GT helped provide rich descriptions of IT user 
ideal types and the 18 possible dimensions of the explanatory second-
order IT culture construct. However, some of the 18 dimensions 
we had qualitatively identified for the IT culture construct appeared 
redundant, as many items from our qualitative data were double-coded 
within two subcategories. With our qualitative data, we had not been 
able to eliminate the redundant dimensions among the 18 identified 
qualitatively. We therefore decided to collect quantitative data and use 
quantitative techniques to help us solve this issue (Walsh & Gettler-
Summa, 2010). With a diversified population, we thus investigated 
further our core category, the users’ IT culture, while linking it to user 
ideal types and eliminated correlated redundant dimensions.  At the 
same time, we aimed to assess the relative importance of each first-
order construct (the IT culture dimensions) used to describe the ideal 
types. To sum up, mixing data and methods helped us conceptualize 
with parsimony; it allowed us to densify the IT culture concept through 
dimension reduction (from 18 to 8).
Theoretical Coding
As discussed earlier, theoretical coding refers to the modeling of 
the relationships between and among the core category and related 
concepts as a fully integrated theory. It is the final stage in the coding 
process and is largely responsible for the overall “shaping” of the the­
ory. We turn to a more extensive discussion of theoretical coding in 
Chapter 7.
The different types of coding in classic GT, which we described 
earlier, are summarized in Table 6.1.


Chapter 6    Analyzing Your Data
87
	
CORE CATEGORY EMERGENCE	
l
Types of Coding
Description
Purpose
Substantive
Open
In vivo codes: 
Capturing what is 
going on
Analytic codes: 
Conceptualizing what 
is happening
To identify incidents in 
the data that appear to 
indicate one or more 
concepts
Selective
Coding around the 
core category
To identify properties 
and dimensions of the 
core category
Theoretical
Coding to model the 
relationships between 
and among the core 
category and related 
concepts
To shape and integrate 
the theory
Table 6.1    
Coding in Classic GT
Data collection in GT focuses on a general area of interest. Then, as 
coding and memoing proceed using the questions listed earlier, the 
analyst begins to discern a latent pattern that focuses attention on a 
main issue or concern in the area under study. As the analyst memos 
ideas around this main issue, a core category emerges that seems to 
explain the most variation in the social behavior around the central 
issue or concern and how the issue is processed or addressed. This core 
category will relate to several other concepts, providing a focus for 
theoretical integration through elaboration of the relationships between 
and among the emergent core and related concepts. Indeed, this theo­
retical integration is the “prime function” (Glaser, 1978, p. 93) of the 
One Student’s Experience
“When I let go of what I wanted the problem to be, I actually found 
some of the answers to what the problem really was, and I actually found 
something much bigger—a theory of who, how, and why.”


CLASSIC GROUNDED THEORY IN PRACTICE
88
core category, ensuring that the resultant theory is both conceptually 
dense and theoretically saturated through the increasing development 
of conceptual interrelationships that organize the theory. Without a 
dynamic core variable that cuts across empirical units, theoretical link­
ages may not be seen and the theorist may opt to simply link up largely 
descriptive comparisons (Bigus et al., 1994).
Differentiating Between a Main Concern and a Core Category
Novice grounded theorists will often confuse the main concern with the 
core category. The distinction is that the main concern highlights the issue 
or problem that occupies much of the action and attention in the research 
setting, whereas the core category explains how that concern or problem is 
managed, processed, or resolved. As such, the main concern and the core 
category are tightly coupled but distinct conceptualizations. As one exam­
ple, in Holton (2006, 2007b) the main concern of knowledge professionals 
was coping with persistent and unpredictable change in their workplaces. 
The core category that emerged was rehumanizing, a three-stage process, 
whereby knowledge professionals engaged in fluctuating support networks 
that served as informal psychic softeners in countering dehumanizing 
workplace climates.
As another example, in Walsh, Kefi, and Baskerville (2010), the main 
managerial concern was dealing with different types of IT usage within 
firms, and the core category that emerged was the IT acculturation pro­
cess investigated through IT culture ideal types.
A core category is tentatively identified, often fairly early in the 
open coding stage, following which the analyst considers which other 
coded concepts appear to have some relationship to the core category. 
The core category, as a theoretical code, can be organized or modeled 
in numerous ways. It is often a process with two or more stages (e.g., 
“status passage”; Glaser & Strauss, 1971), but it can also be a typology 
(e.g., “awareness contexts”; Glaser & Strauss, 1965a; Walsh et al., 
2010), a continuum (Giske & Gjengedal, 2007), strategies (Sandgren et 
al., 2006), a cycle (Higgins, Barker, & Begley, 2008), or dimensions 
(Pergert, 2008).
Glaser (1978) and Glaser and Holton (2004) offer criteria for select­
ing and confirming a core category: centrality (i.e., it appears central to 
the main concern), frequency (i.e., it reoccurs frequently in the data and 
can be seen as a stable pattern), relevance (i.e., it relates meaningfully 


Chapter 6    Analyzing Your Data
89
and easily with other categories), grab (i.e., imagery and explanatory 
power with general implications4 beyond the substantive area), and 
variability (i.e., although conditions vary, the essential meaning 
remains constant; see Figure 6.2).
Core
Category
Main
Concern
Centrality
Frequency
Relevance
Grab
Variability
Significantly
explains 
Figure 6.2    Core Category and Main Concern
As previously discussed, once a core category has emerged, the 
analyst considers other concepts that have patterned out in open cod­
ing to determine their relevance in relation to the core category. Further 
data collection, coding, and memoing are delimited to theoretical sam­
pling for additional conceptual indicators of the core and these related 
concepts, moving the analyst in the selective coding stage. Given its 
centrality and significance for ensuring full explanatory power, it does 
take longer to saturate the core category than related concepts.
One Student’s Experience
“I think I’ve got it . . . one word that has come from my analysis of the 
data . . . it fits with so much data that I am amazed. It accounts for dif­
ferences in emotions in a wide variety of situations; attitudes towards 
a wide variety of shopping types and explains very diverse shopping 
behaviors. And it makes sense. It did not come from me; it came from 
the data.”
	
THE KEY ROLE OF ANALYTIC MEMOING IN GT ANALYSIS	
l
Memoing is the core ideational processing of theoretical ideas as they 
emerge through coding and constant comparative analysis. Memos are 
the researcher’s theoretical notes about the data and the conceptual 


CLASSIC GROUNDED THEORY IN PRACTICE
90
connections between categories. Memo writing is a continual process 
that helps to raise the data to a conceptual level and develop the prop­
erties of each category. Memos present hypotheses about connections 
between categories and/or their properties/dimensions and begin the 
integration of these connections with clusters of other categories to 
generate a theory. The constant comparative reasoning captured in 
memos debunks preconceived notions about what the data should 
reveal (Glaser, 1978). As Glaser (1978) suggests, the goals in memoing 
are to stimulate and capture conceptual ideas that emerge from coding 
and constant comparative analysis, to do so with complete freedom 
from the usual constraints of proper writing (i.e., grammar, syntax), 
and to build up a rich fund of captured ideas in a highly sortable for­
mat that facilitates theoretical integration of a rich, dense yet parsimo­
nious theory.
Memoing your ideas—AS YOU CODE—is essential to transcend­
ing descriptive analysis. Glaser (1998) notes that “falling out” 
(p. 177) at the coding stage is the downfall for many who wish to do 
GT. Without theoretical sensitivity and persistence in using memos 
to capture ideas as they emerge, such work defaults to conceptual 
description where concepts and their properties are simply identi­
fied and described without any integrative theoretical explanation. 
As Klag and Langley (2013) suggest, “it is often in the very act of 
writing that productive conceptual ideas develop and crystallize” 
(p. 161).
One Student’s Experience
“I read the transcripts line-by-line, asking all the time ‘What is the par­
ticipant trying to do?’ and making notes on the margins. Curiously these 
notes were less important than the memos that this exercise generated. 
It was as if writing memos allowed me to better understand what was 
going on—to gradually increase my knowledge of the situation. To see 
what the participants were trying to do and how they did it. After some 
time I had so many memos that I was curious to see the whole picture. 
It takes time but if you are constantly analyzing the data and looking 
carefully at it, there’s no way you can escape learning more and more 
about the situation. And the memos are the tool that will allow you to 
put everything together so you don’t have to worry about keeping it all 
in your mind all the time.”


Chapter 6    Analyzing Your Data
91
Locke (2007) suggests that capturing theoretical ideas in GT neces­
sitates dual modes of thinking whereby we acknowledge the tension 
between the irrational free play of ideas as our minds wander and 
explore and the rational control required in diligently pursuing those 
ideas in a systematic way: “Thus, staying firmly and single-mindedly 
in touch with what it is we are trying to understand at the same time 
as we open and loosen up our thinking to roam freely defines thinking 
in these dual modes” (p. 570).
Fine and Deegan (1996) describe serendipity as a unique and contin­
gent mix of insight and chance, whereas Klag and Langley (2013) con­
trast serendipity with deliberate application of heuristics to stimulate 
variety in theorizing, suggesting that both play an important role in 
facilitating conceptual leaps. While acknowledging that GT entails 
both the serendipitous and the systematic, Glaser does not view logic 
and intuition as diametrically opposed but rather as twin foundations 
for theory generation. The theorizing process in GT operates at both a 
conscious and a preconscious level to process and organize empirically 
grounded, analytically induced concepts.
Skill in memoing, and therefore the conceptual depth of the 
memos, is part of the experiential learning curve in GT skill develop­
ment. Memos are works in progress, intended to capture ideas as they 
emerge without the worries of writing style, grammar, and spelling 
that can cause some writers to freeze their thinking. Many memos writ­
ten early in the constant comparison process may never find their way 
into the final theory because they may not relate to the core category; 
thus, to spend time perfecting these memos is to waste research time. 
Time is better spent in letting the ideas flow and capturing them as 
they emerge. There will be time later in the writing process to polish 
them.
Early in the GT process, writing several short memos on each cat­
egory facilitates theoretical sorting of ideas from which more substan­
tial memos will be written as the process moves toward conceptual 
elaboration and, later, integration. (See Appendix B for an illustration 
of the maturation of early shorter memos into more substantial memos 
as conceptual ideas develop.) Glaser (2014) advocates “free style” 
memoing:
Hans Thulesius, a well-known grounded theory researcher 
and teacher, confirms my dictum of free style memoing. Hans 
says “Memoing is very important in GT research, but you can 
memo in whatever way you like. Hand writing, typing or 
drawing diagrams but keep them private. When you have a 


CLASSIC GROUNDED THEORY IN PRACTICE
92
big stack of memos you can begin hand sorting them, placing 
them in smaller piles on a big table. Then writing more memos 
are triggered by the sorted memos which when you reach satu­
ration will eventually lead to a working paper.” Hans contin­
ues about free style memoing: “Every person finds his or her 
own way of writing and organizing one’s memos. The most 
important thing about memos is that you get out your ideas 
onto paper. Glaser warns against rules for memos since they 
can stifle the creative memo writing. Memos should be written 
at any time to capture the ideas that come when you compare 
and code data. And stay open to whatever emerges and memo 
it at all stages of doing GT” (p. 26).
Preconscious processing means that ideas can come to mind at 
any time. Glaser (2014) urges the GT theorist to write continually, 
memoing thoughts and ideas as analytic bits. Charmaz (2014) describes 
writing and rewriting as crucial phases of the analytic process. Hav­
ing confidence in your memoing is critical, especially early in the GT 
process when it is unclear as yet where theorizing may be headed. 
Grounded theorists learn to trust in whatever ideas occur as they are 
reading through their data and trying to find patterns to explain 
social behaviors in the data. The delayed action nature of GT requires 
continuous memoing of ideas, comparing them to earlier memos to 
continually build a bank of memos. Just as with a bank, your invest­
ment grows as you continue to deposit and build your account, prom­
ising a profitable return. As Breckenridge (2013) describes,
As my memo bank grew, my confidence increased and my 
pace quickened as I became less worried about getting the 
coding right and more excited by the number of possibilities 
emerging in the data. The more I analyzed, the longer my 
memos became as I was able to see more theoretical connec­
tions and identify further questions for theoretical sam­
pling. This in turn slowed my pace, as I moved back and 
forth between coding, constant comparison and memo writ­
ing. At times I found the systematic approach to coding and 
constant comparison rather tedious. Thankfully, however, 
moments of tedium were only ever temporary and fre­
quently gave way to excitement as new concepts emerged, 
reigniting my motivation as memos became more full and 
vibrant with new ideas and connections. (p. 9)


Chapter 6    Analyzing Your Data
93
Some Advice for Productive Memoing
While emphasizing that memoing is a highly personal process, Glaser 
(1978) does offer a number of rules for memoing, including the following:
	
1.	 
Keep data and memos separate. Reference memos to field 
notes from where they emerge. Use incidents as illustrations 
of only one code (as much as possible).
	
2.	 Always interrupt coding to memo whenever an idea arises.
	
3.	 
Bring on memoing by starting to write on a code—such writing 
will open up the output stage of creativity.
	
4.	 Modify memos as growth and realizations occur.
	
5.	 Keep a list of emergent codes at hand.
	
6.	 
If too many memos on different codes seem the same, com­
pare codes or their dimensions for differences that are being 
missed between the codes. If they are still the same, collapse 
the two into one code.
	
7.	 
Problematic digressions should be followed through on a con­
ceptual, not logical, elaboration basis for the purpose of theo­
retical sampling for indicating an area for future research. In 
such cases, memos should make clear distinctions between 
data and conjecture.
	
8.	 
Run memos open as long as possible to enable development of 
rich diversity.
	
9.	 
When writing memos, talk conceptually about the substantive 
codes as they are theoretically coded (do not talk about peo­
ple), and write about the conceptually generated patterns that 
people engage in (and not about the people per se).
	
10.	 
If analyst has two burning ideas, he should write them up one 
at a time to keep them straight and avoid confusion.
	
11.	 
Indicate in memos “saturation” when analyst thinks he has sat­
urated a category.
	
12.	 
No matter how well memoing techniques are working, always 
be flexible with them.
A common concern expressed by novice researchers is how to illus­
trate (i.e., verify) the empirical grounding of their concepts and catego­
ries, as would be expected in a qualitative study. One way to achieve 
this is by creating an audit trail that links memos to the data from 
which they were generated. Then, using a simple labeling scheme, 


CLASSIC GROUNDED THEORY IN PRACTICE
94
footnotes can be used to record the connections (from data to memos) 
in the final memo sorting and writing up of the theory.
An Example of Labeling to Create an Audit Trail
A memo labeled “Memo AU 0904-4” (indicating that it is one of four 
memos written on August 9, 2004) and including references to data from 
interviews collected on four occasions (October 23, 2001; September 10, 
2003; November 12, 2001; and November 26, 2001) is footnoted in the 
final write-up of the theory as “Field Interview O 2301-2, S 1003, N 1201, 
N 2601; Memo AU 0904-4.”
While such evidentiary detail is not relevant or required as part of 
the GT process, it may be important—and expected—to satisfy thesis 
advisers or journal reviewers accustomed to full data displays. Initiat­
ing a similar labeling scheme for all data and memos at the outset of a 
study offers the analyst an opportunity to keep the data-memo connec­
tions clear as the study progresses as well as offering an audit trail 
option for later thesis defense or publication.
l
l
THE ITERATIVE NATURE OF DATA ANALYSIS IN 
GROUNDED THEORY
Earlier in this chapter we make reference to Glaser’s (1998) description 
of GT as “subsequent, sequential, simultaneous, serendipitous and 
scheduled” (p. 15). Glaser’s description is an apt one, and our chal­
lenge in writing this chapter has been to attempt to relate linearly a 
process that is anything but linear! Glaser (1978) describes the impor­
tance of personal pacing, creativity, and cycling in doing a GT study. 
The iterative nature of GT, coupling a rigorous and systematic analytic 
approach with an open and flexible embrace of creative insights 
through preconscious processing—wherever and whenever they 
emerge—requires the grounded theorist to be comfortable with both 
chaos and control:
The chaos is in tolerating the uncertainty and subsequent regres­
sion of not knowing in advance and of remaining open to what 
emerges through the diligent, controlled, often tedious applica­
tion of the method’s synchronous and iterative processes of 
­
line-by-line coding, constant comparison for interchangeability 


Chapter 6    Analyzing Your Data
95
of indicators, and theoretical sampling for core emergence and 
theoretical saturation. This discipline is simultaneously comple­
mented by requiring the theorist to remain open to the innate 
creativity in preconscious processing of conceptual ideation and 
theoretical integration. (Holton, 2007a, p. 273)
Fortunately, GT has built-in motivational phases that carry the theorist 
onward with energy and the excitement of discovery. Eureka moments 
(Glaser, 1998) that emerge from preconscious processing of a study in 
progress are an important motivator. As Breckenridge (2013) describes,
Theoretical realizations sometimes take a while to emerge. Glaser 
has called this preconscious processing. Although I was not con­
vinced about this idea at first, reflecting on my own experience, 
I can remember clearly those occasions where I had given up only 
to find that clarity of thought emerged when I least expected it: as 
I was cooking, out walking, on the train and even waking in the 
middle of the night to scribble down memos in the dark. (p. 11)
Such experiences are commonly shared among grounded theorists! 
Yet, those new to GT often seek specific procedures or templates for 
analyzing their data. There is no one correct way to code and memo. The 
choice is a personal one that will evolve as your GT skills develop. Some 
experienced grounded theorists have shared their approaches (Brecken­
ridge, 2013; Holton, 2007a; Scott, 2009). You may find these interesting 
and possibly helpful in developing your own approach. The best advice 
is to keep it simple but organized and do whatever you can to stimulate 
hand-eye-brain cognitive and preconscious processing through manual 
coding, memoing, and sorting procedures. And finally, the grounded 
theorist must be able to trust in the process every step along the way 
and to trust that following the GT methodology will yield a theory that 
is rich in explanatory power and relevant to the area under study.
	
TO SOFTWARE OR NOT TO SOFTWARE	
l
Computer-assisted data analysis is now commonplace in the research 
world and novice grounded theorists are often tempted to adopt soft­
ware for data analysis. Bringer, Johnston, and Brackenridge (2004) sug­
gested that QSR*NVIVO is compatible with doing GT; however, in doing 
so, they conflate GT with qualitative research methodology for which 
detailed transcription of interview data is the norm. While software pro­
grams are excellent for searching and sorting through such data, they 


CLASSIC GROUNDED THEORY IN PRACTICE
96
tend to do so in a structured linear and hierarchical manner that is more 
appropriate to what Glaser would refer to as the full coverage nature of 
qualitative data analysis. GT quickly leaves the data behind through 
constant comparison to conceptualization, a process that relies on lateral 
thinking (de Bono, 1968) through the conceptual sorting of ideas and 
creative realizations through preconscious processing.
GT is about the sorting of theoretical ideas captured in memos. The 
danger in using qualitative software applications is that the analyst 
slips into mechanically coding large volumes of data and sorting codes 
without engaging systematically in constantly comparing incidents 
within the data and memoing conceptual ideas that, over time, surface 
the subtleties in realizations and relationships between and among 
incidents and emergent concepts. As Breckenridge (2013) explains,
It was easy to code the data—too easy perhaps—and I quickly 
amassed a long list of codes with no idea how they might be con­
nected to one another. Sitting at the computer “dragging and 
dropping” sections of data became automatic and unthinking. 
I was simply organizing my data into a fractured list, without 
comparing incident to incident or stopping to write memos. 
(pp. 8–9)
The result of such mindless coding may, at best, lead to a drift into 
conceptual description without the capacity to generate a conceptually 
dense and fully integrated grounded theory.
A further danger to employing software for data analysis is that the 
analyst may be lured into indiscriminate data collection, collecting 
more data than is necessary simply because it is there. Practically 
speaking, employing qualitative software entails a significant learning 
curve; it is not only time consuming but holds the potential for stifling 
analytic creativity. Both authors of this book, who have been trained 
very differently, find NVIVO software useful for archiving coded quali­
tative data and for linking memos to incidents in the data and linking 
memos as they cumulatively build ideas. This archiving capacity is 
particularly useful in later writing up for publication.
IN SUMMARY
When doing classic GT, you have to go beyond descriptive analysis and 
reach conceptual analysis. All data—whether qualitative, quantitative, 


Chapter 6    Analyzing Your Data
97
or both—must be constantly compared and analyzed as they are col­
lected through iterative cycles and memoing effected alongside this 
process, as ideas emerge. To help analysis, various steps in coding of 
data are applied: Open coding of all data is done until core category 
emergence, then selective coding around the core category, and, finally, 
theoretical coding to highlight causal relationships between concepts. 
Memoing plays a key role in the analytic process. Memoing begins as 
soon as the first data are coded and continues throughout the process to 
the final write-up of the theory. Despite its popularity, grounded theo­
rists are cautious in their use of analytic software, reserving its use for 
archiving (and later retrieval) of manually coded and sorted data.
TEST YOUR KNOWLEDGE
In the following questions one or several answers may be correct.
1.	 In classic GT
a.	 You first have to collect all data and then analyze them
b.	 You analyze your data as you collect them
c.	 You must decide which data to collect next before you start 
analyzing collected data
d.	 You should constantly compare all collected data
e.	 None of the above
	
2.	 In classic GT, coding is
a.	 Open, axial, and selective
b.	 Open, selective, and theoretical
c.	 Substantive and theoretical
d.	 None of the above
	
3.	 The core category
a.	 Keeps recurring in the data
b.	 Has grab
c.	 Emerges while you are open coding
d.	 Must emerge before you start selectively coding
e.	 None of the above
	
4.	 While doing classic GT with qualitative data,
a.	 You must code every word
b.	 You must code every sentence
c.	 You must use software to help you code
d.	 None of the above


CLASSIC GROUNDED THEORY IN PRACTICE
98
FURTHER READING
Glaser, B. G. (1965). The constant comparative method of qualitative analysis. 
Social Problems, 12, 436–445.
Glaser, B. G. (1998). Generating concepts. In Doing grounded theory: Issues and 
discussions (Chapter 9). Mill Valley, CA: Sociology Press.
Glaser, B. G. (2001). Constant comparison and core confusion. In The grounded 
theory perspective: Conceptualization contrasted with description (Chapter 13). 
Mill Valley, CA: Sociology Press.
Glaser, B. G. (2014). Memoing: A vital grounded theory procedure. Mill Valley, CA: 
Sociology Press.
Holton, J. A. (2007a). The coding process and its challenges. In A. Bryant & 
C. Charmaz (Eds.), The SAGE handbook of grounded theory (pp. 265–289). 
London: Sage.
ENDNOTES
1.	 The 
Creative 
Commons-Attribution-NonCommercial-ShareAlike 
License 4.0 International applies to all works published by the 
International Journal of Qualitative Methods. Copyright for articles 
published in the International Journal of Qualitative Methods remains 
with the first author.
2.	 Glaser has a tendency to use the terms code, concept, and category inter­
changeably. Here we wish to clarify our use of the terms. By code, we 
refer to the labels attached to incidents during the substantive coding 
stage of analysis. Codes are often descriptive, particularly in the initial 
stages of data analysis. By concept, we refer to the resultant naming of 
a higher level abstraction of a conceptual idea as indicated through the 
cumulative comparison of codes. Similarly, a category denotes the fur­
ther abstraction of individual concepts as a latent pattern. As an exam­
ple, we might note the following codes in data: being myself, being heard, 
authentic connecting, quiet voices, comfortable with each other, trusting my 
own voice, being genuine. These, along with other codes, are later incorpo­
rated into the concept authenticity, which later emerges as a property of 
the core category of rehumanizing.
3.	 We differentiate here between properties as latent, qualitative character­
istics (e.g., authenticity, trust, healing) and dimensions as the measurable 
component of a concept (e.g., space, time, size).
4.	 It is the general implications of a core category that lead to formal theory 
development (Glaser, 2007, p. 4)


99
After studying this chapter, you will be able to:
• articulate the role of theoretical sampling, theoretical saturation, and 
theoretical coding in shaping an emerging grounded theory
• utilize theoretical sampling and theoretical saturation to build emergent 
concepts and categories
• utilize theoretical coding to integrate a grounded theory
O
ur purpose in this chapter is to explain key techniques for giv­
ing shape to the theory as it emerges and develops. As we have 
set out in earlier chapters, GT is an elegant and intricate 
approach to capturing and conceptualizing latent patterns in social set­
tings. While too complex to be approached in a linear fashion, GT is 
readily embraced by our innate capacity for pattern recognition 
through preconscious processing (Glaser, 1998, p. 50). In this chapter, 
we will explore the techniques of theoretical sampling, theoretical satura­
tion, and theoretical coding, which enable the analyst to explicitly build 
and shape the emerging theory and to present it as an integrated 
­
conceptual explanation.
7
Shaping the Theory


CLASSIC GROUNDED THEORY IN PRACTICE
100
l
l
THEORETICAL SAMPLING
Theoretical sampling is the process whereby the analyst decides both 
what data to collect to develop emerging concepts and where those 
data may be available. The purpose of theoretical sampling is to 
discover categories, properties, and interrelationships suggestive of a 
theoretical whole. As such, theoretical sampling is theory-driven 
sampling, controlled by the emerging theory and not determined in 
advance based on a preconceived theoretical framework. Glaser and 
Strauss (1971) note that
no one kind of data on a category nor any single technique for 
data collection is necessarily appropriate. Different kinds of 
data give the analyst different views or vantage points from 
which to understand a category and to develop its properties. 
(pp. 183–184)
Glaser and Strauss (1967) speak of these different views or van­
tage points as slices of data and suggest that “theory generated from 
just one kind of data never fits, or works as well, as theory generated 
from diverse slices of data on the same category” (p. 68). Gibson and 
Hartman (2014) distinguish between data slicing and theoretical 
sampling:
Data slicing, in keeping with Glaser and Strauss (1971), refers 
to the use of an individual comparison between different 
substantive groups or different social units for the purposes 
of developing theory, usually in the form of refining categories. 
Theoretical sampling refers to a process of combining several 
different slices of data for the purpose of developing theory. 
(p. 128)
Theoretical sampling of different slices, in parallel with constant 
comparative analysis, assists conceptual elaboration with the overall 
integration of the theory more likely to emerge organically. By employing 
a range of data slices, the analyst discovers similarities and differences 
that bring forth the variability of concepts and their properties in 
various contexts and under varying conditions, thereby enhancing the 
conceptual density of the emerging theory. The comparisons are 
ideational, not population-specific. As such, “there are no limits to the 
techniques of data collection, the way they are used, or the types of 
data required” (Glaser & Strauss, 1971, p. 184), provided that GT’s 
foundational principle of emergence is respected.


101
Chapter 7    Shaping the Theory
As an illustration of theoretical sampling of different slices of data in 
different substantive groups and in different social units, you may refer to 
Walsh, Kefi, and Baskerville (2010). Sampling was first done in a corporate 
setting, within different firms (different substantive groups) and then in a 
societal setting (different social unit).
During the open coding stage, sampling proceeds in all directions 
that seem relevant and add to the emerging concepts. Once a potential 
core category has emerged, theoretical sampling becomes selective as the 
analyst focuses on developing central issues related to the emerging 
theory “guided by the logic of the emerging analytic framework” (Glaser 
& Strauss, 1965a, p. 289). There is a deductive element to theoretical 
sampling as the analyst memos by probing and questioning emergent 
theoretical ideas and deciding where to go next to get the data to fill gaps 
and elaborate on emerging ideas, but this deduction is in service to 
further inductive analysis as the theory develops. The analyst may go 
back to existing data to sample for emerging ideas as well as generating 
new data from new slices of data. This deductive (conceptual) elaborating 
is essential for the systematic generation of theoretical possibilities, as 
propositions or hypotheses, to build and explain the emerging theory. 
Fresh data from new populations and samples assist in extending the 
scope and coverage of the emerging theory, thus driving toward 
substantively formalized theory and a less idiographic, contextualized 
perspective. The possibilities for multiple comparisons through theoretical 
sampling are endless and so the grounded theorist selects comparison 
groups according to theoretical criteria. “Groups are, from a theoretical 
viewpoint, clusters of variables, which are not readily apparent, and 
many of which are to be discovered by comparison” (Glaser, 1978, p. 42).
The criteria of theoretical sampling are designed to be applied 
in the on-going joint collection and analysis of data associated 
with the generation of theory. Therefore they are continually 
tailored to fit the data and are applied judiciously at the right 
point and moment in the analysis. The analyst can continually 
adjust his control of data collection to ensure the data’s relevance 
to the impersonal criteria of the emerging theory. (Glaser & 
Strauss, 1967, p. 48)
Sampling strategies also control the scope of the theory by con­
sciously choosing to limit or extend the population sampled. Glaser 
and Strauss (1967) suggest that at the outset, the process is to establish 


CLASSIC GROUNDED THEORY IN PRACTICE
102
the basic categories and their properties by minimizing differences (i.e., 
maximizing similarities) in theoretically sampling comparative groups 
so as to verify the relevance of emerging categories, generate their 
properties, and identify the conditions and fundamental differences 
under which they vary. Once this basic work is achieved, the analyst 
turns to maximizing differences among comparison groups as a means 
of identifying the scope of the emerging theory and stimulating the 
theoretical density so as to saturate conceptual properties once the 
basic conceptual framework has emerged (p. 58).
Skill in theoretical sampling develops over time. It requires the ability 
to be both analytic and flexible. There is no precise formula to be applied 
and no limitations on the techniques for data collection; rather, sampling 
requires sensitive judgments as to the best places to go next to find the data 
needed to answer key questions as they emerge in advancing and satisfy­
ing gaps in the emerging theory. Comparing data as it is collected pro­
duces a “cumulatively intensive theoretical sensitivity” (Glaser, 1978, p. 36) as 
the analyst gradually sees more and more of theoretical relevance in the 
data, creating more abstract levels of theoretical connections as the theory 
is gradually built up inductively through progressive stages of analysis.
Jørgensen, Dahl, Pedersen, and Lomborg (2013) explain the theoretical 
sampling approach in their grounded theory of coping with COPD (chronic 
obstructive pulmonary disorder):
The aim was to secure data that could capture the multidimensionality 
of coping . . . Data collection and analysis guided the theoretical sampling 
phase. For example, the coping behavior of the first included participant 
with moderate COPD persistently deviated from the behavior of the 
following five included participants with severe or very severe COPD. 
Several possible reasons for this deviation were looked for in the data. In 
the ensuing data collection and analysis process, this heterogeneity led 
us to obtain an equal distribution of male and female participants with 
differing degrees of COPD. Additionally, data revealed that spouses con­
stituted a significant extra-personal and interpersonal resource, playing 
a vital role in how participants handled a life influenced by breathless­
ness. Therefore, spouses were invited to participate in the following 
interviews with the intention of enriching our understanding of the 
participants’ coping strategies. These discoveries on equal terms with 
other preliminary findings resulted in an ongoing alteration of guides 
for interview, video observation, and selection of video sequences. (p. 5)


Chapter 7    Shaping the Theory
103
Theoretical sampling generally means that the researcher may well 
spend a longer time in the field but will be collecting fewer data as the 
direction of data collection is more intentional and focused, thereby 
giving the researcher momentum, purpose, and confidence in the cat­
egories that are emerging from the data (Glaser & Strauss, 1967, p. 76). 
The adequacy of the analyst’s theoretical sampling will vary with the 
level of theory proposed; the more formal the theory is, the greater the 
scope and range of data sampled will be.
Perhaps You Are Wondering . . . 
Early in the research process, theoretical sampling may involve asking 
an interviewee about a concept that has emerged in other data that 
you have collected. While you don’t want to force the idea on 
interviewees, there is no harm in asking a simple question about their 
experience of the concept (e.g., “Have you ever experienced 
harassment in your workplace?” “Are there any taboo topics in this 
department?” etc.). Your question may spark a response and more 
indicators for analysis, or it may not—which is also data!
	
THEORETICAL SATURATION	
l
Theoretical saturation refers to the constant comparison of conceptual 
indicators in the data to the point where additional indicators yield no 
further theoretical specification or elaboration. Glaser (2001) describes 
theoretical saturation as “intense property development” (p. 191), sug­
gesting that this stage of theory development is “necessary to lift the 
theory above description and enable its integration through theoretical 
propositions (hypotheses) as abstract conceptual theory” (Holton, 
2007a, p. 281). By theoretically sampling to maximize the diversity in 
both comparative groups and varieties of data, the analyst will enhance 
saturation of both the properties and dimensions of concepts. Probing 
questions, during data collection and analysis, will assist the densifica­
tion of conceptual properties (Glaser, 1978).
As the theory develops, the theorist learns which categories require 
the most and the least saturation and which ones can be dropped; thus, 
the emerging theory generates its own selectivity (Glaser & Strauss, 
1967). Core categories should be saturated as completely as possible. 
Failure to saturate the core and related concepts will leave the theorist 


CLASSIC GROUNDED THEORY IN PRACTICE
104
with a thin theory of loosely connected concepts and little explanatory 
power. However, continuing to collect data for concepts that have satu­
rated or for those that are not related in some way to the emerging 
theory’s core category is a waste of researcher time and resources. “The 
criteria for determining saturation then are a combination of the empir­
ical limits of the data, the integration and density of the theory and the 
analyst’s theoretical sensitivity” (Glaser & Strauss, 1967, p. 62).
Perhaps You Are Wondering . . . 
Do I need to saturate my concepts in one location/­
comparative group before I theoretically sample for those 
concepts in another location/comparative group?
It is difficult to know when a concept will be theoretically saturated. 
It is possible that the concept may saturate in one comparative group 
and that you may not identify any additional properties or dimensions 
of that concept in subsequent theoretical sampling of other groups. 
However, theoretical sampling in a number of comparative groups 
may well capture new properties or dimensions or possibly even new 
concepts with relevance to your emerging theory. As the theorist, it 
is your decision as to when you feel that concepts have been satu­
rated and there are no obvious gaps in the emerging theory that need 
to be followed up with additional sampling. It is a judgment call based 
on the explanatory power, or explanatory gaps, of the emerging 
theory.
l
l
THEORETICAL CODING
Once the core category has emerged, having identified those concepts 
that bear some relationship to the core and having theoretically 
sampled to saturate the core and related concepts, the analyst is ready 
to begin the process of theoretical coding as the final shaping of the 
emergent theory. It is at this point, however, that some theorists fail to 
achieve the full explanatory power of classic GT by not pursuing its 
integration through theoretical coding. Instead, they default to simply 
offering conceptual description rather than pressing on to this final 
stage of GT development. As Bacharach (1989) reminds us, “while 
descriptions may be the source material of theories, they are not 
themselves theoretical statements” (p. 497) and “just as a collection of 


Chapter 7    Shaping the Theory
105
words does not make a sentence, a collection of constructs and 
variables does not necessarily make a theory” (p. 496).
The integration of a grounded theory through theoretical coding 
captures our innate ability for perceiving latent patterns that organize 
and integrate relevant related concepts to provide a full explanation of 
the emergent theory’s resolving or managing of a main concern. Glaser 
and Strauss (1971) speak of “the emergence of a natural integration” 

(p. 190). Theoretical codes conceptualize (i.e., model) how substantive 
codes may relate to each other as propositions or hypotheses to be 
integrated into a theory.
Theoretical coding occurs only after all substantive coding is com­
pleted and the core category and related concepts have been theoreti­
cally saturated: “Theoretical codes give integrative scope, broad 
pictures and a new perspective” (Glaser, 1978, p. 72) and help the 
analyst maintain a conceptual level in writing about concepts and their 
interrelations. They “conceptually punctuate the emerging theory with 
an organizing pattern.”1 As discussed in Chapter 6, the core category of 
a grounded theory is always presented (or modeled) as some form of 
theoretical code—a process, typology, range, or other. It is during the 
hand sorting of memos that ideas begin to emerge about the relation­
ships between the core category and related concepts. The analyst 
begins to sense some sort of overall pattern (or model) that organizes 
the emerging theory through an appropriate theoretical code (or codes) 
that explains the relationships, thereby giving the emerging theory its 
overall shape.
Perhaps You Are Wondering . . . 
Can I use extant theories in theoretical coding?
It is important here to differentiate between using the substance of 
extant theories and using how those theories are modeled. Using the 
substance of an extant theory may lead the analyst to “borrow” 
concepts from the extant theory and introduce (i.e., potentially force) 
them onto the emerging grounded theory. On the other hand, being 
theoretically sensitive to how different extant theories are modeled 
may help the analyst to see how the emerging grounded theory might 
be integrated and modeled in a similar way. Playing with this model in 
relation to the emerging grounded theory is fine. It is only when the 
(Continued)


CLASSIC GROUNDED THEORY IN PRACTICE
106
While the emergence of a theoretical code that works to integrate 
one’s theory is often a flash of realization—what Glaser (1978) has 
termed the “Eureka moment”—developing one’s theoretical sensitivity 
to a wide range of theoretical codes enhances the ability to see their 
emergent fit to a developing theory. Reading widely opens a researcher 
to serendipitous discovery of new theoretical codes from other 
disciplines, offering novel frameworks and models for integrating 
grounded theories. The essential requirement is that the code must 
emerge from theoretical sorting of memos; it is not preselected and 
forced on the theory (Glaser, 1992).
Glaser (2005a) advocates reading theory in other substantive areas 
and disciplines as a means of enhancing one’s theoretical sensitivity 
by building a repertoire of theoretical codes. Reading theory in other 
fields enhances the theorist’s pattern recognition abilities, opening the 
possibility of serendipitous discovery of new theoretical codes with 
emergent fit. Glaser (1978, 1998, 2005a) offers many options for 
theoretically coding a grounded theory. The critical criterion is that the 
appropriate theoretical code(s) must emerge through sorting of 
memos; they are not assumed or selected in advance (see, e.g., Walsh 
et al., 2013, who use translation theory [Callon, 1986; Latour, 1987], a 
sociological theory, to help make sense of their data and propose the 
translated strategic alignment model in rupture with existing literature 
on the subject of strategic alignment in the field of information 
systems).
analyst forces the emerging theory to fit a specific theoretical model 
that the full integrative potential of GT may be compromised.
The researcher who does not reach outside extant theory for 
theoretical coding possibilities runs the risk of producing adequate 
but rather mundane conceptual theory. Such theory makes a limited 
contribution to knowledge and, although certainly preferable to 
purely conjectured theory, it will lack the impact that the creative 
emergence of a novel or nontraditional theoretical code may offer. 
No matter how intellectually seductive, fashionable, or discipline-
dictated a theoretical code may be, to cross the line from theoretical 
exploration to forced integration with a preconceived theoretical 
model undermines the generative nature of grounded theory (Holton, 
2007a, p. 283).
(Continued)


Chapter 7    Shaping the Theory
107
Glaser (1978) advocates the implicitness of theoretical coding: 
“One talks substantively and thinks theoretically of the relationship 
between codes” (p. 72). In writing the theory, however, theoretical 
codes may be expressed implicitly or explicitly. For example, Sandgren 
et al. (2007) refer explicitly to the theoretical code of typology in 
organizing their theory of doing good care, whereas Sandgren et al. 
(2006) never directly refer to a specific theoretical code in their theory 
of striving for emotional survival but implicitly suggest the theoretical 
code of strategies. Holton (2007b) describes her grounded theory of 
fluctuating support networks as a basic social structural process with its 
core category of rehumanizing as a basic social psychological process.
Perhaps You Are Wondering . . . 
Is grounded theory always a basic social process?
The concept of the basic social process is a generic theoretical 
construct in sociology explaining fundamental patterns in the 
organization of social behavior as it occurs over time, involving a change 
over time with discernible breaking points or points of transition or 
passage from one stage to another (Glaser, 1978). Y
ou will read 
accounts of GT methodology as requiring identification of basic social 
processes (Bryant & Lasky, 2007; Starks & Trinidad, 2007), but the basic 
social process is just one theoretical code that may be appropriate to 
the theoretical integration of a grounded theory. “All BSPs [basic social 
processes] are core variables, but not all core variables are BSPs” 
(Glaser, 1978, p. 96). As one example, the seminal work of Glaser and 
Strauss (1965a, 1968) employed a typology of awareness contexts and 
a trajectory of the dying process in hospitalized terminal care.
The emergent nature of theoretical coding is achieved through 
hand sorting of accumulated theoretical (analytic) memos:
Facilitating the emergence of relevant theoretical codes requires 
close attention to the ideas memoed, submersion at the concep­
tual level, a balance of logic and creativity, openness to the 
unexpected, and confidence in following what emerges regard­
less of how counter-rational it may seem to extant theoretical 
perspectives. (Holton, 2007a, p. 284)


CLASSIC GROUNDED THEORY IN PRACTICE
108
As such, theoretical coding reminds us that the essential principle of 
earned relevance in GT is not confined to conceptual emergence but is equally 
important in theoretical integration through the emergence of relevant 
theoretical codes.
Perhaps You Are Wondering . . . 
Is axial coding the same as theoretical coding?
While theoretical coding is not mentioned per se in Glaser and 
Strauss (1967), the coding paradigm of conditions, contexts, actions, 
interactions, and consequences that underlie Strauss and Corbin’s 
(1990, 1998) axial coding approach is implicit in the Awareness study 
(Glaser & Strauss, 1965a), in which Glaser and Strauss explain how, 
early on, they “systematically worked out the concepts (and types) of 
death expectations and awareness contexts, and the paradigm for the 
study of awareness contexts . . . [and how] . . . a concern with death 
expectations and awareness guided the preliminary data collection; 
the systematic formulation of these concepts and of the paradigm 
guid[ing] further data collection and the ensuing analysis” (p. 287). 
Strauss (with Corbin) would go on to incorporate the use of this 
coding paradigm—the conditional matrix—as an essential step in 
their process, referring to it as axial coding.
Glaser (1978) would extend the scope of theoretical coding to 
include a wide range of potential models for theory integration by 
elaborating on 18 different coding families (pp. 74–82) and insisted 
that the appropriate model(s) must emerge from theoretical sorting. 
Glaser (1992) challenged Strauss and Corbin’s (1990) insistence on 
the use of axial coding as forcing a preconceived theoretical code, 
undermining the emergent potential of GT and reducing it to 
conceptual description. Later, he would further elaborate and extend 
the range of theoretical coding possibilities (Glaser, 2005). To sum up, 
axial coding relies solely on one theoretical code (paradigm) applied 
to the data for theoretical integration, whereas classic GT remains 
open to the emergence (through theoretical sorting) of a wide range 
of potential theoretical codes around which to integrate and model 
an emerging grounded theory.


Chapter 7    Shaping the Theory
109
This insistence on the emergence of theoretical codes through hand 
sorting of memos is a hallmark of classic GT not espoused by those 
who follow other approaches. For example, Charmaz (2014) advocates 
theoretical codes for clarifying and sharpening analysis but implies 
that the basic social process is the theoretical code underlying all 
grounded theories. While the basic social process is certainly a domi­
nant theoretical code in grounded theories, such preconception effec­
tively precludes the integrative potential for other emergent theoretical 
codes. At the same time, it is worth noting that Charmaz (2014) appears 
ambivalent regarding the need for a core category, preferring the more 
ambiguous notion of “focused coding.” This is perhaps consistent with 
her constructivist stance and its notion of multiple social realities. It is 
not, however, consistent with classic GT.
Glaser (1998) encourages the grounded theorist to develop a reper­
toire of as many theoretical codes as possible as a means of offering 
theories that transcend the somewhat predictable offerings of extant 
theories. Skill in drawing upon such a repertoire also offers some 
assurance against slipping into what Glaser (2005) terms pet or grabby 
theoretical codes. Here the theorist becomes so enthralled with the 
discovery of an enticing theoretical code—one with imagery that 
grabs—that he or she begins to see it everywhere! As with substantive 
coding, theoretical codes must earn their relevance.
Theoretical Sorting
Hand sorting of memos provides theoretical order and integration of 
ideas and requires the theorist to theoretically discriminate as to where 
each idea fits in the emerging theory. The goal is to find the emergent fit 
of all ideas so that everything fits somewhere with parsimony and scope 
and with no relevant concepts omitted. The physical act of hand sorting 
memos further facilitates the preconscious processing of matured ideas 
and guides the organization and integration of the overall theory. Many 
researchers use sticky notes to help with their theoretical sorting. Piko 
(2014) describes how she used sticky notes on a large window to help her 
visualize the emergent integration of her grounded theory. Over a period 
of weeks she sorted, compared, and resorted her notes, moving them 
around on the window and making additional notes until she achieved 
full integration. Some colleagues provided us with photographs of the 
theoretical sorting of one of their grounded theories (see Figure 7.1).


CLASSIC GROUNDED THEORY IN PRACTICE
110
Figure 7.1    
Theoretical Sorting (Sautrey & Mourmant, 2015. Research 
in progress)


Chapter 7    Shaping the Theory
111
One Student’s Experience
To begin, I printed out my memos, sat on the floor, and put them in a 
pile in front of me.
I placed like with like and grouped together memos with apparent 
links.  
As I picked each new memo off the pile, I compared it to the 
others, asking, “Where does this fit?” and wrote further memos 
explicating these relationships and theoretical connections. A lot of my 
memos contained more than one idea relating to different concepts, 
and so I used scissors to cut them into relevant sections, sometimes 
into individual paragraphs or even sentences. This increased the amount 
of paper I had to manage. 
 As I sorted memos, I wrote further memos 
about the relationships between ideas and integrated these into my sort. 
By the time my final theory was written, my memo bank had expanded 
to over 400 memos. Although other researchers have been able to use 
CAQDAS during this process, I struggled to see how this messy three-
dimensional (3D) process could translate onto a two-dimensional (2D) 
computer screen. 
As I considered the spread of papers in front of me and 
shifted the paper about, I was excited by the potential for my ideas to 
come together in any number of ways. 
This preliminary sort also enabled 
me to weed out those immature memos that did not relate significantly 
to the core category. I retained these in a separate file, meaning that I 
could go back to retrieve them if their relevance later emerged.
Finally, after 2 months of sorting and trying to find the best theoretical 
code, I settled on a basic social process (BSP). The BSP is the most 
common theoretical code in CGT and structures the categories into 
distinct stages. (For further reading on the BSP, see Glaser & Holton, 
2005.) As I tried to write up my theory, however, I became more and 
more confused. Seeing the lack of integration on paper confirmed my 
fear that I had been guilty of forcing an inappropriate theoretical code 
on my data. In my mental fatigue and confusion, I had resorted to settling 
on a BSP, despite it not having best fit. I knew that I still had a pile of 
memos that I had not been able to integrate into my chosen structure, 
but had chosen them because I was fearful that the best theoretical 
code would never emerge. Buoyed by Glaser’s advice that often “wrong 
tracks lead to right ways,” I began sorting again. I explored different 
theoretical codes by rereading Theoretical Sensitivity and Doing Grounded 
Theory. I looked again at the theoretical coding families that I had found 
too difficult to understand and looked for examples of them in other 
CGT studies. Interestingly, most of the theories I read were examples 
(Continued)


CLASSIC GROUNDED THEORY IN PRACTICE
112
Some theorists find that diagramming helps them to visualize 
potential ways of theoretically modeling their emerging theories. 
­
Glaser (2014) quotes one experienced grounded theorist:
I draw figures of the different options of theoretical codes to see 
how my concepts relate to each other. Some of my drawings may 
not be so grounded all the time, but it helps me to trigger my 
creativity so when I go back to my memos and write more memos 
on memos I see how everything finally fits together. (pp. 16–17)
At the same time, however, Glaser (1998) cautions against using 
such models as a short-form approach to presenting a grounded theory. 
of BSPs, which is perhaps part of the reason behind my subconscious 
forcing of a BSP onto my data. Glaser has warned against the popularity 
of the BSP, emphasizing that not all core categories will be a staged 
process occurring over time.  
While I had not intended to go looking for 
a process per se, as a novice researcher, I was perhaps swayed towards 
the simplicity and ‘grab’ of a process. Having increased my theoretical 
sensitivity, I was able to see new possibilities. In particular, Glaser’s 
binary theoretical code resonated with my participants’ main concern: 
being person driven in a service-driven organization. Glaser provided 
the following definition:
“This coding has to do with compliance to institutional or 
­
normal roles—binary retreat; or with non-compliance with role 
­
requirements—binary deconstruction. Thus, for example, when a 
professional meets with a client, to what degree do they interact 
in their roles—binary retreat; or drop the roles and just be their 
human selves—binary deconstruction” (Glaser, 2005a, p. 22).
It was exciting to read this definition for the first time, and it 
sparked an intense period of sorting and a flood of memos as I realized 
that the binary theoretical code had significant fit and relevance to 
my data. By prioritizing client realities, practitioners deconstructed 
service ideals, making service delivery individualized and driven by 
the person. Conversely, the equilibrium point could be shifted in the 
opposite direction as practitioners reinstated service ideals over 
client realities, retreating within their roles and service boundaries. 

I thus conceptualized Revisioning as being dual directional, whereby 
practitioners made adaptations to their practice in order to go one way 
or another. (Breckenridge, 2013, pp. 14–15)
(Continued)


Chapter 7    Shaping the Theory
113
He views a diagram as a cop-out, leaving the hard work of elaborating 
the theory to the reader who may not grasp its rich explanatory 
power:
A diagram is but a precursor to a write up of [the theory]. Thus, 
it is an aide to comprehending the meanings of the written 
theory. It is not a theory in and of itself . . . The meaning has to 
be written (p. 169)
As an illustration of this, the diagrams presented in Walsh and col­
leagues (2013) started to be drawn in 2008. However, it took close to 

5 years for the theory to fully emerge and be elaborated in a manner 
that could be understood by, and useful to, other people. When doing 
GT, one of the authors of the present book uses diagrams as memos, 
and these diagrams keep being modified as new data are collected. She 
finds that they improve her theoretical sensitivity: They help in identi­
fying theoretical codes and the emergence of the theory. This is illus­
trated in Appendix E.
It is quite possible that in sorting, the theorist may see the possibil­
ity of more than one theoretical code that might work to organize the 
emerging theory. It is the theorist’s responsibility to select the code that 
best fits to integrate the relationship between the core category and 
related concepts. One theoretical code always dominates, although 
sub-core relationships may require additional theoretical codes to fully 
explain the complexity inherent in some grounded theories.
Theoretical Coding
While the core category of a grounded theory is always some type of the­
oretical code, some theories may require more than one theoretical code 
for full integration. Holton (2006) illustrates her theory of rehumanizing 
knowledge as a basic social process with three stages (finding and liken­
ing, igniting passions, and mutual engagement) and a number of amplifying 
causal loops creating the energy that motivates and sustains the rehuman­
izing process.
Analytic Rules
Glaser (1978) has suggested that failure to hand sort memos results in 
a thin, linear, and less than fully integrated theory. Sorting also leads to 
more memo writing as the theorist begins to see and record the relation­
ships among concepts. This helps to further raise the conceptual level of 


Igniting
Passions
Finding
Likening
Trust
Altruistic
Atmosphering
Connectedness
Bonding
Commitment
Experimentation
Learning
Energy
Challenge
Confidence
Creativity
Mutual
Engagement
Figure 7.2    
Theoretical Coding of the Rehumanizing Process (Source: Holton, 2006)
114


Chapter 7    Shaping the Theory
115
the theory and reduces the potential for the analyst to slip into descrip­
tion. Glaser (1978) has set out a number of analytic rules regarding the 
processes of sorting, carrying forward, and integrating a grounded 
­
theory. The following is a summary of Glaser’s (1978, 2014) analytic rules:
	
1.	 Starting to sort—Start anywhere. Sorting will force its own 
beginning, middle, and end for writing. The important thing 
is to start. Trying conceptually to locate the first memos will 
force the analyst to start reasoning out the integration. Once 
started, the analyst soon learns where ideas are likely to inte­
grate best, and sorting becomes generative and fun.
	
2.	 Core variable—Sort all other categories and properties only as 
they relate to the core category or BSP. This rule forces focus, 
selectivity, and delimiting of the analysis. Theoretical coding 
helps the analyst decide on and figure out the meaning of the 
relation of a concept to the core variable. This theoretical code 
should be written and sorted into the appropriate pile with 
the substantive code.
	
3.	 Promotion or demotion of core variables—This helps the GT 
analyst to explain some variation of a problem as it is pro­
cessed, thus achieving some theoretical coverage. The goal is 
neither to cover all possible theoretical possibilities nor to 
explain all variation.
	
4.	 Memoing—Once sorting on the core variable begins, the con­
stant comparisons are likely to generate many new ideas, espe­
cially on theoretical codes for integrating the theory. Stop sorting 
and start memoing! Then, sort the memos into the integration.
	
5.	 Carry forward—Analyst carries forward to subsequent sorts 
(pages when writing) the use of each concept from the point of 
its introduction into the theory. Requires only the use of the 
concept, not all illustration. The concept is illustrated only when 
it is first introduced (to develop the imagery of its meaning).
	
6.	 Integrative fit—All ideas must fit in somewhere in the outline, 
or the integration must be changed or modified. This rule is 
essential—if the analyst ignores this step, he will break out of 
the theory too soon, and necessary ideas and relations will not 
be used. This rule is based on the assumption that the social 
world is integrated and the job of the grounded theorist is to 
discover it. If he cannot find the integration, he must resort and 


CLASSIC GROUNDED THEORY IN PRACTICE
116
reintegrate his concepts to fit better. The analyst moves back 
and forth between outline and ideas as he sorts, forcing under­
lying patterns, integrations, and multivariate relations between 
the concepts. This step is intensely generative, yielding many 
theoretical coding memos to be resorted into the outline.
	
7.	 Sorting levels—Conceptual ordering of sorting is on at least 
two levels, for example, the core category, then subcore cate­
gories and conceptual properties or dimensions. Or, for a 
monograph or a book, the ordering would be chapters and 
then sections in each chapter.
	
8.	 Idea problems—Sorting forces the analyst to introduce an 
idea in one place and then establish its carry forward when it 
is necessary to use it again in other relations. When in doubt 
about a place to sort an idea, put it in that part of the outline 
where the first possibility of its use occurs, with a note to 
scrutinize it and pass it forward to the next possible place.
	
9.	 Cutting off rules—Theoretical completeness implies theoreti­
cal coverage as far as the study can take the analyst. He must 
explain with the fewest possible concepts and with the great­
est possible scope as much variation as possible in the behav­
ior and problem under study, sufficiently explaining the main 
concern and its resolution with concepts that fit, work, have 
relevance, and are saturated.
	
10.	 Mechanics of sorting—Cut up memos to facilitate ease of sort­
ing ideas. Keep notes as you are sorting, and sketch out mod­
els of potential theoretical codes for theory integration. Stop 
sorting to memo. Even before intentional sorting begins, keep 
files for memos arranged for some initial integration. Move 
quickly when sorting. Avoid overthinking about any one sort 
as the sort will change over time as the appropriate theoretical 
coding emerges.
	
11.	 Theoretical pacing—Have a flexible but regular schedule with 
periods of uninterrupted sorting through memos to help in 
balancing the buildup of ideas preconsciously with the writing 
of memos that capture preconscious processing. At the 
­
theoretical coding stage, the sorting and emergence of ideas is 
at a deeper level as the theorist begins to see the emerging 
theory as an integrative whole. (Glaser, 1978, pp. 120–127; 
2014, pp. 108–122)


Chapter 7    Shaping the Theory
117
Table 7.1 summarizes our discussion of theoretical sampling, 
theoretical saturation, and theoretical coding as essential techniques 
for shaping the overall elaboration and integration of a grounded 
theory.
One very effective way to test the integration and overall shape of 
a grounded theory is to draft a working paper. Start with a very brief 
explanation of the context and the main concern and how the core cat­
egory works to process, manage, or resolve that issue or concern. Hand 
sorting of memos leads to seeing how best to conceptually organize 
this initial write-up and assists in writing the theory both conceptually 
and parsimoniously without reverting to detailed description.
Theoretical 
sampling
Theory-driven process of data collection based on further 
developing the emerging theory
Pursuing different slices of data for theoretical 
elaboration
Iterative process of cycling between existing data and 
collecting new data as required to pursue and develop 
emerging ideas
Theoretical 
saturation
Intense conceptual property and dimension elaboration
Constant comparison of conceptual indicators until 
additional comparisons yield no further theoretical 
specification or elaboration
Saturation bounded by empirical limits of the data, 
integration, and density of the theory and researcher’s 
theoretical sensitivity
Theoretical 
coding
Emergent latent pattern recognition
Core category of a grounded theory is always some type 
of theoretical code (e.g., process, range, continuum, 
typology, etc.)
Conceptualizes how substantive concepts may relate to 
each other as hypotheses
Overall integration of the emergent theory using 
processes, frameworks, models, etc.
Earned relevance (i.e., modeling is emergent through 
hand sorting of conceptual memos)
Often more than one theoretical code needed 
Table 7.1    
Techniques for Shaping a Grounded Theory


CLASSIC GROUNDED THEORY IN PRACTICE
118
IN SUMMARY
In this chapter, we have explored the role of theoretical sampling, theo­
retical saturation, and theoretical coding in shaping an emerging 
grounded theory. We have discussed the techniques of theoretical sam­
pling and theoretical saturation to build emergent concepts and catego­
ries and the importance of hand sorting of memos for emergent 
integration of the final theory through theoretical coding. We take up 
the issues regarding writing grounded theory in Chapters 8 and 9.
TEST YOUR KNOWLEDGE
In the following questions one or several answers may be correct.
1.	 Theoretical sampling
a.	 Is the sampling of different existing theories or theoretical 
framework
b.	 Is deciding what data to collect next based on the theory that 
is emerging from already collected data
c.	 Is the sampling of only one type of data
d.	 May include any type of data
e.	 None of the above
2.	 Theoretical saturation
a.	 Refers to intense development of conceptual properties
b.	 Is the comparison of extant theories to an emergent grounded 
theory
c.	 Continues until no new properties or dimensions of concepts 
emerge from additional data
d.	 Is achieved through theoretically sampling diverse compari­
son groups and data
e.	 None of the above
3.	 Theoretical coding
a.	 Is done when the grounded theory has emerged
b.	 Is done to help a core category emerge from the data
c.	 Is done after theoretical sampling has helped with saturating 
the core category
d.	 None of the above
4.	 Memo writing
a.	 Helps record the relationships among concepts
b.	 Helps raise the conceptual level of the theory


Chapter 7    Shaping the Theory
119
c.	 Helps avoid falling into simple description
d.	 None of the above
5.	 Theoretical sorting
a.	 Is the process of sorting conceptual memos
b.	 Is in aid of discovering relationships between the core cate­
gory and related concepts
c.	 Can be done by hand or by computer
d.	 Can be a tedious process requiring several attempts at resort­
ing of memos
e.	 Draws on the researcher’s creativity and theoretical ­
sensitivity
f.	 None of the above
6.	 Analytic rules
a.	 Help to raise the conceptual level of a grounded theory
b.	 Guide the process of theoretical sorting
c.	 Foster integrative fit of the emerging theory
d.	 Provide a step-by-step guide to doing classic GT
e.	 None of the above
FURTHER READING
Gibson, B., & Hartman, J. (2014). Rediscovering skills for theoretical sampling. 
In Rediscovering grounded theory (Chapter 7). London: Sage.
Glaser, B. G. (1978). Analytic rules. In Theoretical sensitivity: Advances in the meth­
odology of grounded theory (pp. 120–127). Mill Valley, CA: Sociology Press.
Glaser, B. G. (1978). Theoretical coding. In Theoretical sensitivity: Advances in the 
methodology of grounded theory (pp. 72–82). Mill Valley, CA: Sociology Press.
Glaser, B. G. (1978). Theoretical sampling. In Theoretical sensitivity: Advances in 
the methodology of grounded theory (Chapter 3). Mill Valley, CA: Sociology 
Press.
Glaser, B. G. (2005). The grounded theory perspective III: Theoretical coding. Mill 
Valley, CA: Sociology Press.
Glaser, B. G. (2014). Analytic rules. In Memoing: A vital grounded theory procedure 
(pp. 108–122). Mill Valley, CA: Sociology Press.
Glaser, B. G., & Strauss, A. L. (1967). Theoretical sampling. In The discovery of 
grounded theory: Strategies for qualitative research (Chapter 3). New York: 
Aldine de Gruyter.
ENDNOTE
1.	 E-mail correspondence with Barney Glaser, March 9, 2007.


120
After studying this chapter, you will:
• understand why and how writing is, in itself, an essential part of the 
classic GT methodological protocol
• appreciate and recognize readiness to sort and write moments
• understand possible barriers that can block your writing and be aware 
of some solutions to remove these barriers
W
e saw in a previous chapter the iterative nature of data 
analysis toward the emergence of a grounded theory. 
Writing in GT is part of this iterative process and begins 
with the first data collected, as you start writing memos to help the 
analysis and conceptualization. Memoing was covered in Chapter 6. 
In this chapter, we are concerned with writing up the components of 
your emerging theory and how they are integrated together. When 
doing GT, writing and thinking are embedded and intertwined: 
“Writing is the practical expression of thinking  .  .  .   writing is a 
fundamental act of enquiry as valuable, if not more so, as generating 
or collecting, reading and coding data” (Birks & Mills, 2012, p. 126).
Charmaz (2014) describes writing and rewriting GT as part of the 
discovery process:
8
Writing as an Important Part 
of Doing Classic GT


Chapter 8    Writing as an Important Part of Doing Classic GT
121
You’ll gain further insights and create more ideas about 
your data while you’re writing. You’ll see clearer connec­
tions between ­
categories and draw implications from them. 
Thus writing and rewriting become crucial phases of the 
analytic process. Writing demands more than mere report­
ing. Through writing and rewriting drafts, you can bring out 
implicit arguments, provide their context, make links with 
extant literatures, critically examine categories, present your 
analysis, and provide data that support your analytic argu­
ments. Each successive draft grows more theoretical and 
comprehensive. (p. 289)
	
THE PROCESS LEADING TO	
l
THE READINESS TO WRITE MOMENT
Writing in itself is considered by some researchers as a method of 
inquiry (Richardson, 2000). This perspective is particularly well suited 
to a GT approach as writing up is part of classic GT. Writing is not 
something to do after the research has been completed. “The write-up 
is a vital part of the method . . . Its timing is sequential, its doing not 
optional” (Glaser, 2012, p. 12) because it helps conceptualization: 
“Writing and thinking are inextricably intertwined in the construction 
of a grounded theory” (Birks & Mills, 2012, p. 130).
After data have been collected and constantly analyzed, concepts 
have been generated, and memos have been written, the grounded theo­
rist often reaches a state of ideational overload. At this stage, researchers 
are often facing a huge pile of memos, signaling a readiness to sort 
memos. As discussed in Chapter 7, the physical act of hand sorting 
memos helps the researcher preconsciously process this overload, thereby 
guiding the organization and integration of the emerging theory.
Once the core category of your grounded theory has emerged, 
memo writing accelerates as theoretical sampling progresses to elabo­
rate the core and related concepts and initial efforts are undertaken to 
theoretically sort memos for emergent patterns (theoretical coding). 
When theoretical completeness appears to be achieved, memos are 
again sorted, leading to a “readiness to write” (Glaser, 2012, p. 4) 
moment that will prompt the researcher to write the first draft of the 
theory. The readiness to write moment is built into classic GT and 
should not be bypassed; if it is bypassed, it may be definitively lost. It 
is an essential moment to allow the relationship between the researcher 
and her data to evolve (Glaser, 1978).


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
122
Glaser (2012) describes in great detail memo sorting as a crucial 
episode in the classic GT research process. To sort memos, researchers 
should pick up any memo, anywhere in the pile, and place it on a table, 
on the floor, or anywhere big enough to accommodate a large number 
of small pieces of paper (Figures 8.1  and  8.2). They then pick up 
another memo and compare it to the first one; they either place it on, 
or next to, the first one or place it in a new pile while writing another 
short memo about how this memo relates to the first one.
This new memo is then placed between the two piles (perhaps 
suggesting a potential theoretical code). This process continues until 
all initial memos are sorted into different piles with new memos 
linking all piles. Then the researcher writes the theory that he sees 
unfolding from his data on the table. If there are any gaps or clarifi­
cations needed, the researcher goes back to the field to theoretically 
sample. Once sorting is completed, the memos are merged and inte­
grated into a readable text with good flow. Thus, there is no precon­
ceived outline when you write as part of the ­
classic GT process.
The process leading to the readiness to write moment is summarized 
in Figure 8.3.
When sorting memos, the researcher does not put a memo down idly. 
If it does not fit somewhere, it is put aside in a “left out” pile as nonfitting 
or nonrelevant; usually this pile is small. If a memo can fit in several 
places, it should be placed where it fits best to help the write-up of the 
Figure 8.1    
Theoretical/Memo Sorting on a Table (Source: Walsh, 
2014a)


Chapter 8    Writing as an Important Part of Doing Classic GT
123
Figure 8.2    
Sorting Memos on the Floor (Source: Sautrey & Mourmant, 
2015. Research in progress)
emergent theory. Sorting helps generate the theory and writing it up. 
When sorting memos, Glaser (1998, 2005a) advocates talking and writing 
substantively but thinking theoretically. The idea here is that the researcher 
stays close enough to the substantive area to ensure that the emergent 
theory remains relevant to those in the substantive area and that in orga­
nizing and writing up the theory, it does not become so conceptually 
abstract that it no longer works to explain the main concern and its resolu­
tion. On the other hand, the researcher must resist the temptation to fall 
into copious description that inevitably bogs down the theory, holding it 
hostage to one specific setting (i.e., specific people, time, and place), and 
stealing the explanatory power of its broader general implications.


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
124
Figure 8.3    
The Process Leading to the Readiness to Write Moment
1. Data
collection
2. Concept
generation
3. Write
memos
4. Information
overload
5. Readiness
to sort
memos
6. Memo
sorting into
piles
7. Further memo
about piles of memos
and their relationships
Theoretical
integration:
Readiness
to write
Once the researcher has completed analysis of empirical data, and after 
the core category has emerged, he can turn to the literature of his rele­
vant field as more data to be constantly compared in elaborating and 
saturating emergent concepts, developing further their properties and 
dimensions (for an illustration, see Appendix C). Glaser (1978) describes 
the literature as a “prominent class of outside comparisons” (p. 51). Of 
course, in doing so, the researcher must determine whether the litera­
ture is empirically grounded. Even “conjectured” theory can yield 
interesting ideas, but its groundedness will need to be empirically con­
firmed through further theoretical sampling to merit a place in the final 
write-up of a grounded theory. Another approach to addressing the 
literature is to compare it with your emergent theory, discovering and 
l
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APPROACHING THE LITERATURE IN A GT STUDY


Chapter 8    Writing as an Important Part of Doing Classic GT
125
considering what aligns, what modifies, and what challenges extant 
theory. In so doing, the researcher begins to identify how the emergent 
grounded theory supports, extends, corrects, and contributes to extant 
knowledge. As we saw in a previous chapter, the literature from other 
fields of research may also be used to help make sense of collected data, 
build new theory, and, more generally, write a compelling account of 
the emerging theory.
	
WRITING GT WITH MIXED	
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QUANTITATIVE AND QUALITATIVE DATA
Writing as part of the GT process is particularly delicate and essential 
when you use mixed quantitative and qualitative data. A researcher 
may use quantitative and qualitative data and analyze them as one set 
in an exploratory GT stance with the help of those quantitative and/or 
qualitative techniques that the researcher thinks will best serve the pur­
pose of her research (e.g., Schall, 1983; Walsh, 2014a). In some instances, 
neither qualitative nor quantitative data/methods are sufficient to fully 
develop your theory; rather, all are necessary (Figure 8.4). The different 
quantitative and qualitative data, methods, and techniques are mixed 
and supplement each other within a single project. For instance, and as 
highlighted in Walsh (2014c), the researcher may need to qualify quan­
titative data (e.g., make sense of the results of the factor analysis of 
quantitative data with the help of collected qualitative data) or quantify 
qualitative data as a compromise between numerical precision and 
­
narrative complexity (Sandelowski et al., 2009).
Figure 8.4    
Using Mixed Data to Write a Grounded Theory
Patterns
Concepts—
Propositions
Qualitative
Data
Quantitative
Data


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
126
When using mixed data, it is even more essential to write the 
emerging grounded theory as it unfolds, when data becomes daunting 
and memo sorting becomes compelling, so as not miss the readiness to 
write moment. Attempting to later capture back elements that were 
bypassed and not written will be extremely difficult because of the 
embeddedness of the qualitative and quantitative analyses.
The Consequences of Bypassing the Readiness to Write Moment: 
Testimony by Isabelle Walsh on How the Publication of Walsh 
(2014a; see Appendix C) Was Unnecessarily Delayed
The grounded theory proposed in 
Walsh (2014a) had to be written and rewrit­
ten a number of times because the readiness to write moment was bypassed. 
Even though, when sorting the various memos, I felt compelled to write what 
was emerging from my data, I just did not know how to write what I saw in my 
data: at the time, there were very few examples of mixed GT studies.
When I first wrote this mixed GT study (and submitted it to a top-tier 
information systems journal), I first presented all quantitative results, then 
all qualitative results, then attempted to integrate them all. But this was not 
how the theory had emerged. I was very lucky to have reviewers who were 
very patient and very open to grounded theory. They made me go back to 
my data and account faithfully to what had emerged, when and how. Not all 
reviewers are as patient and open as these reviewers were. Guided by the 
reviewers’ queries and criticisms, I had to work very hard to recapture the 
true emergence of the theory.
Eventually, I found that the easiest way to write mixed GT was not to try and 
isolate quantitative and qualitative data/results that were embedded. Instead, I 
found that it was best to relate my stories as they unfolded, as impossible as it 
may appear at first. If quantitative and qualitative data/results were embedded, 
then I wrote “as was.” However, I wrote quantitatively, that is, with acronyms 
for variables/categories/concepts and numbers, when I used quantitative data, 
and I wrote qualitatively, with fully developed names for variables/categories/
concepts and verbatim, when I used qualitative data. What I wrote was then 
easy for me, the reviewers, and (I hope) the readers to read, follow, and under­
stand. However, the whole process took me years and some very indulgent and 
patient reviewers (I have not always been that lucky! Other reviewers might 
have rejected my work during the first round), whereas, if I had done the writ­
ing as the theory emerged and when the readiness to write moment was at hand, 
the whole process would have been easier, less risky, and much faster.


Chapter 8    Writing as an Important Part of Doing Classic GT
127
	
THE FIRST DRAFT	
l
Writing as part of the GT process must be done as simply as possible 
to highlight patterns as they emerge and to help move toward concep­
tualization and increased abstraction. Once the memos are theoreti­
cally sorted, the researcher is usually eager to write up the theory that 
he sees in the data to produce a first rough draft of his conceptual 
ideas. This first draft, or working paper, is “a ‘write up’ of piles of 
ideas [the piles of memos] from theoretical sorting” (Glaser, 1978, 

p. 128). It is “writing concept to concept relations integrated into a 
conceptual theory” (Glaser, 2012, p. 111) and certainly not like writing 
fiction. Perfection in terms of style, grammar, and spelling is not 
required for this first draft, which is not written to please a supervisor, 
a committee, an editor, or reviewers. The purpose of this first draft is 
only to put the theory on paper. In turn, this first draft becomes a 
memo in itself.
In writing up your GT, it is important to stay on a conceptual level. 
One very effective way to do this is to write in the present tense; that is, 
talk about your theory as if it is going on now, for it surely is . . . some­
where! Writing in the past tense (as research findings) tends to drift into 
description, producing an account of what happened during data collec­
tion rather than a conceptual explanation of the discovered latent pat­
tern of social behavior. Employing “illustration dosage” (Glaser, 1998, 
pp. 198–199) by adding brief data excerpts to illustrate key concepts 
helps control conceptual density, improving readability as well as 
enhancing understanding. Too many lengthy quotes, however, can bog 
down your theory, interrupting the conceptual flow and making it dif­
ficult to read.
As part of an academic process, first drafts may eventually be sub­
mitted to international conferences in order to present one’s ideas 
within one’s research field. Even though Glaser often advises not to 
voice one’s ideas and not to present them for other people’s judgment 
until the theory has fully emerged, it may be useful and important for 
young scholars in search of legitimacy to offer a working paper for 
peers’ judgment. In our experience, the peer review process mostly 
helps strengthen results and identify weaknesses as long as the 
researcher takes the comments as provisional and stays true to what is 
in the data. When reviews are positive, they provide great encourage­
ment to complete a research project, which might otherwise have been 
put aside.


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
128
Another Type of Memoing
One of the authors of this book regularly submits her first drafts to inter­
national conferences. She also encourages her doctoral students to do the 
same.
For instance, the grounded theory of “IT culture creep” was published 
in 2010 (Walsh et al., 2010). However, the first draft was published in 2008 
at the Americas Conference in Information Systems (Walsh & Kefi, 2008). 
Comments from peers obtained during the conference helped finalize the 
theory of IT culture creep and eventually publish it in a top tier research 
outlet.
Another example is the grounded theory of the “translated strategic 
alignment model” that was published in a top tier research outlet in 2013 
(Walsh et al., 2013). The first draft of this theory was published in 2010 
at the Americas Conference in Information Systems (Walsh et al., 2010), 
before categories were fully saturated.
These publications at international conferences may be considered 
as a type of memoing and will eventually help the final write-up of your 
theory.
Proposing first or intermediate drafts of their grounded theories to 
international conferences for peers’ perusal and informed comments 
will help prepare researchers for what we consider scholars’ ultimate 
purpose: writing toward publishing in a recognized and well-ranked 
research journal of their field. This issue is addressed at length in the 
next chapter.
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WRITING BLOCKS
Writing as part of the GT process may be impeded in many ways for 
many reasons, and in some extreme cases, blocked. In his 2012 mono­
graph about the act of writing grounded theory, Glaser investigates in 
depth the different elements that may block writing. These writing 
blocks may be personal. For instance, the researcher may suffer from 
performance anxiety, lack of experience, poor reading ability that 
induces a lack of knowledge of existing literature, or an inability to see 
patterns and conceptualize. Writing may also be blocked by method­
ological issues. For instance, conflicts between GT and other approaches 
researchers might wish to use in combination. Finally, writing blocks 
may be induced by circumstances that are contextual or external to the 


Chapter 8    Writing as an Important Part of Doing Classic GT
129
researcher. For instance, supervisors, committee members, or universi­
ties may hinder writing through insisting on some prescribed writing 
formats. Difficult life circumstances (health, family) may also hinder 
writing.
Personal Blocks
Some examples of personal blocks are provided in Figure 8.5 and 
discussed in this section. The fear of writing needs to be acknowledged 
as legitimate for most inexperienced researchers; it will usually dimin­
ish with time and practice. It is mostly linked to performance anxiety, 
more especially when a researcher lacks confidence, has no writing 
experience of any sort, or when the format prescribed to the researcher 
(e.g., by his supervisor) is too constraining.
Novice grounded theorists may be overwhelmed by their data, 
which could feed not one theory but multiple theories, leading to per­
petual outgrowing (Glaser, 2012, p. 112). This can be worsened when 
researchers “fall in love” with their data and search for perfection or 
when too many redundant descriptive elements are blurring the over­
all theory. If writing is done as memos are sorted, then perpetual 
­
outgrowing can be minimized. It will also probably help alleviate what 
Figure 8.5    
Overcoming Some Personal Writing Blocks
WRITING
Write as
sorted
Creative
autonomy
Memo
sorting
Experience
Perpetual
outgrowing
Worrisome
accuracy
Confusion
Fear of writing
Falling in love
with your data
Redundancy
No writing
experience
Prescribed
format
Performance
anxiety
Lack of
confidence
Premature
writing
Inability to see
patterns
Supervisor
validation 
Concept
validity
Field
jargon
May be solved by
May be solved by
May be solved by
May be solved by


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
130
we named “postpartum blues” (!) when one doctoral student, whom 
one author of this book helped supervise, seemed to fall into depres­
sion once her dissertation document had been submitted to her com­
mittee. If parting with your theory is done a little at a time, the final 
break will be less difficult to accept.
What Glaser (2012) names “worrisome accuracy” (p. 119) is another 
possible writing block induced by superfluous descriptive details, the 
need to conform to field jargon, or the desire to obtain a supervisor’s 
validation. Fighting for and obtaining creative autonomy will solve 
this issue. Even though writing is an intrinsic part of classic GT, 
attempting to write up one’s theory before memos are sorted into piles 
will lead to confusion and may well tempt the researcher into precon­
ceiving the theory. It has to be remembered that writing as part of the 
GT process is a “write-up of sorted memos” (Glaser, 2012, p. 120) as 
they provide conceptual meaning and will help fight confusion.
Methodological Blocks
Sometimes, writing may be hindered by some methodological 
blocks. For example, the researcher may feel compelled to use some 
methods or techniques that violate GT principles. These methods or 
techniques have then to be revised and adapted. We have found that 
any methodological blocks may be addressed by going back to the 
seminal texts, Glaser and Strauss (1967) and Glaser (1978), and keeping 
clearly in mind GT’s main foundational pillars as highlighted in 
Chapter 3: emergence, theoretical sampling, and constant comparative 
analysis. We have found that if this is done, while remaining in an 
exploratory stance that is an essential aspect of a GT approach, GT 
methodological protocol may be applied with any data, collected 
through a variety of methods, and analyzed with the help of many data 
analysis techniques. In using quantitative data in particular, we have 
found that many methods and techniques may be adapted to fit GT’s 
main foundational pillars (see boxed text).
Using Quantitative Techniques in a GT Stance
Factor analysis of quantitative data may be used in an exploratory fashion. 
Its purpose is “to examine the underlying patterns or relationships for a 
large number of variables and to determine whether the information can 
be condensed or summarized in a smaller set of factors or components” 


Chapter 8    Writing as an Important Part of Doing Classic GT
131
External/Contextual Blocks
Context may also hinder writing. This is when the “outside world” 
imposes unwarranted constraining demands on the researcher. Every 
researcher will have her own ideal physical writing preferences, for 
example, being completely isolated from anybody else, listening to 
music while writing, and so on. However, researchers will often have 
to deal with nonideal contextual situations imposed by family life 
obligations or the demands of a dissertation supervisor, for example, 
the requirement to review the literature ex ante, which goes against 
(Hair, Black, Babin, Anderson, & Tatham, 2006, p. 101). This quantitative 
technique may help with the selective and theoretical coding of quanti­
tative data by sorting the data “into the appropriate pile” (Glaser, 2004, 

p. 191). 
When applying this technique with the help of some software, make 
sure not to inform the software with the number of factors you might 
expect (based on previous qualitative phases and results); rather, let the 
slices of quantitative data inform the emerging number of factors.
Cluster analysis is another statistical technique that we have found use­
ful when doing GT with quantitative data. It is a statistical data analytic 
method viewed mainly as an exploratory technique (Hair & Black, 1998) 
and whose main purpose is “to group objects based on the character­
istics they possess” (Hair, Anderson, Tatham, & Black, 1995, p. 423). High 
within-cluster homogeneity and between-cluster heterogeneity is the aim. 
Cluster analysis is very close to the GT comparative method. It searches 
for nonobvious patterns and relationships between important sets of data. 
However, the way one uses cluster analysis (or any other multidimensional 
scaling instrument) will, of course, determine whether theory is allowed to 
emerge from the data or whether theory is forced upon those data.
Even techniques like structural equation modeling, traditionally con­
sidered as a confirmatory technique, may be adapted to fit a GT stance. 
For instance, when using mixed qualitative and quantitative data, you may 
choose partial least squares (PLS) path modeling to investigate the quanti­
tative data. This approach is generally viewed more as an exploratory, soft 
modeling approach than as a confirmatory one (Gefen, Rigdon, & Straub, 
2011; Hair, Sarstedt, Ringle, & Mena, 2012; Ringle, Sarstedt, & Straub, 2012; 
Vinzi, Trinchera, & Amato, 2010). All possible paths between the various 
constructs under scrutiny may be investigated before deciding which paths 
are the most probable when considering quantitative and qualitative data 
together as one set.


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
132
classic GT’s main pillars or meeting impossible deadlines. This will 
create a block that can be alleviated (as proposed by Lowe, cited by 
Glaser, 2012) by engaging momentarily in other, purely menial, tasks 
and then eventually coming back to the problematic contextual issue 
after having taken time to ponder over it.
The most serious external or contextual writing block may some­
times stem from your PhD supervisor or your dissertation committee 
who may not be experienced grounded theorists and may try to 
impose their own pet approaches, which may violate some of GT’s 
foundational pillars. Beyond all precious advice that one may find in 
various publications (see, e.g., Glaser, 2012), we have found that the 
best way to get around possible issues with supervisors or committee 
members is to have your work recognized by your peers, through pub­
lication in top tier international conferences and/or peer reviewed 
journals before you present your finished work to them. Writing for 
publication is addressed in the next chapter.
IN SUMMARY
Writing in itself is an intrinsic part of GT. If ideational overload is over­
looked and the readiness to write moment is bypassed, the completion 
of your work will be delayed. Some personal and contextual blocks to 
writing may be overcome through memo sorting and simply writing 
things as they emerge and as you see them through collected data, on 
the way to building up your creative autonomy and developing your 
GT writing experience.
TEST YOUR KNOWLEDGE
In the following questions one or several answers may be correct.
1.	 When doing GT
a.	 You have to write memos all the time as you analyze your 
data and ideas emerge
b.	 You have to write your memos properly so that anybody can 
understand them
c.	 You have to wait to be ready to write your theory before you 
start memoing
d.	 None of the above


Chapter 8    Writing as an Important Part of Doing Classic GT
133
2.	 When you are overtaken by information/ideational overload, 
you should
a.	 Stop doing GT
b.	 Change your codes
c.	 Start memo sorting
d.	 None of the above
3.	 The readiness to write moment
a.	 Comes after memo sorting
b.	 Is when you are ready to write a first draft of some theoretical 
statement
c.	 May be bypassed
d.	 None of the above
4.	 Drafts of your theory
a.	 Must include the relationships between concepts that 
emerged from your data
b.	 Involve writing up the memos that you have sorted into piles
c.	 Eventually become memos until another, more densified, 
theory emerges
d.	 None of the above
FURTHER READING
Glaser, B. G. (1978). Theoretical sensitivity: Advances in the methodology of grounded 
theory (Chapter 5). Mill Valley, CA: Sociology Press.
Glaser, B. G. (2012). Stop, write: Writing grounded theory. Mill Valley, CA: 
Sociology Press.
Schall, M. (1983). A communication-rules approach to organizational culture. 
Administrative Science Quarterly, 28(4), 557–581.
Walsh, I. (2014). A strategic path to study IT use through users’ IT culture and 
IT needs: A mixed-method grounded theory. Journal of Strategic Information 
Systems, 23, 145–173.
ENDNOTE
1.	 All articles in FQS are licensed under a Creative Commons Attribution 
4.0 International License. Authors hold the copyright and retain 
­
publishing rights without restrictions.


134
9
Writing Classic GT for 
Publication
After studying this chapter, you will:
• understand that you can rarely publish a grounded theory as it actually 
emerged from your data
• accept reviewers’ opinions and address their concerns even though you 
might not always agree with them
• have some general guidelines to help you get your work read, accepted, 
and published
W
hether or not you have read the previous chapters, having 
arrived at this stage of the book probably means that you are 
convinced that using GT as a research paradigm, a method-
ology, or an analysis framework is important and significant for your 
research. However, writing as part of the classic GT process, that is, of 
“doing” GT, and writing classic grounded theory for an audience, to 
share and disseminate your ideas, are two rather different processes.
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DOING VERSUS WRITING
Writing—the way we write—matters “theoretically and practically. . . . 
How should we write our research? Who is our audience? . . . What are 


Chapter 9    Writing Classic GT for Publication
135
our goals? . . . What readers will we write for? . . . How should we write 
so that our writing matters?” (Richardson, 1990, p. 9). These questions 
highlight many implicit issues, which can be broadly classified as 
­
rhetorical, ethical, and methodological.
Rhetorical Issues: The Way We Write
The first issue with writing grounded theory for an audience is the 
fact that no written words can fully describe the iterative GT process, 
the incessant iterations between data and conceptualization, or the 
final conceptual leap that illuminates a grounded theory. If one 
attempts to describe fully in writing, and in truth, the way their GT 
research was conducted, it is most probable that few people would 
read or understand it. When you write a classic grounded theory, it is 
therefore essential to give sufficient information to guarantee the 
reader that all classic GT guidelines have been followed, but, at the 
same time, you must also organize your work in a way that respects 
the conventions of the intended audience.
There are many different ways to write and account for your 
research depending on your chosen outlet. When you write your dis­
sertation, you have to first please your thesis supervisor, then your 
committee members and external examiners. When you write an arti­
cle to be published in an academic outlet, you have to please the 
reviewers and the associate and senior editors. When you write a 
monograph, you mostly please yourself. Finally, when you write 
a methodological book like this book, your purpose will be fulfilled if 
your readers find it easier to apply the method(s) you describe after 
reading your book.
Writing options and writing outlets are numerous. Even though 
Lyytinen, Baskerville, Iivari, and Te’eni (2007) have pointed to differ­
ences in publication culture whereby European scholars have tended in 
past years to favor book publications, most academics are currently 
hired, promoted, and tenured on the basis of publications in so-called 
top-tier journals. As academics, we depend essentially on promotion 
and tenure committees and on other committees or public bodies that 
allocate resources to academic units and individual researchers. These 
various committees will judge your work based mostly on your publi­
cations in top tier journals (Takeda, 2011).
Furthermore, if, while doing your PhD, you publish your classic 
grounded theory in a top-tier journal, or (as revision processes of such 
journals are very lengthy) you at least avoid the dreaded “desk reject” at 
the outset and engage your work in the revision process of such a journal, 


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
136
it is unlikely that your thesis supervisor will object or question the use of 
GT or the way you write your research, whatever way you wish to do so. 
When you are a young researcher, your peers’ positive judgment is a very 
convincing argument that is difficult to contradict, even by an established 
professor. Therefore, we will concentrate in this chapter on the type of 
writing required to publish in those so-called top-tier outlets.
Different journals emphasize different topics, methodologies, or 
both. To find out about a journal’s preferences, you can investigate 
their mission statement and submission guidelines, which are available 
on journal websites. To publish in any research journal, your work has 
to be presented in a way that meets the writing conventions of that 
journal and the respective editors’ and reviewers’ expectations. There­
fore, you have to find ways to reconcile these conventions with classic 
GT precepts.
If you choose to publish in a research journal (rather than, for 
instance, in a monograph or a book), another issue that you face when 
writing classic grounded theory is that journal articles “have become 
increasingly narrow in scope and focused on a little bit of knowledge—
as if knowledge ‘really’ were a bin of bits” (Richardson, 1990 p. 16) and 
were only progressing incrementally. However, if you have success­
fully applied classic GT, you should have produced a theory that has 
the potential to be in rupture with existing literature, hence difficult to 
reduce to incremental progress and a single journal article. One way to 
counteract this drawback is to rationalize, as much as possible, the way 
you write and fit in, as much as possible, the prescribed formats, with 
established rhetorical devices that may be found in most social sciences 
(de Vaujany, Walsh, & Mitev, 2011).
Different Writing Formats
The editor in chief of a top-tier research journal, whom we know well, once 
confirmed to us that research articles are traditionally more suitable for 
incremental discoveries. This came up when, new to the game of publish­
ing, one of the authors of this book asked for his advice on publishing a 
conceptual piece of work that would create some upheavals if published. 
The editor in chief had read and liked this piece of work. He strongly 
advised us to publish it as a book because the intellectual leap that it pro­
posed was too broad to fit in a research article and would involve a length 
of manuscript that would be difficult to adhere to any research journal’s 
­
writing ­
conventions.  
As we did not wish to go through the book format, 


Chapter 9    Writing Classic GT for Publication
137
Finally, and as highlighted by Fetters and Freshwater (2015), when 
aiming to publish, “effective authors compose their articles for the 
understanding of the reader, and not their own egos” or seeking “to 
impress others” (p. 204). These authors propose that we consider every 
word and sentence we write and ask ourselves the following questions: 
“Will this make sense for the reader? Will this help the reader under­
stand what I want to convey?” (p. 204).
Ethical Issues: Adjusting Your Work to Fit 
Established Conventions
Academic papers are encoded with academic conventions (e.g., the 
main current paradigmatic positions of the research field covered by a 
given journal) and writing conventions (e.g., length, focus, method­
ological stance) fitting traditional standards that readers expect will be 
met (Richardson, 1990). To help you identify these conventions and 
verify that they are compatible with whom you are and with your work, 
Richardson (2000) implicitly provided some valuable guidelines even 
though prescription was certainly not her purpose as she was solely 
aiming to identify different writing formats in qualitative research. 
However, using some of her ideas out of context helped us to highlight 
some interesting propositions that can be applied while writing classic 
grounded theory geared toward publication in a top-tier outlet.
We would propose for you to choose an article published in a top tier 
outlet of your own field, an outlet where you would like to publish. 
Choose an article that is fairly recent (published during the past 3 years) 
because cyclic changes in editorial boards may change some of the applied 
writing conventions (de Vaujany et al., 2011). As the chosen article has 
been published in the chosen outlet, it exemplifies in some ways this out­
let’s writing conventions and will guide you toward framing your work 
within these conventions. This article should be one that you admire, one 
that you believe makes important contributions to the field, and one 
whose philosophical assumptions are globally in tune with yours.
Provided reviewers do not subsequently ask you to forfeit some of 
your own philosophical assumptions, you then might be able to fit 
your work within the conventions of the given journal. That being said, 
the original article was divided into several articles, to which we added 
empirical data. The original piece of work thus “gave birth” to a number of 
journal articles, most of which have now been published.


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
138
and whatever the writing conventions and philosophical assumptions 
of the editorial board of a journal, our experience is that if your work 
is considered innovative and important for your field, and if your 
­
writing is clear enough to allow the reader to understand your contri­
bution, you will eventually be published in a top-tier journal—perhaps 
not in the journal that you originally aimed for (you may have to inves­
tigate other top-tier journals with broader-minded editors in chief), but 
you will be published.
Learning From Experience
It took me many years to be able to publish my first GT article in a top 
tier journal. Before I actually succeeded in publishing it, I had to submit 
it twice.  
After the initial submission in the first journal, I was asked to 
review and resubmit while removing the methodological GT approach 
as well as the core category of my grounded theory! Although there 
was no doubt that my work was not convincing enough and that other 
elements of these initial reviews helped me improve it, I did not review 
and resubmit to the same journal. I felt that, if I did so, I would be lying 
about my research and that I would somehow forfeit my identity as a 
researcher. Instead, I improved my work based on some other elements 
of the initial reviews that I felt highlighted some serious flaws in my work, 
but I submitted to another journal, which incidentally was higher ranked 
and globally considered as “better” than the first one. Interestingly, during 
the reviewing process of this second submission, and after I had addressed 
all revisions required by the reviewers, the associate editor advised me, 
for the last round of review, to bring in a coauthor who could guide me 
to write within the conventions of the journal. This did not jeopardize my 
philosophical assumptions, nor did it entail disguising my research. It only 
meant that my writing format was not the right format for the chosen 
outlet. I brought in an experienced coauthor who helped me understand 
the associate ­
editor’s and the journal’s writing expectations, and the article 
was eventually published.
—Isabelle Walsh
Methodological Issues: Writing to Be Read and Published
Even though doing GT is never a linear process, the writing format 
of GT is rarely otherwise if one wishes to be read, easily understood, and 


Chapter 9    Writing Classic GT for Publication
139
published in a top-tier journal. As hypothetical-deductive research has 
left an enduring imprint in most of the so-called top tier outlets, and 
although sections found in published empirical articles are sometimes 
named differently and sometimes merged, the basic standard and 
expected structure of a journal article that relates empirical research is, 
globally, fairly linear and includes six sections. Each of these sections as 
outlined in the following box aims to answer particular questions.
The Standard and Expected Structure of a Journal Article and 
Questions to Be Answered
1. Introduction
• Why should the reader bother to read your article? (What is the 
issue you are investigating or addressing? Why is it important?)
• What are the major contributions of your work?
• How is your work organized?
2. Literature Review
• What does the literature tell us about the object of your 
research?
• What is the gap that is being addressed?
3. Methodology
• What are the data, data collection methods, and data analysis 
techniques used in your research?
4. Results
• What have you found?
5. Discussion
• How do your findings fit in with existing literature?
• What are the limitations and contributions of your work?
6. Conclusion
• How has the research issue, highlighted in the introduction, been 
addressed?


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
140
On one hand, this expected structure, in itself, may raise a num­
ber of issues in relation to a classic GT stance. On the other hand, it 
need not raise any issues if we keep in mind that doing research and 
writing to publish are different processes, even though they are linked, 
and writing research should reflect, at least in some manner and to 
some extent, the way the research has been conducted.
When you do classic GT, you basically start with a general area of 
interest and methodological guidelines that you will apply in congru­
ence with your philosophical positioning, but you do not even know 
which data are going to be collected or how you are going to collect 
them. You start by listening and observing in the field. It is only after 
you have identified the main concern and after a core category has 
emerged to explain this main concern that you turn to the literature. 
But this does not mean this is the way you should actually structure 
your story for publication. It also does not mean that you have to lie 
or ­
pretend you did what you did not do. For instance, when you 
write the introduction, in which traditionally the reader expects to see 
your research question, the fact that you comply with this does not 
mean that the focus of the study was decided at the outset of the 
study through preconceived notions. You can, in the introduction, 
indicate that you began the study with an area of interest and phrase 
the research question in an open manner (e.g., What is the experience 
of knowledge workers regarding work-related informal networks?). 
Even when placing your literature review after the introduction and 
before the results, you can clearly indicate that it was done ex post but 
is presented ex ante in order to help the reader understand your 
work. This confirms that, as per classic GT precepts, the literature was 
held in abeyance until the core category of your grounded theory 
emerged.
Being a scholar means that you read a lot. Doing GT does not 
mean that you stop doing so. Sometimes, an existing framework 
developed in a different field of research (or a different domain in your 
field) and discovered by chance through your readings helps you 
understand and code your data, not because you tried to apply it and 
forced your data into this framework but, rather, the opposite; that is, 
this theoretical framework was not preconceived but rather emerged 
from other literature (which is also data as such) and helped you make 
sense of some of your data (see the use of translation theory, a theory 
from the field of sociology, in Walsh, 2010, or the use of values, needs, 
and motivation theories, from the psychological field of research, in 
Walsh et al., 2010).


Chapter 9    Writing Classic GT for Publication
141
To illustrate the difference between doing and writing GT, we give 
the example of an article published in a top tier journal of the 
information systems field of research (Walsh et al., 2013), which was 
first published online in 2013 and won a best paper award in May 2014. 
Table 9.1 presents a summary of how the research was conducted and 
how it was presented for publication.
The Way the Research Was Conducted
The Way the Journal Article Was 
Written
Phases
Description
Sections
Description
1
Open coding
Constant 
comparison
Identification of 
the main 
concern
1
Introduction with 
traditional research 
question as 
formulated in Phase 6 
of the research 
(Introduction)
2
Theoretical 
sensitivity to 
translation 
theory
2
Theoretical anchorage 
(Literature review)
3
Selective 
coding
Constant 
comparison
Emergence of 
the core 
category
3
Research settings, 
samplings, data 
collection, coding 
(Methodology)
4
Literature 
review
4
Description and 
conceptualization 
(Results)
5
Theoretical 
saturation
5
Proposed theory and 
discussion with 
respect to existing 
literature (Discussion)
6
Theoretical 
coding
Integration and 
elaboration of 
the grounded 
theory
Articulation of 
traditional 
research 
question
6
Summary of what was 
accomplished and 
answer to the research 
question (Conclusion)
Table 9.1    
Conducting a GT Research Project Versus Writing an 
Article for a Research Journal


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
142
The actual chronology of this research, as befitting classic GT, was 
identification of the main concern during many years of practical expe­
rience, consultancy missions, and preliminary research investigations 
in three firms in 2008 and 2009. This main concern was the failure of 
information technology (IT) projects in firms. It must be highlighted 
that this was not our (the researchers’) main concern, as the consultancy 
missions conducted in the three firms (and later theoretically sampled 
for our research) were not concerned with IT projects as such but with 
change management. The main concern identified was, indeed, the 
participants’ main concern.
Later, while preparing a class in 2008 about organizational theories, 
we (by chance) discovered translation theory and actor network theory 
(ANT; Callon, 1986; Latour, 1987). Theoretical sensitivity to this framework 
allowed us to move on from open coding to relate various categories, 
thus highlighting the core category (which had not been highlighted 
before as such in the literature). The core category, Aligning stakeholders’ 
needs, emerged in 2009, after which we started selective coding leading to 
theoretical coding. The literature review was conducted from 2010 to 2012 
by a supervised doctoral student, who co-signed the final published 
article. All categories in our theory were fully saturated in 2012. The 
research question was formulated at the end of 2012 while finalizing the 
writing of the article before submission to a journal. If one looks at the 
way the article was written and published after three rounds of revision 
and substantial modifications required by reviewers, it reads as follows:
Introduction: Up front, the introduction includes an argument 
on the fact that our area of interest (the alignment between 
information systems and business strategy) is an important 
issue for both managers and researchers (it is considered as 
essential in firms to drive toward competitive advantage), the 
limitations of the existing literature (the concept of strategic 
alignment has mostly been studied through Henderson and 
Venkatraman’s 1993 strategic alignment model, and this static 
model relies on assumptions that are no longer valid), and the 
research question (How do organizations achieve strategic 
alignment in practice?).
Literature Review: The second part of the article, titled “Theoreti­
cal Anchoring and Analytical Framework,” describes the model 
that predominates in the literature and why it is challenged. Then 
ANT is introduced as well as the important concept of translation, 
which helped us relate, make sense of, and understand our 
empirical data.


Chapter 9    Writing Classic GT for Publication
143
Methodology: This part describes research settings and sam­
plings, data collection and coding, and our coding categories.
Results: The results include a descriptive part (the emerging 
landscape in the three firms investigated in depth) and our 
conceptualization of seven dynamic needs alignments to 
maximize the chances of success of an IT project.
Discussion: The theory that we propose (the translated strate­
gic alignment model [TSAM], which is a processual, non-
functionalist model) is discussed in this section together with 
its contributions and how it addresses previous models’ short­
comings, hence the gap filled in the literature, as well as the 
managerial (practical) implications.
Conclusion: The conclusion summarizes the work accom­
plished and how the research question was addressed in the 
article.
We were familiar with the journal where the article was submitted 
and knew that several members of the editorial board were grounded 
theorists. Despite this familiarity, we were still expected to deliver a 
certain standard format that could be easily read and understood by 
any researcher or practitioner, whether or not they knew about GT. In 
truth, and as far as we have experimented, no research process that 
leads to theorizing in rupture with existing literature is linear. How­
ever, top-tier journals expect our writing to be so. To complicate issues 
further, they also expect a compelling story and significant results. An 
article, beautifully fitting the conventions and standards of a given 
research field and journal but which does not have a compelling story 
to tell, nor interesting results, stands little chance of being published in 
a top-tier journal.
	
WRITING CLASSIC GT USING MIXED DATA	
l
Another major and delicate issue in writing classic GT is aiming to 
do so while using both qualitative and quantitative data and tech­
niques. Data, methods, and techniques have become, wrongly 
(Walsh, 2014c), implicitly embedded in paradigms: quantitative data 
and techniques in positivist hypothetical-deductive research that 
uses a mathematical type of writing and corresponding symbols; 
qualitative data and techniques in interpretive research that uses a 
more narrative style of writing. However, research is currently 


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
144
moving beyond these extreme caricatures, and a solution has to be 
found to write mixed design research while retaining sense-making 
for the reader. Writing a compelling qualitative GT study toward 
publishing it in a top tier journal is already difficult enough. Doing 
so for a mixed GT study is an even greater challenge; it may, how­
ever, help greatly in the production of formal theories of great value 
because these theories are often in rupture with existing literature 
(Walsh, 2014c).
Beyond remaining within the established conventions of top tier 
outlets and rationalizing the presentation of our work, we found 
through trial and error that the easiest way to write the results of a 
“mixed design GT” study is, in the results section of the work, to stick 
as close as possible to the way the theory unfolded. Walsh (2014a) 
mixed method grounded theory was written several times trying dif­
ferent writing strategies before it was accepted in a top-tier journal. It 
was first submitted to what is considered a well-established and “tra­
ditional” quantitative positivist journal. To have it be considered for 
publication by such a journal, the qualitative data slices and the GT 
stance were “obliterated.” It was, of course, rejected, but we were very 
lucky to have a senior editor who went to the trouble of pointing out 
weaknesses. Unsurprisingly, the weaknesses were positioned where 
we had obliterated qualitative data.
The work was then submitted in full to a journal where we had 
previously published qualitative GT. When we submitted the article, 
we warned the editor in chief that the design was unusual and required 
reviewers to be familiar with classic GT and at ease with both qualita­
tive and quantitative data and techniques. Here again, we were very 
fortunate, as one of the reviewers challenged us to write our results as 
close as possible to the way our theory had unfolded. When we first 
submitted it, we had tried to describe the results obtained from the 
quantitative data collected and the techniques applied, then the quali­
tative ones, and finally the theory that had unfolded. But this did not 
work because both qualitative and quantitative data, methods, tech­
niques, and analyses were embedded. Next we tried mixing them since 
they had truly been embedded in the reality of our research journey, 
but we lost the reader because our explanations were too complicated. 
Finally, in the third round of revision, we used a subterfuge: We listed 
our theoretical propositions and explained how we had reached each 
of them using mixed data, ­
methods, and techniques. To be easily 
understood by the reader, we talked and wrote quanti (using in the text, 
numbers, acronyms to ­
designate the variables, and mathematical 


Chapter 9    Writing Classic GT for Publication
145
symbols) when we were describing quantitative data and techniques, 
and we talked and wrote quali (using in the text nothing but words and 
full extended names for the variables) when we were describing quali­
tative data and techniques. In other words, we used established 
­
philosophical caricatures to make our work understood. Then, the fact 
that we used both qualitative and quantitative data and techniques to 
develop some ­
propositions did not hinder understanding.
This article (Walsh, 2014a) was the follow-up to a previous qualita­
tive grounded theory that had already been published (Walsh et al., 
2010), so with the primary data set collected for this second work, we 
used as secondary data what had been collected and used in the previ­
ous study. But we could not say so as, otherwise, reviewers could have 
identified us and this would have jeopardized the double blind review­
ing process. We resolved this issue by using our previous work as data 
(obtained from existing literature). For further information on method­
ological aspects of this article, the reader can refer to Appendix C and 
an example of the mixed writing described earlier. The introduction of 
elements extracted from the literature as data is provided in the follow­
ing boxed text.
Using the Literature as One of the Sources of Data
Proposition 2: Individual IT culture has a positive influence on contextual 
IT needs.
Walsh et al. (2010) found that the users who were highly IT
-accultur­
ated also had high levels of contextual IT needs. When we investigated 
the path IITC à CONITNEE, we found β = 0.400, significance: p < 0.001. 
However, this path was investigated with quantitative data obtained within 
an academic context and a highly-computerized organization. Some ele­
ments resulting from our qualitative data set alerted us. For instance, 
interviewee P4 is a young assistant professor with a couple of previous 
teaching experiences and who has started a new job in “X” school; she 
grew up in a computerized home environment with a father who is an 
IT engineer. She told us: I certainly could not do my job in ‘X’ school without 
IT. This was not the case in my previous institution, although I personally feel I 
need IT to do my job as a professor [ . . . ] for instance, it’s the first time I have 
been asked to use PowerPoint slides to teach. In my old school, nobody ever 
used PowerPoint. Thus, this professor did not perceive the same contextual 
(Continued)


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
146
l
l
WRITING CONVENTIONS
IT needs when she taught in an old-fashioned, IT
-conservative university 
as when she teaches in a forward-looking, highly-computerized university. 
This more generally suggests that the same person with a given level of 
IT acculturation may perceive different contextual IT needs, depending on 
the organization they work in.
In the wording of the items for the construct of contextual IT needs . . . it 
is quite clear that we specify the organization that we refer to. 
Therefore, in 
the present study, the influence of the organization is included in the con­
struct of contextual IT needs and explains why this path was quantitatively 
supported; it might not be so in a different organization and proposition 2 
might not extend to other substantive areas and other contexts. (Walsh, 
2014a, p. 155)
Writing, like power, is a “sociohistorical construction” (Richardson, 
1990, p. 51), and we inscribe values in our writing. Therefore, we can­
not improvise if we want our research to be published in the top jour­
nals of our research fields. It is a long learning process. “Styles of 
writing . . . are not fixed or neutral but reflect the historically shifting 
domination of particular schools or paradigms” (Richardson, 1990, 
p.  16). For instance, when we started publishing our GT works, we 
found that GT appeared to be more easily accepted in European jour­
nals of our research field even though these journals were also well 
recognized in North America. This guided our choice of relevant out­
lets for our work. This was even more the case when we were trying to 
publish mixed design GT that used mixed qualitative and quantitative 
data. This might be explained by the fact that Paul Lazarsfeld’s works, 
to which GT is anchored, were, at the time, extremely well accepted in 
Europe, where they have led to an important school of thought in 
­
statistics, led by Benzécri (1973).
The situation is currently1 evolving, and North American journals 
are being made more and more aware of what GT may bring to 
research, and its use has been increasing exponentially during the past 
two decades (Figure 9.1). Some fields, for instance, medicine and nurs­
ing, have widely recognized the added value of GT (Figure 9.2). Other 
(Continued)


Chapter 9    Writing Classic GT for Publication
147
Medicine (40.1%)
Other (11.7%)
Undefined (2.3%)
Engineering (3.5%)
Arts and Humanities (3.9%)
Business, Management (8.3%)
Health Professions (6.4%)
Computer Science (8.7%)
Psychology (13.4%)
Nursing (22.4%)
Social Sciences (30.8%)
1500
1250
1000
750
Documents
500
250
0
1974
1980
1986
1991
1996
2001
2006
2011
Figure 9.1    
Number of GT Works Published per Annum (All Research 
Fields Included)
Figure 9.2    
GT Works Published by Subject Area
fields, such as the management/business field (Figure 9.2), are still 
lagging behind, although a significant increase in the number of GT 
papers published during the past decade can be witnessed even within 
these fields (Figure 9.3).


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
148
l
l
SOME GUIDELINES FOR PUBLISHING 
CLASSIC GT IN TOP TIER RESEARCH JOURNALS
1991
140
120
100
80
60
Documents
40
20
0
1997
2002
2007
2012
Figure 9.3    
GT Works Published in the Business Research Field
If you are an experienced researcher who has already published a num­
ber of works, you probably know your research field and its writing 
conventions fairly well. Then, you might try to publish your GT work in 
any research outlet and “fight” for it to be recognized even though it 
might not be congruent with the historical writing conventions of the 
outlet that you have chosen. The “fight” might be long and arduous. If, 
however, you are a novice researcher, you might not be prepared to take 
this chance. Here we provide some guidelines for novice researchers.
Present your ongoing works in conferences and to colleagues 
within and outside your institution, within and outside the GT research 
community, and as often as you can, before submitting to a journal. 
Encourage and accept criticism. This will help you improve your work 
and maximize your chances of being published.
When you think you are ready to submit to a research outlet, study 
the top tier journals of your field thoroughly, as well as their writing con­
ventions and the philosophical assumptions of the editorial board. For 
instance, if you pick a journal that has always published 100% quantitative 
positivist research, it is completely unlikely that a qualitative interpretive 
grounded theory will be accepted in this journal. On the contrary, if you 
pick a journal renowned for its qualitative or mixed methods research, 



Chapter 9    Writing Classic GT for Publication
149
you might have a chance proposing an interpretive qualitative grounded 
­
theory or a critical realist mixed design grounded theory, respectively.
Once you have identified the journal(s), select articles that are 
meaningful and interesting to you and use them as models of the 
expected writing conventions. This does not mean that you reproduce 
them but, rather, that you use them as models of the type of writing 
that is expected and commonly accepted in the chosen outlet(s). 
Rewrite your work to fit the identified writing conventions as long as 
this does not cause you to forfeit some of your own basic philosophical 
assumptions or “disguise” your research beyond what you deem 
acceptable. If this is the case, choose another outlet.
If you are lucky enough not to receive a “desk reject,” your article will 
be sent to reviewers. Be patient and humble when you read the review­
ers’ comments. All reviewers cannot be wrong. Reviewers are usually 
chosen within a population of researchers who have already published in 
the outlet that you have chosen. These reviewers are usually specifically 
appointed to review your work because of their competence in your 
research area and sometimes for their methodological competence. 
Hence, if they question some aspects of your work, there is a fair chance 
that the readers of the outlet you have chosen to publish in will as well.
Keep in mind that reviewing is a thankless and unpaid task. Even 
though we have, on occasions (though extremely rarely), encountered 
some reviewers who did not understand our work, the other reviewers 
appointed at the same time usually allowed the associate editor to 
minimize the impact of the “bad” reviewer and our work was eventu­
ally published. Do not be discouraged if and when your work is 
rejected. This is part of the learning process, and we all learn much 
more through being rejected than accepted. As long as your work is 
reviewed, even if it is eventually rejected, you can draw some valuable 
lessons from this process and keep improving your output.
Not Quite Ready . . .
The paper we gave as an example earlier in this chapter (Walsh et al., 2013), 
whose results were eventually voted by a scientific board as most important 
for both the research and practitioner communities, was ­
previously rejected 
by another top-tier journal.  
At the time of this first submission and subse­
quent rejection, it just was not ready for publication and the research flow 
was still obviously difficult to follow. However, through this first ­
rejection, 
and the detailed reviews that accompanied the rejection, we learned a lot 
and were able to readjust our writing format to better suit a top-tier outlet.


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
150
Be humble and do not hesitate to call in, as a second author, some­
one with experience and know-how in a domain where your own 
competence is lacking. If you choose this person correctly, what you 
might learn in the process of coauthorship will be extremely valuable 
and might open some doors that you did not even know existed.
IN SUMMARY
Doing GT and writing GT with the goal of publishing in a research jour­
nal involve different processes. It is often necessary to organize the way 
we write our grounded theories to fit, at least approximately, the writ­
ing conventions of the outlets in which we choose to publish. All 
reviewers cannot be wrong, and we should remain humble. If one of our 
works submitted for publication is rejected, and if we want our works 
to be read and understood, we should take the opportunity to learn 
from, rather than refuse, the judgment of our peers.
TEST YOUR KNOWLEDGE
In the following questions one or several answers may be correct.
1.	 In order to publish a grounded theory in a research outlet
a.	 You should forfeit your philosophical assumptions
b.	 You should unconditionally fit in the research outlet’s ­
writing 
conventions
c.	 You do not have to show that you have followed all of GT’s 
foundational guidelines
d.	 None of the above
	
2.	 Writing GT for an audience
a.	 Has to be done through the GT process
b.	 Has to faithfully describe the full GT process
c.	 Has to be done in iterative cycles
d.	 None of the above
	
3.	 Writing conventions
a.	 Are the same for all research outlets
b.	 Are all clearly stated in writing by research outlets
c.	 May be identified partially through the publications of 

each outlet
d.	 None of the above


Chapter 9    Writing Classic GT for Publication
151
	
4.	 Writing for an audience
a.	 Is always a linear process
b.	 Accounts for findings in a mostly linear fashion
c.	 Is usually helped by regular exposure of results to one’s peers
d.	 None of the above
FURTHER READING
Richardson, L. (Ed.). (1990). Writing strategies: Reaching diverse audiences 

(Vol. 21). Newbury Park, CA: Sage.
Richardson, L. (2000) Writing: A method of inquiry. In N. K. Denzin & Y. S. 
Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp. 923–948), 
Thousand Oaks, CA: Sage.
ENDNOTE
1.	 The source of the data used in this section is the Scopus database, 
launched in 2004 by Elsevier. On its website, it claims to be “the largest 
abstract and citation database of research literature and select results 
from the web . . . [covering] 27 million abstracts, 230 million references 
and 200 million web pages.” In this database, we investigated works 
that include the term grounded theory in the title, abstract, or keywords.


152
1 0
Evaluating Classic Grounded 
Theory
After studying this chapter, you will:
• understand the criteria for evaluating classic grounded theory
• recognize the quality of a classic grounded theory by applying these 
criteria
D
ifferent approaches to research necessitate different criteria for 
evaluating the quality of the research outcomes. Establishing 
such criteria is a somewhat contentious aspect of the shifting 
nature of research paradigms.
l
l
THE CONSEQUENCES OF THE PARADIGM WAR
Patton (2002) refers to the traditional dichotomy in research approaches 
as competing inquiry paradigms: “using quantitative and experimental 
methods to generate and test hypothetical-deductive generalizations 
and using qualitative and naturalistic approaches to inductively and 
holistically understand human experience and constructed meanings in 
context-specific settings” (p. 69). He acknowledges a range of qualitative 
research approaches with alternative criteria and concludes that issues 


Chapter 10    Evaluating Classic Grounded Theory
153
of quality and credibility in qualitative research relate to three concerns: 
rigorous methods, credibility of the researcher, and philosophical belief 
in the value of qualitative inquiry (p. 584).
Venkatesh, Brown, and Bala (2013) propose an integrative 
framework for mixed methods inference quality and provide several 
examples of validity in quantitative and qualitative research. For 
quantitative research, they propose to check on design validity (i.e., 
internal and external validity), measurement validity (i.e., reliability 
and construct validity), and inferential validity (i.e., statistical 
conclusion validity); for qualitative research, they propose to check on 
design validity (i.e., descriptive validity, transferability, and credibility), 
analytical validity (i.e., theoretical validity, dependability, consistency, 
and plausibility), and inferential validity (i.e., interpretive validity and 
confirmability). However, their work still appears somewhat biased by 
the paradigm war that we described in Chapter 2; also it cannot be 
applied “as is” to evaluate classic grounded theories. For instance, 
external validity, that is, “whether the cause-effect relationship holds 
over variation in persons, settings” (p. 13), does not hold for a classic 
grounded theory. External validity will depend on the researcher’s 
philosophical stance and if the investigated theory is a simple 
substantive theory bounded in time, space, and context; a general 
substantive theory that extends to a substantive area; or a conceptually 
formalized theory that extends to many different substantive areas.
As highlighted in Chapter 2 and discussed by Walsh (2014c) and 
Walsh and colleagues (2015b), we must be careful not to enroll in 
paradigmatic caricatures. We must not automatically assimilate the use 
of quantitative data with hypothetical-deductive research or the use of 
qualitative data with interpretive research. Guba (1990) refers to a 
paradigm dialogue, noting a plethora of alternative approaches to 
research that have emerged over the past decades to challenge 
conventional positivism: “United in what they oppose, they are 
nevertheless divided, sometimes sharply, on what they espouse” (p. 9). 
To engage further in this dialogue is beyond the scope of this text, but 
given the extensive use of qualitative data in GT, novice researchers 
can find themselves attempting to justify their work in relation to a 
menu of criteria. As for the use of quantitative data, or mixed data, with 
an exploratory stance, it is still so rare in many fields that researchers, 
even experienced ones, who adopt this path may also find themselves 
in need of justification for their work to be understood and validated. 
Therefore, here, we focus our attention on exploring criteria for 
evaluating the quality of a classic grounded theory with any type of 
data, qualitative and/or quantitative, and from any philosophical 


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
154
stance that a researcher may choose (although such a stance should be 
clearly stated early on in a GT work to enlighten the reader and make 
the work transparent).
l
l
CRITERIA FOR EVALUATING GROUNDED THEORY
First, let us consider some general criteria for evaluating the quality of 
theory-building research. Bacharach (1989) contends that “the goal of 
theory is to diminish the complexity of the empirical world on the basis 
of explanations and predictions” (p. 513). Eisenhardt (1989) reminds us 
that strong theory-building research should result in new, even frame-
breaking, insights and that a good theory “should display enough 
evidence for each construct to allow readers to make their own 
assessment of the fit with theory . . . a strong theory-building study has 
a good, although not necessarily perfect, fit with the data” (p. 548). While 
noting that theory is always provisional, Bacharach (1989) cautions,
To dangle criteria above the head of a theorist like the Sword 
of Damocles may stifle creativity. . . . However, during the 
early stages of theory building, there may be a fine line 
between satisfying the criteria of the internal logic of theory 
and achieving a creative contribution. A good theorist walks 
this line carefully. (p. 513)
Whetten (1989) contends that “sensitivity to the competing virtues 
of parsimony and comprehensiveness is the hallmark of a good 
theorist” (p. 490).
While appreciating the perspectives of these experienced 
methodologists, it is important to remember that GT’s distinct research 
paradigm sits outside the “classically opposed orthodoxies” (Patton, 2002, 
p. 69). Our consideration should, therefore, begin by recalling the nature 
and purpose of GT: to systematically generate theory from data as an integrated 
set of concepts and as hypotheses about the interrelationships among these 
concepts that focus a main issue or concern in the area under study and explain 
how that issue or concern is processed or resolved. Glaser (1998, 2005a) refers 
to GT as asymptotic and emphasizes its propositional nature; it never 
fully reaches truth but the more data analyzed, the more plausible the 
generated theory. As an exploratory approach, GT’s credibility cannot be 
judged on the basis of established knowledge. Its relevance comes from 
data, not extant literature. “The important question is the usefulness of the 
theory that has been generated” (Baker, Wuest, & Stern, 1992, p. 1359).


Chapter 10    Evaluating Classic Grounded Theory
155
O’Connor, Netting, and Thomas (2008) note the intent of grounded 
theory is emergent understanding, not testable theory, and suggest 
“the easy advice for those facing an IRB [Institutional Research Board] 
would be to demonstrate the clearest standards available, those 
suggested by its original developers” (p. 36). Glaser and Strauss (1971) 
offered four specific criteria for evaluating grounded theories: “theory 
that fits the real world, works in predictions and explanations, is 
relevant to the people concerned, and . . . is readily modifiable” 

(p. 176). Charmaz (2006, 2014) proposes the criteria of credibility, 
originality, resonance, and usefulness.
Glaser (1992, pp. 116–117) reiterates the four criteria of fit, 
workability, relevance, and modifiability and later elaborates, as 
follows, to define these terms and propose related questions that may 
be asked to verify the quality of a classic grounded theory:
1.	 Fit is another word for validity. Does the concept adequately 
express the pattern in the data which it purports to conceptual­
ize? Fit is continually sharpened by constant comparisons.
2.	 Workability means do the concepts and the way they are related 
into hypotheses sufficiently account for how the main concern of 
participants is resolved?
3.	 Relevance makes the research important, because it deals with the 
main concerns of the participants involved. To study something 
that interests no one really or just a few academics or funders is 
probably to focus on non-relevance or even trivia for the partici­
pants. Relevance, like good concepts, evokes instant grab.
4.	 Modifiability is very significant. The theory is not being verified 
as in verification studies, and thus never right or wrong . . . it 
just gets modified by new data to compare it to . . . New data 
never provides a disproof, just an analytic challenge. (Glaser, 
1998, pp. 18–19)
In exploring these criteria further, we might draw some comparison 
of fit with notions of validity. As Glaser and Strauss (1967) state,
the theory should provide clear enough categories and 
hypotheses so that crucial ones can be verified in present and 
future research; they must be clear enough to be readily 
operationalized in quantitative studies when these are 
appropriate . . . that the categories must be readily (not forcibly) 
applicable to and indicated by the data under study. (p. 3)


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
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We might compare GT’s criterion of workability with external 
validity and transferability (i.e., the theory is sufficiently abstract to 
transcend specific empirical incidents for theoretical generalizability). 
Indeed, replication is built into the research process through theoretical 
sampling and constant comparisons (Glaser & Strauss, 1965a, p. 290).
GT’s criterion of relevance speaks to the practical value or utility of 
the theory (Patton, 2002, p. 588). Its relevance, credibility, and 
confirmability are, of course, affirmed through its empirical grounding 
in data and the “conceptual grab” (Glaser, 1978, p. 100) of the theory. 
Concepts convey credibility when they are both “analytic—sufficiently 
generalized to designate the properties of concrete entities (not the 
concrete entities themselves)” and “sensitizing—they yield a 
‘meaningful’ picture—with apt illustrations” so that the reader can 
“feel that vicariously he has been in the field” (Glaser & Strauss, 1965a, 
p. 291). We might align criteria of resonance and usefulness (Charmaz, 
2006, 2014) here as well. Bacharach (1989) suggests that “a theory is 
useful when it can both explain and predict” (p. 501). Glaser (1963) 
suggests that plausibility is sufficient in developing theory; that “to 
develop, not test, a model, it is sufficient to explore plausible relations 
between variables, and not necessary to build a strong case on hard 
fact” (p. 381). Glaser and Strauss (1965a) suggest that any grounded 
theory can be “applied and adjusted to many situations with sufficient 
exactitude to guide thinking, understanding and research” (p. 292).
GT’s emphasis on the modifiability of a good grounded theory is 
perhaps unique in expressly acknowledging its propositional nature as 
distinct from the verificational nature of hypothetical-deductive theory 
building and from the detailed descriptive nature of many qualitative 
research works. While contending that the primary goal of a theory is 
“to answer the questions of how, when, and why” (Bacharach, 1989, p. 
498), Bacharach insists that a theory that is not testable “no matter how 
profound or aesthetically pleasing it may be, it is not a theory” (p. 512). 
Indeed, while insisting that the goal of GT is not hypothesis testing, 
Glaser and Strauss (1967) maintain that “evidence and testing never 
destroys a theory (of any generality), they only modify it. A theory’s 
only replacement is a better theory” (p. 28).
To the criteria of fit, workability, relevance, and modifiability we 
would echo Glaser and Strauss (1967) in noting the criteria of “logical 
consistency, clarity, parsimony, density, scope [and] integration” (p. 3) 
as indicators of a good grounded theory. Density refers to levels of 
conceptualization, development of theoretical properties, and overall 
conceptual integration (Glaser & Strauss, 1971, p. 190). Bacharach 


Chapter 10    Evaluating Classic Grounded Theory
157
(1989) notes that “the purpose of theoretical statements is twofold: to 
organize (parsimoniously) and to communicate (clearly)” (p. 496). 
Indeed, Glaser and Strauss (1967) acknowledge the importance of the 
“parsimony of variables” (p. 110) through GT’s delimiting the 
emergent theory to a core category and related concepts. Eisenhardt 
(1989) reiterates this call for parsimony, cautioning that in studies 
with a “staggering volume of rich data, there is a temptation to build 
theory which tries to capture everything. The result can be theory 
which is rich in detail, but lacks the simplicity of overall perspective” 
(p. 547). Bacharach (1989) defines scope as “the range of the 
phenomena encompassed by the theory” (p. 509), suggesting that “for 
adequate scope . . . the constructs must, in turn, sufficiently, although 
parsimoniously, tap the domain of the phenomenon in question” 

(pp. 506–507).
IN SUMMARY
In this chapter, we have explored the distinct criteria for evaluating a 
classic grounded theory that go beyond the accepted criteria for both 
hypothetical-deductive and qualitative research. Classic GT’s criteria 
of fit, workability, relevance, modifiability, and parsimony facilitate the 
generation of theory with grab, or what Charmaz (2006, 2014) proposes 
as originality. Of course, like all efforts at theory generation, the quality 
of a classic grounded theory will vary according to the theorist’s under­
standing and thoroughness in applying GT principles and techniques, 
the theorist’s ability to stay open to discovery in the data, the incisive­
ness of analysis, and the clarity and elegance with which the theorist 
writes up his or her theory.
TEST YOUR KNOWLEDGE
In the following questions one or several answers may be correct.
1.	 Criteria for evaluating classic grounded theories
a.	 Include objectivity, generalizability, and reliability
b.	 Are similar to criteria for interpretive research
c.	 Emphasize modifiability and parsimony
d.	 Include fit, workability, and relevance
e.	 None of the above


WRITING AND EVALUATING CLASSIC GROUNDED THEORY
158
2.	 In a good classic GT
a.	 The concepts express patterns in the data
b.	 Emerging concepts and their relationships account for how 
the main concern is resolved
c.	 The concepts have grab
d.	 The theory may be modified by new data
e.	 None of the above
3.	 In a good classic GT
a.	 Replication is built into the research process
b.	 The theory does not need to have practical value
c.	 It is sufficient to explore plausible relations between ­
variables
d.	 It is necessary to build a strong case on hard facts
e.	 None of the above
FURTHER READING
Glaser, B. G. (1978). Theoretical sensitivity: Advances in the methodology of grounded 
theory (Chapter 1). Mill Valley, CA: Sociology Press.
Glaser, B. G. (2003). The grounded theory perspective II: Description’s remodeling of 
grounded theory methodology (Chapter 9). Mill Valley, CA: Sociology Press.
Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies 
for qualitative research (Chapter 9). New York: Aldine de Gruyter.


Conclusion
M
any authors have, over the years, written methodological 
books about GT. In so doing, they have often reinterpreted 
and “remodeled” GT (Glaser, 2003) from their own philo­
sophical perspectives. In this book, our purpose was to revisit classic 
GT as a full package while remaining as true as possible to its founders’ 
thoughts. We have reminded the reader of the roots of GT (Chapter 1) 
and the ultimate purpose of GT, that is, discovering theories no matter 
what the researcher’s philosophical assumptions may be (Chapter 2). 
We have highlighted GT’s foundational pillars (Chapter 3), clarified 
common sources of confusion related to GT (Chapter 4), helped the 
reader to find and use all available data (Chapter 5), analyze the data 
(Chapter 6), shape the emerging theory (Chapter 7), and write as both 
an intrinsic part of doing GT (Chapter 8) and also toward publishing 
in top tier research outlets (Chapter 9). Finally, we have offered 

the reader advice on evaluating the quality of grounded theories 
(Chapter 10).
Throughout this book, we hope to have shown that GT is much 
more than a qualitative research methodology befitting only an inter­
pretive philosophical stance, even though it may accommodate such 
a stance. We have aimed to provide practical guidance while staying 
true to the classic GT practice. However, it must be highlighted that, 
as our own philosophical stance is critical realist, it is most probable 
that, unwittingly we may have, at least partially, remodeled GT that 
is supposed to be philosophically neutral. In the same way, the 
frameworks proposed by Strauss and Corbin (1990) or Charmaz 
(2014) are epistemologically and methodologically biased interpreta­
tions and applications of GT. We do not call for a reconsideration of 
the intrinsic worth of these works, which might suit the philosophi­
cal assumptions of some researchers. However, grounded theory 
being a grounded theory itself as often stated by Glaser, it has to be 
highlighted that these frameworks are at a lesser level of conceptual 
159


CLASSIC GROUNDED THEORY
160
formalization than that of classic GT (Glaser, 1978; Glaser & Strauss, 
1967), a fact that is often misunderstood and which Glaser has been 
defending for many years.
To give a few illustrative, non-limitative examples, Strauss and 
Corbin (1990) and Charmaz (2009, 2014) propose frameworks that use 
mostly qualitative data—the former with a symbolic interactionist 
philosophical background, the latter with a constructivist philosophical 
background. Glaser (2008) uses quantitative data with a post-positivist 
background. Walsh (2014c) uses mixed qualitative and quantitative 
data with a critical realist background. All these frameworks have 
been empirically developed through doing and supervising various 
research projects with emergent substantive GT research designs 
(GTDs) and have probably and unwittingly remodeled, at least to 
some extent, classic GT, whereas Holton (2006, 2007b) uses qualitative 
data and develops a substantive GT design in her works, from a 
critical realist perspective in line with classic GT, without remodeling 
it to become a different framework. This highlights the possibility of 
viewing classic GT as a meta-theory of inductive research design 
(Walsh, 2014c).
Classic GT
(Glaser, 1978; 
Glaser and Strauss, 1967)
Mixed data
Meta-Theory
of Research
Design
GT
Frameworks
Substantive
GT Designs
(GTDs)
“All is data”
Qualitative data
Strauss and
Corbin’s (1990)
Framework
Charmaz’s
(2009)
Framework 
Quantitative
data
Glaser’s (2008)
Framework
GTD
GTD
GTD
Larsen’s
(2003)
GTD
Walsh et al.’s
(2010) Walsh and
Gettler-Summa’s
(2010) GTD
GTD
Walsh's (2014c)
Mixed Typological
Framework
Epistemological
Background
Symbolic
Interactionism
Constructivism
Post-
Positivism
Critical
Realism
Conceptual Formalization of GT
Holton (2006,
2007b) GTD 
GTD
GTD
GTD
GTD
GTD
GTD
Figure 11.1.    
Classic GT as a Meta-Theory of Inductive Research 
Design (Adapted from Walsh, 2014c)


	
WHAT IS GT, AND WHY THE CONTINUAL REMODELING?	
l
This naturally leads to the question as to what classic GT truly is; a 
question that has recently been openly asked (see Walsh et al., 2015a, 
2015b, and the related special issue of Organizational Research Meth­
ods). As we saw, many different answers to this question may be 
proposed. In various fields of research, GT has been described as a 
technique (e.g., Lawrence & Tar, 2013), a method (e.g., Amsteus, 2014), 
a methodology (e.g., Manuj & Pohlen, 2012), or a paradigm (e.g., 
Holton, in Walsh et al., 2015b; Levina, in Walsh et al., 2015b; Rodrí­
guez-Martin et al., 2013). Based on Glaser (1978), Fernandez (in 
Walsh et al., 2015b) describes GT “as a basic social process in which 
researchers engage with their data and participants in creating the­
ory.” Bailyn (in Walsh et al., 2015b) sees GT as a “perspective on data 
and on what one can learn from data.” Finally, Walsh (2014c; Walsh 
et al., 2015b) proposes GT as a meta-theory of exploratory research 
design.
Is GT a method, a technique, a methodology, a framework, a para­
digm, a social process, a perspective, or a meta-theory of research 
design?
GT is probably all of these at the same time. As such, and 
depending on the researcher’s perspective, it will keep being remodeled. 
However, if we go back to the four possible different definitions of the 
word paradigm highlighted in the preface of this book, Morgan (2007) 
showed that all are a “shared belief system that influences the kinds of 
knowledge researchers seek and how they interpret the evidence they 
collect” (p. 50). Therefore, it may certainly be stated that, because of its 
“transcendent” quality (Glaser, 2004), classic GT is a research paradigm 
befitting the discovery of new theories from any philosophical 
perspective.
Conclusion 
161


Appendix A
Doing Qualitative Research Does Not Mean You Are 
Doing Classic GT
Note the precise, planned-in-advance sample; the attention to complete 
data capture through use of a preformulated interview guide, taping, 
and transcribing; the abundance of codes generated from line-by-line 
coding of the transcripts; the logical and thematic organization of the 
resultant codes; and the descriptive (past tense) reporting of findings.
Excerpt From the Study Design
I selected eight individuals from the community for personal 
interviews. Those selected included four males and four females; six 
had participated in the focus group at the May reunion while two—
one male and one female—had been unable to attend. Interviews 
were semistructured in that I used a set list of questions but 
encouraged the interviewees to take the conversation where they 
wished. Each interview lasted from 60 to 90 minutes and was audio-
taped and transcribed. In addition to line-by-line coding the eight 
interview transcripts, I also completed coding of the focus group 
transcript. This initial coding was free form and highly intuitive and 
yielded 155 codes.
After reviewing my coding, some duplication was evident. I 
reviewed the codes and grouped them to remove duplications, 
reducing the list to 75 coded concepts. I then identified three 
overarching categories that I labeled Aspects of Community, Aspects of 
Practice, and Aspects of Participation. I explored the correlation of the 
various codes to the three overarching categories and assigned 

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QUALITATIVE DESCRIPTION DATA COLLECTION AND ANALYSIS1
162


Doing Qualitative Research Does Not Mean You Are Doing Classic GT
163
43 codes to the Aspects of Community category, 38 to the Aspects of 
Practice category, and 24 to the Aspects of Participation category.
Beginning with the interviews, I focused my attention to the most 
frequent codes in each category. I selected those coded to at least 12 of 
the 18 interviews to represent a reasonable distribution of individual 
member perceptions within the data. Based on this criterion, I was able 
to narrow the selection of codes from 75 to 42: 26 correlating to Aspects 
of Community, 10 correlating to Aspects of Practice, and 6 correlating to 
Aspects of Participation.
Of the 26 concepts correlating to Aspects of Community, I designated 
five as subcategories—Community Identity, Bunch of Emotions, 
Connectedness, Commitment, and Power of this Community. Under these 
five subcategories I thematically distributed the remaining 21 concepts. 
Similarly, under the category Aspects of Practice, I designated three 
concepts as subcategories—Allowed Me to Grow, Sharpening the Saw, and 
Play—and thematically distributed the remaining seven concepts 
among these three. Under Aspects of Participation, I designated one 
concept as a subcategory—Participation in Community—and assigned 
the remaining five concepts to this subcategory.
Excerpt From the Study Findings
Members referred to the reassurance they felt in being part of a tight-
knit family “able to get together, tell old war stories, create new 
experiences, make new friends” (A.M., Interview, October 2001) and “a 
place where egos and agendas don’t seem to get in the way of genuine 
dialogue and caring for one another” (L.G., Interview, October 2001). 
Images emerged of good times, celebrations, vivid memories, and 
powerful connections. Shared stories and memories reinforced the 
closeness. As in a family, members described the sense of belonging as 
important: “knowing that I can wander into the group 20 years from 
now and be accepted as one of the gang. I’ve found a community where 
I believe I am liked as much for my loony-ness as for my creativity . . . I 
guess that for me the community, the fun, and the belonging is 
paramount” (J.M., Interview, October 2001).
Our findings suggest that community identity is forged through 
shared experiences: “I know that my experience in the community has 
shaped how I will live the second half of my life. I want to stay 
connected to others who have that same mid-life grounding point” 
(M.S., Virtual Interview, October 2001). “It was a slog at times. . . . I was 
grateful that I have been through some tough times in my military 
career, because that gave me the ability to see the slog as something 


CLASSIC GROUNDED THEORY
164
that bound us all together. We got through it because of the fun after 
and during class as well as by having the stamina to suffer the late 
nights, the lack of sleep in the residencies and the expectations on us to 
produce” (J.M., Interview, October 2001).
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CLASSIC GT DATA COLLECTION AND ANALYSIS2
Note the tentative and emergent approach to sampling, the use of field 
notes to capture data, the use of constant comparison and theoretical 
sampling to progress theorizing, the emergence of a core category to 
explain resolution of the main concern, and the use of theoretical 
saturation and theoretical coding to fully develop the emergent 
explanation. Note also that the theory is written conceptually and in 
the present tense.
Excerpt From the Study Design
I had initially projected my fieldwork to involve approximately 
50 participants. In accordance with GT methodology however, 
sample size could not be definitively established in advance of the 
fieldwork but rather emerged as the study progressed. In summary, 
61 individuals participated in the study: 27 through personal 
interviews and 34 individuals in focus group sessions. Interviews 
lasted from 60 to 90 minutes each. The focus groups occurred during 
two retreats, each lasting three days. These events were not organized 
specifically for my research efforts but were events to which I was 
invited by individuals who, because they had already participated in 
interviews, were aware of my research and felt that I might gain 
further data for my study.
Field notes were written up directly during and after each 
interview or focus group session. Consistent with the principles of 
classic GT development, field notes were collected and analyzed 
according to the constant comparison method of joint coding and 
analysis with subsequent data collection directed to theoretical 
sampling as concepts emerged. In open coding of my data, I first 
compared incidents to incidents in the data, and I would ask, “What 
category (concept) does this incident indicate?” “What property of 
what category does this incident indicate?” and “What is the 
participant’s main concern?” (Glaser, 1998, p. 140). These questions 
enabled me to transcend the descriptive detail of the data and abstract 
incidents within my data as indicators of the latent pattern of social 
behavior that would eventually emerge as a conceptual theory.


Doing Qualitative Research Does Not Mean You Are Doing Classic GT
165
Among the substantive codes that emerged were changing workplace 
context, coping with change and humanizing workplace. The important 
thing is that each concept has earned relevance in relation to the theory, 
its relevance theoretically sampled for and sufficiently validated and 
its properties and dimensions identified though constant comparison 
and interchangeable indicators to theoretical saturation. As my analysis 
progressed, rehumanizing emerged as a potential core category. It 
appeared to account for much of the variation around knowledge 
worker concerns with the changing knowledge workplace and resultant 
dehumanization. Accordingly, rehumanizing became the focus of further 
selective data collection and coding efforts.
Subsequent data collection and coding were thereby further 
delimited to that which was relevant to the emergent conceptual 
framework (Glaser & Holton, 2004, para. 56–57). Selective data 
collection and analysis continued until the core and related categories 
were sufficiently saturated and further coding of new incidents did not 
yield any new properties. It was time to cease collecting and coding 
data and to turn to conceptual integration and articulation of the 
emerging grounded theory.
Excerpt From the Emergent Theory
The instability of many workplace environments results in a loss 

of autonomy and even identity for many knowledge workers. 

The concept of rehumanizing explains how knowledge workers 
resolve their concerns with the dehumanizing impact of a changing 
knowledge workplace, that is, how they restore the human dimension 
in their work relationships and working environments. Rehumanizing 
gives meaning to their work while sustaining energy and commitment. 
Rehumanizing is characterized by authenticity, depth and meaning, 
recognition and respect, safety and healing and kindred sharing. The 
bureaucratic relationships in many knowledge workplaces lack 
authenticity. They are power-laden transactions. Whether the power 
can be attributed to position, influence or alliance, the tensional shifts 
can leave individuals feeling uneasy, anxious, and without a base or 
grounding. The need to play roles in organizations and to assume 
corporate identities also leads to inauthentic voices, disconnecting 
individuals from what they really feel. Identity and purpose in work 
are eroded as “everyone plays the script.” With the increasing 
intensification and specialization of knowledge, workers feel siloed. 
The time available for more general interactions is reduced. Removing 
opportunities for broad interpersonal contact further dehumanizes the 


CLASSIC GROUNDED THEORY
166
work environment and reduces the potential for authentic engagement. 
Many highly skilled knowledge workers risk vulnerability in moving 
up in the organization to take on managerial roles that progressively 
distance them from their areas of specialized expertise. There is a risk 
of vulnerability, as well, in emotional disclosures in the workplace. 
Individuals frequently feel they have to restrict emotional displays or 
run the risk of being seen as weak, a vulnerability that can restrict 
career progression. Emotions are held in check and authenticity 
compromised.
ENDNOTES
1.	 Holton (2001). Unpublished case study.
2.	 From Holton (2006).


Appendix B
Classic GT Using Qualitative Data
T
he following is based on Holton (2006)1 and elaborates her 
application of classic GT methodology, in particular the process 
of concept development, to her generation of a grounded theory 
of rehumanizing knowledge work through fluctuating support networks in 
the knowledge workplace.
	
THE DATA	
l
My data consisted of field notes from personal interviews, casual 
conversations with knowledge professionals, and observation of 
various focus group gatherings. Participants were drawn from the 
public and private sectors and a variety of professional fields under the 
general rubric of knowledge work. In summary, 61 individuals 
participated in the study: 27 through personal interviews (13 face-to-
face interviews, 12 virtual interviews via e-mail, and 2 telephone 
interviews) and 34 individuals in focus group sessions. In addition, I 
encountered opportunities in the field to augment the interview and 
focus group data with participant observations of informal networks as 
well as document analysis. Data were analyzed using the full 
complement of procedures that comprise classic grounded theory 
methodology. In accordance with classic GT, concepts emerged through 
the process of constant comparison of interchangeable empirically 
grounded indicators.
167


CLASSIC GROUNDED THEORY
168
My thesis explains the basic social structural process of fluctuating 
support networks through which knowledge professionals self-organize 
to overcome dehumanized work environments as well as the social 
structural conditions that precipitate their formation, namely, the 
changing knowledge workplace, coping with change, resistance to change, 
dehumanization, and rehumanizing. Such networks operate outside the 
formal organization. They are self-emerging, self-organizing, and self-
sustaining. Participation is voluntary and intuitive. The core category 
is rehumanizing, a three-stage basic social psychological process through 
which knowledge professionals engage in finding and likening, igniting 
of passions, and mutual engagement, giving meaning to their work while 
sustaining their energy and commitment. Rehumanizing restores 
authenticity, depth, and meaning to their work; it offers them recognition 
and respect, safety and healing, and kindred sharing as they cope with the 
turbulence that inevitably accompanies persistent and unpredictable 
organizational change. In the process, they are reenergized, their passion 
for work is reignited, stimulating creativity, the desire to embrace 
challenge, experimentation, and innovation.
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l
THE EMERGENT THEORY
l
l
STAYING OPEN
Early in my research process, I had to learn the hard way that my 
research interest was not the main concern of the knowledge 
professionals that I was interviewing. Like many novice researchers 
wishing to do GT, I initiated data collection focused on a preconceived 
professional interest. I developed an interview protocol and diligently 
taped and transcribed the first five interviews. As I sat transcribing, I 
was struck not only by how much time the transcribing was taking but 
also by how dull the interviews were. While my interviewees were 
completely respectful of my research and dutifully tried to answer each 
question posed, it was clear to me that they had little genuine interest 
in my topic. We were all doing our best, but I found myself despairing 
and anxious as to how any of this was going to produce a theory! My 
early efforts at open coding of these data, heavily influenced by 
qualitative data analysis procedures, had yielded 155 substantive 
codes—data overwhelm!
I went back to further study GT methodology, particularly the work 
of Barney Glaser (1978, 1998), and realized that my approach was 
privileging my professional interest and not allowing the main concerns 


    Classic GT Using Qualitative Data
169
of my participants to surface. So I set aside those first five interviews and 
started over to collect new data without an interview protocol and without 
taping. My first two “new” interviews were with managers in a public 
sector organization. Rather than launching into a long preamble about my 
research interests, I simply asked them to tell me about their jobs. I listened 
for what my participants wanted to tell me. I followed up with the 
question, “What concerns you most in your role?” That’s all it took to open 
the “flood gates.” In each case, the interviewees started voicing concerns 
about organizational change and how the nature of change was impacting 
their ability to manage the motivation and productivity of their staff. I 
jotted down key words and phrases during each conversation (e.g., 
fragmented knowledge pockets, structures eroding, people marginalized, 
how to free people up, opening people up to possibility).
Immediately after each meeting, I rushed to a nearby coffee shop 
and started jotting down more detailed field notes using those key 
words and phrases to remind me of what had emerged in our 
conversation. What these managers wanted to talk about wasn’t what 
I had set out to research, but I trusted the process. I coded my field 
notes and memoed. I struggled to let go of my preconceived research 
interest and simply to trust what was emerging from the data, but as I 
continued collecting and coding data, this main concern (changing 
workplace context) emerged over and over.2
	
OPEN CODING	
l
With my more open field notes, and using the constant comparison 
process, open coding led to a much reduced list of 57 substantive codes. 
It is worth noting that this latter list of codes is substantially smaller and 
more conceptual than that produced in my earlier efforts. This illustrates 
not only the potential for overwhelming the novice researcher with 
descriptive data capture but also the very real possibility of losing the 
true conceptual power of classic GT to transcend the descriptive level 
(Glaser, 1978, pp. 49–52; 1998, pp. 23–26).
	
CONSTANT COMPARISON	
l
The purpose of constant comparison is to see if the data support and 
continue to support emerging categories. At the same time, the process 
further builds and substantiates the emerging categories by defining 
their properties and dimensions. When comparing incidents to incidents 
in the data, I would ask, “What category (concept) does this incident 


CLASSIC GROUNDED THEORY
170
indicate?” “What property of what category does this incident indicate?” 
“What is the participant’s main concern?” (Glaser, 1998, p. 140). These 
questions enabled me to transcend the descriptive detail of the data and 
abstract the incidents within as indicators of latent patterns of social 
behavior that would eventually emerge as a conceptual theory.
Each concept is developed through constant comparison of 
interchangeable (empirical) indicators in the data. The number of 
indicators per category is not as significant as the requirement to 
sample sufficiently to achieve theoretical saturation. The important 
thing is that each concept has earned its relevance in relation to the 
theory—relevance that is theoretically sampled for and sufficiently 
validated (theoretically saturated).
Example of the Constant Comparative Method
1. Comparing Incident to Incident
In coding my interviews, I noted several references like these:
. . .  
a place where she found 
“like minds,” common passion, and where there 
was a building of trust . . .  
authentic connection . . .  
Through her subsequent 
participation, she is “getting to really know others. . . . Building connections”
“ . . . way of hearing quiet voices in organizations” . . . authentic voices in 
organizations rather than corporate voices
. . . corporate relationships are not authentic, they are transactional/
power related . . . Where do we find the experience of being ourselves in 
organizations? Where do we learn to be human?
. . . having a chance to “be heard,” to share with others who understand, 
who they can trust to help and to hold confidences
. . . the corporate model that most of us work in now squeezes out our 
humanity. We develop machine relationships—even odd corporate voices 
. . . manner of speaking . . . a “dead sound” where our real personality has 
been excluded, as has emotion and feeling
C. and R. bring that wonderful warmth and spontaneity to the 
community—nothing can be dull when they’re present—lots of depth and 
experience there—looking forward to getting to know both much better
By comparing incident to incident, I developed the concept of authenticity.
2. Comparing Concept to More Incidents
As I continued to code my data, I began comparing new incidents in the data 
with my concept of authenticity. I wrote memos to conceptually elaborate 
my ideas about the concept. Memos such as the following:


    Classic GT Using Qualitative Data
171
(Memo 1) The instability characteristic of current organizational 
environments results in a loss of autonomy and even identity for many 
knowledge workers. Power shifts leave individuals feeling “at sea”—uneasy, 
anxious, without a base or grounding.
(Memo 2) The prevalence of the “machine” metaphor in organizations 
has dehumanized work environments. Many workers actually begin to view 
themselves as “cogs” in a wheel and begin to respond accordingly with 
automatic, mindless behaviors that further disconnect them from their 
work. As one interviewee said, “It appears that the corporate model that 
most of us work in now squeezes out our humanity. We develop machine 
relationships—even odd corporate voices . . . a manner of speaking a ‘dead 
sound’ where our real personality has been excluded as has emotion and 
feeling.” Another interviewee commented, “This machine world is causing us 
to become ill and depressed . . . We act in this impersonal and unreal way in 
our whole lives. We even act like this to ourselves and no longer have a real 
relationship with ourselves. How can we learn and experience being human 
again? 
What is the essence of being human? It is surely to hear our real voice.”
(Memo 3) The need to play roles in organizations—to assume corporate 
identities leads to inauthentic voices. This creates disconnect between what 
individuals really feel and what they feel they must voice. It erodes identity and 
connection with purpose in work. Individuals put on armor to protect and 
to project. After a while nothing feels real—people may long for a return to 
connecting with what they value/what they are passionate about. 
They long for 
a place to be real, to feel safe to express their authentic selves, to explore, to 
play, to risk . . . a place where they can relax, release tensions, open up.
(Memo 4) Fluctuating networks offer a respite from time crunched, 
transactional, efficiency-driven work environments where there is little 
tolerance or time for the curiosity, exploration, and fun. What is being 
sacrificed? Not only the potential for creativity and innovation but the very 
humanity-centered nature of vocation—the joy in creating and problem 
solving through collaborative engagement. The Xerox technicians story 
(Orr, 1990) . . . On their own time, they found a way to integrate the social 
and technical aspects of their vocation. They raised the solving of work-
related problems to an art. 
Valuable, tacit knowledge was transferred almost 
effortlessly among the technicians in an open environment where knowledge 
sharing was the power—building individual reputations (“war stories”) while 
enhancing organizational performance in service to clients. The exercise 
raised the identity of 
“master technicians,” provided a process for recognizing 
and sharing expertise, bringing new technicians into a culture of sharing and 
learning—a culture of mutual benefit—an inclusive meritocracy.
(Continued)


CLASSIC GROUNDED THEORY
172
l
l
CONCEPTUAL MEMOING
l
l
CORE CATEGORY EMERGENCE
l
l
THEORETICAL SAMPLING
In classic GT, conceptual elaboration and integration are facilitated 
through an extensive and systematic process of memoing that parallels 
the data analysis process. Memos are theoretical notes about the data 
and the conceptual connections between categories written up during 
the coding and analysis process as the ideation of the emerging 
theory’s conceptualization. During the course of the constant 
comparison process, I wrote more than 400 memos capturing the 
development of my theory.
I started collecting data in 2001. Rehumanizing emerged as a potential 
core category in September 2003. As a concept, it appeared to account 
for much of the variation around knowledge professionals’ concerns 
with the changing knowledge workplace and resultant dehumanization.
At this point, rehumanizing became the focus of theoretical sampling. 
Subsequent data collection and coding was thereby delimited to that 
which was relevant to the emergent core and related concepts, 
facilitating their conceptual elaboration through intense property 
development.
I theoretically sampled for authenticity to further elaborate the concept 
in terms of its properties. For example, openness and honesty, trust, sharing, 
being heard, safety, power, balance, and so on.
3. Comparing Concept to Concept
I began to compare authenticity with other concepts that were emerging, 
writing memos about the relationships to develop hypotheses that linked/
integrated these concepts. Eventually, through hand sorting of memos, 
authenticity would emerge as a property of my core category, rehumanizing.
(Continued)


    Classic GT Using Qualitative Data
173
Selective data collection and analysis continued on 38 substantive 
codes until the core and related categories were sufficiently saturated 
and further coding of new incidents did not yield any new properties. 
The research process had achieved theoretical saturation of the core 
concept—rehumanizing—and its related categories through constant 
comparison and interchangeability of indicators (Glaser, 2001, 

pp. 191–193). Further coding and constant comparison yielded no new 
conceptual ideation. It was time to cease collecting and coding data 
and turn to conceptual integration and articulation of the emerging 
grounded theory.
	
SELECTIVE CODING AND THEORETICAL SATURATION	
l
	
THE CONCEPTUAL ELABORATION OF IGNITING PASSIONS	
l
To illustrate the process of conceptual development, I offer here a 
detailed account of the emergence and theoretical saturation of one 
concept, Igniting Passions, the central stage in the basic social 
psychological process of rehumanizing.3 This same process was applied 
to each concept that emerged as relevant in relation to the core category 
of rehumanizing.
Igniting Passions is the catalytic middle stage in the basic social 
psychological process of rehumanizing, facilitating the symbiotic 
relationship, continuous amplification, and interdependent functioning 
of rehumanizing’s subcore processes of finding and likening and mutual 
engagement. The dynamic capacity of igniting passions sustains the 
overall rehumanizing process by continuously generating confidence, 
energy, commitment, and bonding among network members. A total of 62 
specific indicators of the concept of igniting passions appear in data 
collected from 15 interviews and two focus groups conducted between 
October 2001 and January 2004. A total of 22 memos were written 
between November 2002 and April 2004 elaborating the concept of 
igniting passions. The memos range in length from several lines to 
several pages.
As each incident can be an indicator of more than one concept, 
several of the memos correspond to more than one category or concept. 
Therefore, to facilitate hand sorting the memos (the final stage in 
conceptual integration of the theory), the corresponding categories are 
printed in bold type across the top of the memos. Each memo appears 
only once in the final theoretical integration of the theory, but its 


CLASSIC GROUNDED THEORY
174
appropriate placement was not known until all concepts had been fully 
saturated. I was ready to begin hand sorting of the memos for emergent 
theoretical codes.
The category passion appeared in open coding of data collected as 
early as October 2001. Early memos capture the dimensions of feedback 
and recognition and learning as being positively correlated with igniting 
passions for network engagement.
Conceptual Memos
Recognition Needs: Feedback and recognition fuel alignment of individual 
and organizational passions
Reciprocating Passions: Learning and knowledge sharing . . . Being selfish in 
terms of learning . . . Someone commented . . . 
 
“When we are (selfish), we learn 
to give to others—greedy to share . . .” “vessels” for fulfilling greedy learning/
sharing needs—reciprocal fulfillment of passions for earning and sharing. 

The accidental learning presented is a “smorgasbord” that rekindles the 
desire to learn—it also restores the learner’s control and builds autonomy 
self-select, sample, take what’s desired and needed. Restless spirits sated by 
continuous learning opportunities—integrated learning and practice
Power 
in 
diversity: 
 
 
Diversity 
of 
personalities, 
talents, 
perspectives, 
experience—
creates a bubbling energy and creative potential. 
The range of contributions . . . 

is immense—energy, passion, strength, vulnerability, commitment, opening up, 
connecting, authenticity, tenacity, validation, modeling, confirming, facilitating . . . 

This heady emotional/transformational release. . . . This may be applicable to 
networks and communities like _____ because of the nature of their domain/
practice leadership, learning, organizational culture. . . . It will be interesting to 
see whether any of this feeling shows up in their networks.
Additional dimensions of igniting passions emerged as data collection 
and analysis continued with igniting passions linked to learning as well 
as to vocation and to enhancing one’s professional skills.
Conceptual Memos
The Passion of Vocation: Networks as keeping personal and professional 
passions from being eroded, depleted in the hectic, humdrum of daily 
organizational operations. “Our job is our work . . . our practice is our passion.” 
Distinguishing between “practice” and “work”—between “vocation” and “job.”


    Classic GT Using Qualitative Data
175
Passionate Learning:  “really start to learn when they find a passion for a 
subject and then make a real connection to another learners and real time 
practitioners.” Individual passion for learning is stimulated and reinforced 
in community. Further data collection and analysis reveal the dimension of 
alignment and suggest that networks can assist in enabling individuals to 
align their personal purpose with their work. As such, passions are ignited 
through purposeful engagement.
Passion and Purpose: Networks may be a venue/vessel for individuals 
to be able to bring their passion back into their organizational roles—
experiences of participation speak of their reconnecting with previous 
“dream jobs,” core values, interests, and passions—this desire to reconnect 
passion and purpose may be a motivation for seeking/participating in 
networks/communities of practice.
Sustaining Passion: Networks as a way to find the people with a passion 
for organizational work . . . this is a simple principle of self-organization 
at work—allocating resources to follow the passion. She talks of the 
organizational challenge of getting past “assigned work” to matching 
assignments with passions–may not be worth the effort of trying to 
orchestrate this (i.e. manage the process)—that it may be better to let 
things take their course and be satisfied with “60%.” . . .
Letting the system self-organize . . . organic . . . “if no one has passion, 
we should not be doing it!” . . . organizations can use networks to 
monitor “passions” and set strategic directions accordingly . . . The 
human dimension—passion ignites passion—creates understanding and 
engagement—networks as a forum to mobilize passion into action . . . 
bringing diverse talents, skills together—mobilized by shared passion—
vision—system sorts itself/self organizes to achieve action—progress the 
work—enhance the practice.
Engagement: Concept of engagement coming up a great deal lately in 
a lot of the literature/press on workplace issues. Article in the Globe and 
Mail this week (March 5) by Barbara Moses titled  
“Embrace the Challenge: 
Be Engaged” on this issue noting importance of both satisfaction and 
engagement as key determinants of employee retention in the current 
environment. Another article in the Globe and Mail (same day, by Tara 
Wohlberg) on spirit in the workplace. One of the great advantages of the 
“knowledge age” is the seemingly endless potential for combining uniquely 
personal talents and passions into an unlimited array of new careers/roles. 
At the same time, we are suffering a 
“hangover” from the old mental models 
of the Industrial era which focus on efficiency, productivity, and competition 
(“push” factors/stressors).


CLASSIC GROUNDED THEORY
176
A longer memo begins to integrate emergent conceptual ideas.
Conceptual Memos
Igniting Passions: “. . . . passion. This is when you are really working with 
people—not just a “file” that you are responsible for.” In work involving 
“the human dynamic” it is essential to come together face to face—there 
is a cost benefit to doing so if it engages the passion of organizational 
members—we speculate about whether perhaps organizations should 
allocate resources on the basis of matching passions! . . .
“Try to develop a passion meter” . . . networks as a way to find the 
people with a passion for organizational work . . . this is a simple principle 
of self-organization at work—allocating resources to follow the passion.
Organizational challenge of getting past “assigned work” to matching 
assignments with passions—may not be worth the effort of trying to 
orchestrate this (i.e., manage the process)—that it may be better to let 
things take their course and be satisfied with “60%” . . . Letting the system 
self-organize . . . organic . . . “if no one has passion, we should not be doing it!” 
. . . organizations can use networks to monitor “passions” and set strategic 
directions accordingly . . . The human dimension—passion ignites passion—
creates understanding and engagement—networks as a forum to mobilize 
passion into action . . . bringing diverse talents, skills together—mobilized 
by shared passion vision—system sorts itself/self organizes to achieve 
action—progress the work—enhance the practice.
Core Group: These were the individuals who really had a passion . . . 
to go beyond the technical. They were “kindred spirits”—passionate. They 
are still friends even though most are no longer working in the training 
departments. . . . The task is done, but the community remains. They still 
have a passion for the work (learning) and are still personally connected. 
They stay in touch regularly—still goes to them when she 
“needs boosting.” 
There is one or two whom she uses as “sounding boards” and as sources 
of information and contacts.
Building Community as Central Value: Like minds, common passions, 
respect for diversity, trust—authentic connection—reciprocating—building 
capacity, strength, common good.
Power in Diversity: Diversity of personalities, talents, perspectives, 
experience—creates a bubbling energy and creative potential. The range 
of contributions is immense—energy, passion, strength, vulnerability, 
commitment, opening up, connecting, authenticity, tenacity, validation, 
modeling, confirming, facilitating . . . Practice is “deep,” heartfelt, engaging, 


    Classic GT Using Qualitative Data
177
transformational for individual and the group as a whole. . . . The fractal 
effect of networks—just imagine the potential to be able to release this 
within organizations! 
This heady emotional/transformational release . . . 
This 
may be applicable to networks like these because of the nature of their 
domain/practice leadership, learning, org culture . . . It will be interesting to 
see whether any of this feeling shows up in networks in other domains—
technology networks, etc. . . . Need to sample for this . . .
The Passion of Vocation: Networks as keeping personal and professional 
passions from being eroded, depleted in the hectic, humdrum of daily 
organizational operations. “Our job is our work . . . our practice is our passion.” 
Distinguishing between “practice” and “work”—between “vocation” and job.”
Passionate Learning: “really start to learn when they find a passion for a 
subject and then make a real connection to other learners and real time 
practitioners.” Individual passion for learning stimulated and reinforced in 
community.
Reciprocating Passions: Learning and knowledge sharing . . . Being selfish in 
terms of learning . . . Someone commented . . .”  
When we are (selfish), we 
learn to give to others—greedy to share.”
Networks as a “vessel” for fulfilling greedy learning/sharing needs 
reciprocal fulfillment of passions for learning and sharing. The accidental 
learning presented in Networks is a “smorgasbord” that rekindles the 
desire to learn—it also restores the learner’s control and builds autonomy 
self-select, sample, take what’s desired and needed. Restless spirits sated 

by continuous learning opportunities—integrated learning and practice.
Poised to Learn: Individuals must be self-motivated to learn—have an 
urge, desire to learn—be “greedy” to learn. Someone commented . . .” 
“When you’re ready to learn, a teacher will appear . . .” Networks brings 
eager, “poised to learn” together with ready teachers.
Passion and Purpose: Networks may be a venue/vessel for individuals 
to be able to bring their passion back into their organizational roles. 
Experiences of network participation speak of their reconnecting with 
previous “dream jobs,” core values, interests, and passions—this desire 
to reconnect passion and purpose may be a motivation for seeking/
participating in networks.
Time Challenge: Struggle to find time to participate—juggling priorities—
time vs. needs—geographic distance adds further challenge.
A conceptual memo written January 5, 2004, goes back to earlier 
data for further analysis.


CLASSIC GROUNDED THEORY
178
Conceptual Memo
Passion, Resistance, and Bonding:  Rereading field notes from interview with 
A . . . , noted the many references to passion, in particular, the connection 
between passion and bonding of network members. Appears that the 
common passion that brings network members together—part of the 
likening that creates a network—is also the “glue” that bonds network 
members. She goes on to describe the “passionate few” as bonding due to 
the resistance they encounter from the formal system—“the resistance 
serves as a way to separate out those who really have a passion to keep 
working” . . . So . . . passion creates likening, resistance creates bonding and 
reinforces passions. . . . a cyclic process that sustains member engagement in 
fluctuating networks.
Further data and memoing suggest that passion may be a key 
variable in work performance with passion being positively correlated 
to performance and disconnected passion blocking performance.
Conceptual Memo
Disconnected Passion: Individuals who exhibit resistance to change or appear 
to be risk averse can become labeled in organizations as “non-performers.” 
They may be shunned by others—particularly by managers. This response 
further isolates the individual and contributes to lower performance—a self-
fulfilling prophecy.  
What may lie at the heart of this superficial assessment of 
non-performance is a disengagement of the individual from their passion or 
purpose in work. 
This disengagement may be a defense or coping mechanism 
in response to increasing dehumanization from changing workplace context 
or a botched or poorly executed change effort. Individuals are 
“having trouble 
trying to reconnect.” They are “disillusioned about the organization and 
trying to cope with the change” around them. 
They’ve become 
“disconnected 
from their work.” If efforts are made to listen past the disengagement and 
non-performance, one can often find that there was a time when these 
individuals “had a real hunger for their work.” What they require is a chance 
to reconnect with their passion, to feel depth and meaning in their work.
They “need to be whole” to have their “eyes light up” again, to feel the 
energy of a new challenge and new learning. This isn’t happening in many 
organizations because “managers are not listening.” The result of their 
inattention is the veritable blocking of employee capability and desire in 
their work. The lack of opportunity leaves individuals feeling powerless.


    Classic GT Using Qualitative Data
179
In February 2004, I moved from open coding of data to selective 
coding for the core category (rehumanizing) and related concepts. I went 
back to data that I had collected earlier in my research. Although these 
data had been collected using an interview protocol (early in my 
research and before I fully understood the importance of staying open) 
I did use it—all is data—to selectively code for indicators of rehumanizing 
and related categories—including Igniting Passions. My analysis of 
these earlier data (collected between December 2002 and November 
2003) yielded the following conceptual ideation.
Conceptual Memo
Igniting Passions: Maturity of network members enhances purposeful 
engagement and facilitates their kindred sharing. Maturity may reduce 
opportunism and transactional interactions. Mature individuals may 
appreciate the value of authentic connections and kindred sharing more 
than those with less experience in the work world. Reference to second 
careers may also be reference to more passionate engagement in the focus 
of the network—where their hearts are—their passions engaged more so 
than in their “day jobs” in the formal organization.
Contextual dimensions of network engagement energy, fun, 
achievement, tension emerged from further data analysis as positively 
correlated with passion.
Conceptual Memos
Depth and Meaning in Igniting Passions: There’s no pretense—no 
showiness—in network interactions. Members are there for a purpose. 
There’s depth and meaning in their participation—“problem solving, sorting 
out . . . let’s have some discussion on this.” Real purpose and value (meaning) 
to those engaged and at the same time, it’s fun. They enjoy the action, the 
inevitable debates. It stimulates the environment. It enables members to 
“eyeball people and get stuff sorted out” so there’s real value—progress is 
made in work.
Igniting Passions: Pressured environment creates an energy that ignites 
passion. Unpredictability creates energy—fast pace creates excitement and 
stimulates networking. 
While there’s anxiety created in this environment, it’s
(Continued)


CLASSIC GROUNDED THEORY
180
an enjoyable anxiety—it’s edgy, stimulating. . . . “What excites me is the pace of 
it. I like that, I like the energy it gives me and the energy it creates so I like it fast 
not slow. . . . I think it’s very important. It feels like a life! Ahh . . . sometimes it’s too 
much for sure but I’ll take any day of too much than too little.”
Challenge emerged as another dimension of igniting passions.
Conceptual Memo
Igniting Passions: Passions are ignited by challenge—the “against all odds” 
syndrome—finding mutual commitment to a goal that others consider 
impossible or crazy. Setting themselves apart from the “masses,” the 
ordinary—taking on a challenge and making it work—high achievement 
orientation—success is sweeter when shared. Believing in the impossible 
and then making it happen. There’s a charge in being challenged and being 
creative in solving an issue, a problem that ignites passionate engagement 
within a network—draws members in.
Likening facilitates the challenge and achievement dimensions of 
igniting passions.
Conceptual Memo
Igniting Passions/Likening/Sustaining: “Newness” is part of likening—the 
chance to meet others who not only share a sense of energy but who 
are also open to what others have to offer—the chance to “show and 
tell” interests, share knowledge and skills—creates an energy and ignites 
passions—can feel out of control at times but in a good way—skills and 
abilities are stimulated by joint problem solving and collaborative creativity 
(see Bennis book on creative collaboration . . . ). Moving past challenge to 
achievement also very important. Novelty likens but achievement sustains.
While igniting passions creates bonds, it can also create insularity.
(Continued)


    Classic GT Using Qualitative Data
181
Conceptual Memo
Igniting Passions/ Sustaining: Passions are not always positive—they can also 
involve spirited outbursts of anger. This is particularly the case when the 
core group of a network have developed such a close group identity that it 
compromises their relationship with others in the external environment—
insularity leading to intolerance—impacts upon ability of the network to 
function within the larger external environment of the formal organization. 
Interactions become personalized and highly emotional—core becomes 
segregated—trust erodes and threatens sustainability . . . network members 
may limit/reduce their participation if they feel it jeopardizes their position 
within the formal organization—cannot risk the consequences.
Challenge and the risk associated with it emerged again as a 
dimension of igniting passions.
Conceptual Memo
Igniting Passions: Challenge and the chance to achieve ignite passions. The 
higher the stakes, the greater the passion. That feeling of being on the 
edge—risking it to overcome and achieve holds a special kind of magic—a 
pull that can be very empowering. The fear of failure mixed with the high 
of success.
Passion ignites through an initial spark when individuals connect 
and liken.
Conceptual Memo
Finding/Igniting Passions: A network forms and fluctuates around an initial 
spark—this may be a person who takes the lead in identifying and 
connecting people of “like minds.” Once like minds find each other, they 
quickly self-identify and self-organize. Their mutual passion creates the 
energy for perpetuating interactions and sustained mutual engagement.


CLASSIC GROUNDED THEORY
182
Kindred sharing facilitates igniting passions and sparks creativity, 
learning, and growth.
Conceptual Memo
Kindred Sharing/Creativity/Igniting Passions/Learning: Kindred sharing involves 
venting—the chance to “blow off steam”—within a safe and empathetic 
environment. But kindred sharing moves past venting to strategizing—a 
proactive and empowering response to dealing with organizational issues—
problems and frustrations. It’s exploring ways to overcome the hurdle 
and move forward. It engages collective minds to addressing issues—the 
collective response not only enables a better response but it also builds 
momentum and support for moving forward. There’s arguing in kindred 
sharing—those outside the network might see this as negative but it’s 
part of sparking the creativity and problem-solving power of the collective 
mindset in a network. It’s part of the fluctuating nature of the network. 
Energy builds through fluctuating emotions—passions ignite and creativity 
is sparked. The outcome is learning and growth.
Igniting passions wards off boredom and stimulates the desire for 
network participation.
Conceptual Memo
Igniting Passions: Boredom mitigation drives networking—igniting passions 
accelerates network engagement and participation. Work environments 
that exude boredom challenge K.W. to seek avenues to sustain energy and 
ward off dehumanization.
As the concept reaches theoretical saturation, a further memo 
attempts to integrate the conceptual ideas that have emerged, organizing 
them into conditions, dimensions, and consequences of igniting passions.
Conceptual Memo
Igniting Passions: “. . . . passion. This is when you are really working with 
people—not just a ‘file’ that you are responsible for.” In work involving “the 
human dynamic,” it is essential to come together face to face—there is a 


    Classic GT Using Qualitative Data
183
cost benefit to doing so if it engages the passion of organizational members—
we speculate about whether perhaps organizations should allocate resources 
on the basis of matching passions! . . .
“Try to develop a passion meter.” Informal networks as a way to find the 
people with a passion for organizational work . . . this is a simple principle 
of self-organization at work—allocating resources to follow the passion. 
Organizational challenge of getting past “assigned work” to matching 
assignments with passions—may not be worth the effort of trying to 
orchestrate this (i.e., manage the process)—that it may be better to let 
things take their course and be satisfied with “60%” . . . Letting the system 
self-organize . . . organic “if no one has passion, we should not be doing it!” 
Organizations can use networks to monitor “passions” and set strategic 
directions accordingly. The human dimension—passion ignites passion—
creates understanding and engagement—Informal networks as a forum to 
mobilize passion into action . . . bringing diverse talents, skills together—
mobilized by shared passion—vision—system sorts itself/self organizes to 
achieve action—progress the work—enhance the practice.
Conditions: Energized Environment: Pressured environment creates an 
energy that ignites passion. Unpredictability creates energy—fast pace 
creates excitement and stimulates networking.  
While there’s anxiety created 
in this environment, it’s an enjoyable anxiety—it’s edgy, stimulating. . . .” 
 
“What 
excites me is the pace of it. I like that, I like the energy it gives me and the 
energy it creates so I like it fast not slow. . . . I think it’s very important. It feels 
like a life! Ahh . . . sometimes it’s too much for sure but I’ll take any day of 
too much than too little.” There’s no pretense—no showiness—in network 
interactions. Members are there for a purpose. There’s depth and meaning 
in their participation—“problem solving, sorting out. . . . .let’s have some 
discussion on this. . . .” Real purpose and value (meaning) to those engaged 
and at the same time, it’s fun. They enjoy the action, the inevitable debates. It 
stimulates the environment. It enables members to “eyeball people and get 
stuff sorted out” so there’s real value—progress is made in work.
Initial Spark: 
A network forms and fluctuates around an initial spark—this 
may be a person who takes the lead in identifying and connecting people 
of “like minds.” Once like minds find each other, they quickly self-identify 
and self-organize. Their mutual passion creates the energy for perpetuating 
interactions and sustained mutual engagement.
Boredom Mitigation: Boredom mitigation drives networking—igniting 
passions accelerates network engagement and participation. Work 
environments that exude boredom challenge K.W. to seek avenues to 
sustain energy and ward off dehumanization.
(Continued)


CLASSIC GROUNDED THEORY
184
Dimensions: Recognition: Passion is positively correlated to recognition. 
Feedback and recognition fuel alignment of individual and organizational 
passions.
Personal Alignment: Informal networks may be a venue/vessel for 
individuals to be able to bring their passion back into their organizational 
roles. Experiences of network participation speak of their reconnecting 
with previous “dream jobs,” core values, interests, and passions—this 
desire to reconnect passion and purpose may be a motivation for seeking/
participating in networks. Maturity of network members enhances 
purposeful engagement and facilitates their kindred sharing. Maturity may 
reduce opportunism and transactional interactions. Mature individuals may 
appreciate the value of authentic connections and kindred sharing more 
than those with less experience in the work world. Reference to second 
careers may also be reference to more passionate engagement in the focus 
of the network—where their hearts are—their passions engaged more so 
than in their “day jobs” in the formal organization.
Purposeful Engagement: Informal networks as keeping personal and 
professional passions from being eroded, depleted in the hectic, humdrum 
of daily organizational operations. . . . “Our job is our work . . . our practice 
is our passion.” Distinguishing between “practice” and “work”—between 
“vocation” and “job” . . . “. . . passion.  
This is when you are really working with 
people—not just a ‘file’ that you are responsible for.” In work involving “the 
human dynamic,” it is essential to come together face to face—there is a cost 
benefit to doing so if it engages the passion of organizational members—we 
speculate about whether perhaps organizations should allocate resources on 
the basis of matching passions! . . .  
“Try to develop a passion meter.”
Networks as a way to find the people with a passion for organizational 
work . . . this is a simple principle of self-organization at work—allocating 
resources to follow the passion. Organizational challenge of getting past 
“assigned work” to matching assignments with passions—may not be worth 
the effort of trying to orchestrate this (i.e., manage the process)—that it 
may be better to let things take their course and be satisfied with “60%” 
. . . Letting the system self-organize . . . organic—“if no one has passion, 
we should not be doing it!” Organizations can use networks to monitor 
“passions” and set strategic directions accordingly. 
The human dimension—
passion ignites passion—creates understanding and engagement—networks 
as a forum to mobilize passion into action . . . bringing diverse talents, skills 
together—mobilized by shared passion—vision—system sorts itself/self 
organizes to achieve action—progress the work—enhance the practice.
(Continued)


    Classic GT Using Qualitative Data
185
Challenge: Passion is positively correlated to challenge. Passions are 
ignited by challenge—the “against all odds” syndrome—finding mutual 
commitment to a goal that others consider impossible or crazy. Setting 
themselves apart from the “masses,” the ordinary—taking on a challenge 
and making it work—high achievement orientation—success is sweeter 
when shared. Believing in the impossible and then making it happen.
Challenge and the chance to achieve ignite passions. The higher the 
stakes, the greater the passion. That feeling of being on the edge—risking it 
to overcome and achieve holds a special kind of magic—a pull that can be 
very empowering. The fear of failure mixed with the high of success. There’s 
a charge in being challenged and being creative in solving an issue, a problem 
that ignites passionate engagement within a network—draws members in.
Learning: “really start to learn when they find a passion for a subject and 
then make a real connection to other learners and real time practitioners.” 
Individual passion for learning is stimulated and reinforced in community. 
Learning and knowledge sharing. . . . Being selfish in terms of learning . . . 
Someone commented . . .  
“When we are (selfish), we learn to give to others—
greedy to share . . .” Networks as a “vessel” for fulfilling greedy learning/sharing 
needs—reciprocal fulfillment of passions for learning and sharing. 
The accidental 
learning presented in networks is a “smorgasbord” that rekindles the desire to 
learn—it also restores the learner’s control and builds autonomy self-select, 
sample, take what’s desired and needed. Restless spirits sated by continuous 
learning opportunities—integrated learning and practice. Individuals must 
be motivated to learn—have an urge, desire to learn—be “greedy” to learn. 
Someone commented . . .  
“When you’re ready to learn, a teacher will appear . . . 
networks bring the eager, poised to learn together with ready teachers.”
Consequences: Likening:  
Passion is positively correlated to likening. 
“Newness” 
is part of likening—the chance to meet others who not only share a sense 
of energy but who are also open to what others have to offer—the chance 
to “show and tell” interests, share knowledge and skills—creates an energy 
and ignites passions—can feel out of control at times but in a good way—
skills and abilities are stimulated by joint problem solving and collaborative 
creativity (see Bennis book on creative collaboration . . .). Moving past challenge 
to achievement also very important. Novelty likens but achievement sustains.
Rereading field notes from interview with 
A. . . . , noted the many references 
to passion, in particular, the connection between passion and bonding of 
network members. Appears that the common passion that brings network 
members together—part of the likening that creates a network—is also the 
“glue” that bonds network members. So . . . passion creates likening.
(Continued)


CLASSIC GROUNDED THEORY
186
Bonding: She goes on to describe the “passionate few” as bonding due 
to the resistance they encounter from the formal system—“the resistance 
serves as a way to separate out those who really have a passion to keep 
working” . . . Resistance creates bonding and reinforces passions . . . a cyclic 
process that sustains member engagement in fluctuating networks. Bonding 
reinforces passions but excessive bonding creates insularity.
Insularity is negatively correlated to network sustainability. Passions are 
not always positive—they can also involve spirited outbursts of anger. This is 
particularly the case when the core group of a network have developed such a 
close group identity that it compromises their relationship with others in the 
external environment—insularity leading to intolerance—impacts upon ability 
of the network to function within the larger external environment of the 
formal organization interactions become personalized and highly emotional—
core becomes segregated, trust erodes and threatens sustainability . . . network 
members may limit/reduce their participation if they feel it jeopardizes their 
position within the formal organization—cannot risk the consequences.
Performance: Passion is positively correlated to performance. Individuals who 
exhibit resistance to change or appear to be risk averse can become labeled 
in organizations as “non-performers.” They may be shunned by others—
particularly by managers. This response further isolates the individual and 
contributes to lower performance—a self-fulfilling prophecy. What may lie at 
the heart of this superficial assessment of non-performance is a disengagement 
of the individual from their passion or purpose in work. 
This disengagement may 
be a defense or coping mechanism in response to increasing dehumanization 
from a changing workplace context or a botched or poorly executed 
change effort. Individuals are “having trouble trying to reconnect.” They are 
“disillusioned about the organization and trying to cope with the change” 
around them. They’ve become “disconnected from their work.” If efforts are 
made to listen past the disengagement and non-performance, one can often 
find that there was a time when these individuals “had a real hunger for their 
work.” What they require is a chance to reconnect with their passion, to feel 
depth and meaning in their work. They “need to be whole,” to have their “eyes 
light up” again, to feel the energy of a new challenge and new learning. This 
isn’t happening in many organizations because “managers are not listening.” 
The 
result of their inattention is the veritable blocking of employee capability and 
desire in their work. 
The lack of opportunity leaves individuals feeling powerless.
Creativity: Arguing is part of igniting passions and sparking the creativity 
and problem-solving power of the collective mindset in a network. It’s part 
of the fluctuating nature of the network. Energy builds through fluctuating 
emotions—passions ignite and creativity is sparked.
(Continued)


    Classic GT Using Qualitative Data
187
Theoretical sampling of data collected earlier in the study (October 
and November, 2001) provided additional indicators of several 
dimensions of igniting passions that had emerged previously—fun, 
challenge, learning, energy, confidence, and bonding.
Conceptual Memo
Igniting Passions: There’s a strong desire to continue to network once 
individual passions have been ignited. Passions are fueled by the desire to 
continue to experience the energy and synergy that result from mutual 
engagement—to work and learn and laugh together. There’s a strong sense of 
fun, of pushing the envelope. The desire to continue to move the network 
forward creates its own sense of excitement and fuels a passionate belief 
in the ability to make a difference.
Conceptual elaboration concludes when the relationships among 
individually elaborated concepts emerge through the identification 
and use of relevant theoretical codes to achieve an integrated theoretical 
framework for the overall theory. The theoretical codes that emerged in 
this study include basic social process (both psychological and 
structural processes) and amplifying causal looping. Igniting passions 
earned a pivotal position within the basic social psychological process 
of rehumanizing, the core category of fluctuating support networks, itself 
a basic social structural process.
	
CONCEPTUAL INTEGRATION AND WRITING UP	
l
Once I had achieved theoretical saturation of my core concept and 
related categories, I proceeded to review and hand sort memos regarding 
the core and its properties and dimensions. As I began to sort and look 
for relationships between the various concepts, theoretical codes began 
to emerge as an abstract modeling of the latent structural patterns that 
could integrate and explain the emerging theory. The first indication of 
emergent theoretical codes was memoed in December 2003.
My study may be viewed as that of a basic social process (BSP)—coping 
with change (in this case, the substantive area is the knowledge workplace). 
(Continued)


CLASSIC GROUNDED THEORY
188
As the overarching BSP
, coping with change includes both a basic social 
structural process (BSSP)—fluctuating networks of professional concern4—
and a basic social psychological process (BSPP)—rehumanizing—which is 
also the core category, integrating and explaining the BSPP of rehumanizing 
through the BSSP of fluctuating networks of professional concern. 
Rehumanizing can be viewed as a structural condition affecting the nature of 
fluctuating networks of professional concern. These networks have always 
been there in the workplace as they are inherent to social organization 
generally, but today’s increasingly compressed and dehumanized work 
environments (changing workplace context) have brought the need for 
rehumanizing to the fore as a means of addressing the main concern of 
those involved—coping with change (thereby magnifying the BSPP of 
rehumanizing as a structural condition of the BSSP of fluctuating networks). 
As such, the BSSP of fluctuating networks of professional concern has 
taken on the properties of the BSPP of rehumanizing, including authenticity, 
depth/meaning, respect, safety, healing . . . As a preliminary suggestion, the 
stages in the BSPP of rehumanizing may be finding, likening, igniting passions, 
kindred sharing, experimenting, bonding, and sustaining. Some of these may 
be combined as research progresses; new ones may be identified . . . the 
structural process (of fluctuating networks) is of significance because it 
explains the organization of behavior (as emergent informal organization) 
to address the main concern of the participants—coping with change 
within the workplace—through a BSPP (rehumanizing) as antidote to the 
dehumanizing impact of traditional formal organizational structures. This is 
starting to feel 
“right” for me—things are fitting into place and I can now see 
an overall conceptual framework around which to begin building the theory.
While continuing to consider basic social process as an appropriate 
theoretical code through which to integrate my emerging theory, I 
remained open to the emergence of other theoretical codes as I 
continued to hand sort and integrate memos. A final integration of the 
theory occurred in March 2004 with the emergence of the theoretical 
code of amplifying causal looping. The final sorting and integration 
followed, generating a theoretical outline (conceptual framework) for 
the full articulation of the theory.
(Continued)


    Classic GT Using Qualitative Data
189
ENDNOTES
1.	 It is important to remember that this is the work of a novice conducting 
her first grounded theory study. It is, as such, an exemplar but is by no 
means offered as a marker of excellence in doing classic GT.
2.	 As so often happens, there was a connection back to my initial research 
interest in communities of practice, but my grounded theory explains 
much more about what is really going on as managers and other knowl­
edge workers voluntarily seek out and engage in work-related commu­
nities and networks. My theory explains that the motivation for their 
participation is not simply learning and practice enhancement. It is 
much more about their efforts to overcome the dehumanization they 
experience in their work and their workplace context in the midst of 
persistent and unpredictable change.
3.	 My grounded theory is rather complex in that it is a “theory within a 
theory”—the overall theory of fluctuating support networks (a basic social 
structural process) with its core category of rehumanizing (a basic social 
psychological process) of which igniting passions is the central stage in 
the process (the core category of the rehumanizing process).
4.	 I later renamed this concept fluctuating support networks to more effec­
tively reflect their substantive role in providing support to network 
members.


Appendix C
Doing Classic GT Using Mixed Qualitative and 
Quantitative Data
T
his appendix is based on excerpts from Walsh (2014a) and 
elaborates on her application of classic GT while using mixed 
qualitative and quantitative data and techniques. The full article 
“A Strategic Path to Study IT Use Through Users’ IT Culture and IT 
Needs: A Mixed-Method Grounded Theory” may be found in the 
Journal of Strategic Information Systems, 23(2), pp. 146–173. Copyright 
2014. (Permission granted by Elsevier, License No. 3674860603800, 

July 23, 2015). Examples of the mixed qualitative and quantitative data 
applied in this work may be found in Appendix D.
l
l
1. A MIXED-METHOD GROUNDED THEORY DESIGN
In this section, we detail the research design that was not set at the 
beginning of our study; instead, this emerged as our research was 
conducted, and it was guided by the emerging theory.
1.1 The GT Framework
If one does not take into account any paradigmatic consideration, 
two main distinct variants of the GT framework may be found in the 
literature. One of these variants is favored by Glaser and the other by 
Strauss (Melia, 1996; Urquhart et al., 2009), with main disagreements 
involving coding issues and the role of extant literature. Our stance is 
closest to Glaser’s but, beyond any difference of opinion between 
190


    Doing Classic GT Using Mixed Qualitative and Quantitative Data
191
Glaser and Strauss over the years, and if one goes back to their original 
(1967) common work, Urquhart et al. (2009) showed that four main 
characteristics should be found in a GT study: (1) Emergence of both 
research design and outcome must be rooted in data and not 
preconceived/imposed on data; (2) constant comparative analysis of 
the data must take place (i.e., data are constantly compared to 
previously collected and analyzed data, looking for similarities and 
differences to help toward conceptualization and theorization); ­
(3) there 
must be theoretical sampling (i.e., sampling is directed by the emerging 
theory and continues until the saturation of concepts, categories, 
properties, and relationships is reached); and (4) the ultimate aim of GT 
must be theory building, substantive or formal theory (i.e., discovering 
and constructing, rather than verifying. theory is the end purpose of a 
GT study).
1.2 A Mixed-Method Approach
In his 2008 work about quantitative GT, Glaser uses Lazarsfeld’s 
work and his techniques of quantitative analysis for discovery. 
Lazarsfeld “insisted on the combining of qualitative and quantitative 
analysis” (Glaser, 2008, p. 6), leading to a mixed-method approach. 
­
A  mixed-method design includes different quantitative and/or 
qualitative methods, which combine with and supplement each other 
within a single project. In our work, we did not use qualitative data 
and methods to draw hypotheses that we verified through quantitative 
data and methods; this would be what we understand to be a multi-
method approach (Morse, 2003, pp. 189–208). In our research, neither 
qualitative nor quantitative data/methods were sufficient in themselves 
to theorize; all were necessary. This article combines qualitative and 
quantitative data and research methods in a mixed-method GT 
approach.
We used both secondary data (from the existing literature) and 
primary data (collected for the present study). When we collected 
primary data (quantitative and/or qualitative), we did so as we needed 
them to move the emerging theory forward and also to allow for 
triangulation. Qualitative data were collected when we needed rich 
description of the emerging relationships between concepts. 
Quantitative data were collected when we needed to move back from 
details and obtain a synthetic perspective. All data (qualitative and 
quantitative) were analyzed as one set.


CLASSIC GROUNDED THEORY
192
1.3 Study Context, Sample, and Data Collection
The software that we more specifically investigated is an open 
source e-learning software platform, Moodle (Modular Object-Oriented 
Dynamic Learning Environment), which may be used by both 
professors and students in third-level education and training contexts. 
We investigated the use of this platform in a European business school, 
where this software is mostly used as an exchange platform for 
professors and students. Professors can (if they wish) upload their class 
materials. Their students have direct and unlimited access to this 
platform and can download what they need. The platform also allows 
students to submit assignments, and grading may be done online by 
the professors. It also provides facilities for instant messaging, and 
online news, announcements, and timetable details. The use of the 
platform is not institutionally mandatory, but its use is presented as a 
great help for both students and professors in fulfilling their tasks; it is, 
therefore, strongly recommended by administrators for both professors 
and students. “Well, it’s on Moodle, you can find it for yourself” is a 
recurring answer from some administrative staff when either students 
or professors ask questions. Most full-time professors use it at least to 
provide course materials, and students may feel compelled to use it to 
gain access to these materials, although they could also obtain them 
from other students if they do not want to use the platform. Some 
professors might, however, insist that students upload their homework 
on the platform in order to obtain grades.
Our sampling was guided by the emerging theory. The slices of 
quantitative data were collected through surveys administered at the 
end of a school year (June 2012). Therefore, the participants in these 
surveys had had the opportunity to directly access the investigated 
exchange platform for a significant amount of time (at least two 
semesters) and had been offered voluntary training sessions at the 
beginning of each semester.
The qualitative slices of data were collected through interviews 
conducted between September 2011 and September 2012 with both 
students and professors. As we were part of the institution investigated, 
we also used participant observation. We used Walsh et al.’s (2010) 
typological work to guide us in our theoretical sampling. We identified 
users that fit some of the ideal types highlighted by Walsh et al. (2010). 
More than the seven interviewees we report on were involved; we 
retained for the present study and report on only those users whose 
information technology (IT) culture profile we could identify with 
sufficient precision and who were different in terms of age, nationality, 
and background to allow for different perspectives. Professors who 


    Doing Classic GT Using Mixed Qualitative and Quantitative Data
193
were interviewed were colleagues that we worked with, sharing 
classes, chores, or research projects (but not the present one) with them. 
Students who were interviewed were those with whom we had an 
above-average number of exchanges as we supervised their master’s 
degree dissertations. Interviewees were treated as case studies rather 
than as simple interviewees. For the first slice of qualitative data, 
several extensive interviews were conducted with each interviewee 
between September 2011 and May 2012; notes were taken and memos 
written up.
Two surveys (one for professors and one for students)—very 
similar but with some questions adapted to the different end-users—
were put online, and e-mails were sent to faculty and students to invite 
them to respond in May and June 2012. E-mails were sent to 375 faculty 
members (85 full-time professors and 290 part-time professors) and 
1,076 students, and 84 professors and 198 students answered the 
surveys (the response rates were 22.4% and 18.4%, respectively). All 
received answers were used, and no outliers were removed. We 
verified our results with early and late respondents’ data with no 
significant difference. This indicates that nonresponse bias should not 
be a problem in the quantitative data collected for the present study 
(Armstrong & Overton, 1977).
Quantitative data were analyzed with the help of partial least 
squares (PLS) structural equation modeling (SEM), applied in an 
exploratory manner. The transcripts of the qualitative data were hand-
coded a number of times and sorted out on a table (in parallel with our 
quantitative investigations. After we had started to explore our 
quantitative slices of data, together with the qualitative data already 
collected, we conducted further interviews in July and September 2012 
with the seven participants theoretically sampled for the present study, 
in order to verify our interpretation of the qualitative and quantitative 
data already collected and to expose this to their perspectives. These 
last interviews with each interviewee were recorded and relevant 
elements transcribed. During these interviews, and only then, did we 
briefly describe to interviewees details of the research that was being 
conducted. We also introduced them to such terms as IT acculturation 
or IT culture and the distinction we drew between the two terms IT 
utilization and IT use, which follows Walsh’s (2010) definition of these 
two terms.1 Had we not done so, and as these terms were part of our 
own vocabulary, we risked biasing their answers through the way we 
asked our questions. Also, because some respondents were of different 
nationalities and spoke different native languages, some of the 
interviews were conducted in English and others in French. In order to 


CLASSIC GROUNDED THEORY
194
translate adequately important words such as usage and utilization from 
one language to the other, the meaning of the terms used had to be 
fully understood by respondents and our interpretations/translations 
verified. Therefore, we chose to inform interviewees about the meanings 
we attributed to these terms in order to avoid misinterpretation on our 
part of the qualitative data collected. Further notes were made and 
memos were again written up immediately after these last interviews 
took place as, in most instances, these final conversations continued after 
the interviews were recorded. Qualitative data were first open-coded on 
concepts (e.g., IITC, utilization, global IT needs, contextual IT needs, and 
situational IT needs) and then theoretically coded in terms of the 
relationships between these concepts (e.g., individual IT culture à 
global IT needs, position à contextual IT needs, situational IT needs à 
utilization). Secondary data (from the literature), primary data 
(collected for the present study), quantitative data (collected through 
Quantitative
data collected
from surveys
conducted with
students and
professors (282
respondents)
Qualitative
data collected
from participant
observation
and interviews
with seven
participants
Secondary Data 
(Deci & Ryan, 1985; Ryan & Deci, 2000;
Vallerand, 1997, 2007; Walsh, 2010;
Walsh et al., 2010)
Primary Data
Constant
Comparative
Analysis
Emerging Theory
Emerging Theory
Emerging Theory
Emerging Theory
Constant
Comparative
Analysis
Figure C.1    Using All Our Data as One Set1


    Doing Classic GT Using Mixed Qualitative and Quantitative Data
195
surveys), and qualitative data (collected through interviews and 
participant observation) were analyzed together, and, in a critical 
realist stance, we opted for the relationships between concepts that 
appeared to best fit our whole data set, while considering at the same 
time broader perspectives reaching beyond our substantive area, in 
order to start driving toward formal GT.
In order not to confuse the reader, and although many different 
paths were investigated that are not reported here due to space 
allocation, we present in the results section only the final models that 
emerged from and were congruent with our whole data set. As 
quantitative and qualitative investigations and analyses were done 
simultaneously, while taking into account all data as one set, and 
constantly comparing and analyzing all data as they were collected 
(see Figure C.1), we justify our models with previous works from the 
literature, quotes from interviews, quantitative reports, or all three of 
these. Using mixed methods allowed us to avoid the “Texas sharpshooter 
approach”2 (Biemann, 2012), a bias that plagues many quantitative 
studies and that results mostly from their authors’ quest for publication 
(Biemann, 2012).
Throughout the present research, we openly remain in an 
exploratory stance and iterate between all data. For instance, some 
relationships were investigated and confirmed through quantitative 
methods with our substantive quantitative data set, but we neither 
retained nor reported on them as they did not make sense in our 
complete data set. Conversely, some other relationships that were not 
completely confirmed through our quantitative data are discussed, 
because, based on information obtained through our qualitative data 
set, they could be important for further research in other substantive 
areas with different, and less specific, targeted sampling.
1.4 Quantitative Measures
All research variables were quantitatively modeled as reflective 
using multi-item Likert-type scales (ranging from 1 = “not true at all” 
to 7 = “completely true”), except for the variables POSITION, IITC, and 
UTILIZATION.
Reflective measures for the constructs global IT needs (GLOBITNEE), 
contextual IT needs (CONITNEE), and situational IT needs (SITITNEE) 
were developed specifically for the present study. To ensure content 
validity, we used qualitative data from the interviews that were 
conducted, as well as Walsh et al.’s (2010) qualitative GT work and its 
detailed online coding appendices. Reflective measures for ease of use 
(EOU) and usefulness (U) were adapted from Davis (1989). Benbasat 


CLASSIC GROUNDED THEORY
196
and Barki (2007) stressed the “need to make sure usefulness is measured 
beyond perceptions where possible” (p. 216). The perceptual aspect of 
usefulness was minimized, as we studied usefulness and ease of use as 
assessed by users after they had had time to be trained and to test and 
access the investigated software for a significant period of time.
The control variable POSITION was dummy-coded (Professor = 1 
and Student = 2).
For the IITC construct, we used as a starting point the measure 
proposed by Walsh (2009) and verified by Von Stetten, Wild, and 
Chrennikow (2011), which we aimed at improving. We modeled IITC 
as a first-order reflective, second-order formative construct and 
followed the guidelines provided by Diamantopoulos, Riefler, and 
Roth (2008). The first-order latent reflective variables we started with 
were accomplishment needs satisfied through IT usage (ACCNEE), 
affiliation needs satisfied through IT usage (AFFNEE), primary needs 
satisfied through IT usage (PRIMNEE), power needs satisfied through 
IT usage (POWNEE), intrinsic motivation to know IT (INTMOTKNO), 
extrinsic motivation to use IT through identified regulation 
(EXTMOTID), and extrinsic motivation to use IT through external 
regulation (EXTMOTEX). The items of this last construct were worded 
negatively and had to be reverse-coded to obtain the new variable 
REV_EXTMOTEX. However, Von Stetten et al. (2011) found that this 
sub-construct, proposed by Walsh (2009), did not have a significant 
influence on IITC. We verified and confirmed this point in our 
preliminary investigations and did not include this sub-construct in the 
final measurement model of IITC, as we found theoretical reason not to 
do so: If one considers the self-determination continuum proposed by 
Gagné and Deci (2005), all sub-constructs of IITC are on the positive 
end of this continuum except for the questioned variable EXTMOTEX, 
which rather appears to capture some elements of amotivation 
(Pelletier et al., 1997). Even recoding and inverting this variable does 
not solve this theoretical issue, as it has not been proven that 
amotivation is the reciprocal function of motivation. This is why we 
chose not to include this sub-construct in the IITC measurement model. 
Measures of the other dimensions were adapted to improve reliability, 
and content validity was ensured through the study of various scales 
in the psychosociological field of research, which we adapted for our 
purpose.3
Concerning the assessment of use, there is a lack of consensus in 
the literature on how to measure system usage/utilization, and there is 
a vast array of measures for it, as highlighted by Burton-Jones and 
Straub (2006). Wu and Du (2012) categorized the existing measures 


    Doing Classic GT Using Mixed Qualitative and Quantitative Data
197
found in the literature as actual usage, reported usage, and measured 
usage. Actual usage (e.g., the measures used by Devaraj et al., 2008, or 
Venkatesh et al., 2002) involves objectively collected measures such as 
computer logs. The other two categories (reported and measured 
usage) imply subjective self-reported measures. Reported usage (e.g., 
the measures used by Adams et al., 1992; Keill et al., 1995; Taylor & 
Todd, 1995) involves measures of duration and frequency that aim at 
objectivity even though these measures are reported by the users 
themselves: for instance, the number of hours spent on a system per 
day. Assessed usage (e.g., the measures used by Davis, 1989; Igbaria & 
Parasuraman, 1989; Karahanna, Agarwal, & Angst, 2006) involves 
ordinal measures of intensity and extent, the ordinal scale being the 
main element of differentiation of this type of assessment. Over 

the years, other elements of differentiation that involve the user’s 
subjective cognitive abilities were added. As a result of their 
investigations, Wu and Du (2012) propose the use of rich measures of 
assessed usage through multiple dimensions that mix different 
measures. Originally, we had wished to assess IT utilization as defined 
by Walsh (2010), that is, “the actual, objectively assessed, use of an IT” 
(p. 152). As actual measures extracted from computerized reports were 
vetoed by our faculty, we opted for self-reported ordinal measures of 
utilization. We included in our measure of utilization the number of 
functionalities used that assessed the extent of utilization (UT_FUNCT) 
and the frequency of utilization that assessed the intensity of utilization 
(UT_FREQ). We also wanted to assess the type of utilization, that is, 
whether users’ perception of the platform led them to a self-determined 
or constrained utilization (variable UT_TYPE). For instance, if users go 
on the platform “as little as possible” or only when they feel “it is 
absolutely necessary,” their utilization might be considered closer to 
mandatory than voluntary, and with differing degrees of self-
determination. We therefore specifically developed for the present 
study a three-dimensional formative measure of utilization that 
included the dimensions UT_TYPE, UT_FREQ, and UT_FUNCT. The 
construct UTILIZATION was specified as formative, since changes in 
any of the three indicators would lead to changes in utilization but, 
conversely, a change in utilization does not imply a change in each of 
the three indicators.
All measures were pretested with 25 students in a postgraduate 
class and with a selected sample of 20 faculty members, in order to 
obtain comments and feedback before the pilot test was finally 
conducted. After the pretest, the wording of some items was readjusted, 
and one item was deleted from three of the reflective scales (EXTMOTID, 


CLASSIC GROUNDED THEORY
198
GLOBITNEE, and CONITNEE) in order to improve reliability; these 
three scales were, thus, reduced from three to two items each. As these 
scales were for reflective constructs and we aimed at a parsimonious 
instrument, this was deemed acceptable.
The measurement models, as well as the relationships between all 
variables, were analyzed using a PLS approach, and bootstrapping as a 
resampling technique (500 random samples), to generate t-statistics 
(Chin, 1998). We used the SMARTPLS software (Ringle, Wende, & Will, 
2005). PLS analysis was preferred because it does not require data with 
a normal distribution (Fornell & Cha, 1994), and it supports both 
reflective and formative constructs (Gefen, Rigdon, & Straub, 2000). It 
was also preferred because our research objectives were exploratory 
(Gefen et al., 2011; Hair et al., 2012; Ringle et al., 2012). PLS analysis is 
particularly suitable in situations of high complexity, with low theoretical 
information (Jöreskog & Wold, 1982): We had an important number of 
variables and little theoretical backup on the relationships we wanted to 
investigate. Another advantage of the PLS approach is its ability to work 
with a small sample size (Ringle et al., 2012; Urbach & Ahlemann, 2010).
As advised by Glaser (1978, 2008), we let our data “speak” and guide 
the emerging theory. The propositions highlighted could not have been 
developed if all data had not been interpreted as one set, and if both 
qualitative and quantitative methods and analyses had not been 
applied in combination.
Our stance is critical realist (Bhaskar, 1979, 1989, 1998, 2002). 
Although a debate about critical realist precepts is beyond the scope of 
this book, before we present our models and propositions, it is essential 
to clarify what causality is for a critical realist. The notion of causality 
as a “generative mechanism” is a core and defining feature of critical 
realism (Bhaskar, 2002). Generative mechanisms are best understood as 
“tendencies,” as their activation is highly context-dependent (Bhaskar, 
2002). In contrast with the Humean vision of causality (“A causes B”), 
commonly accepted in information systems (IS) traditional quantitative 
positivist circles, a generative mechanism can be reformulated as 
­
“A  generates B in context C” (Cartwright, 2003; Smith, 2010). For a 
critical realist, causality is thus a process of how causal powers are 
actualized in some particular context: a process in which the generative 
mechanisms of that context (C) shape (modulate, dampen, etc.) 
l
l
2. THE EMERGING RESEARCH MODELS


    Doing Classic GT Using Mixed Qualitative and Quantitative Data
199
­
the  particular outcomes. For instance, building on Tsang and Kwan 
(1999)’s example, a car will drive adequately if it has four wheels and 
an engine but only if somebody also turns on the ignition and a nail 
does not puncture one of its tires. Therefore, the arrows in the diagrams 
proposed in this section do not illustrate causality as traditionally 
understood; they illustrate the activation of causal powers as revealed 
in the substantive area (the use of the Moodle platform by students and 
professors) and the context (a European business school) that were 
investigated. The paths that are reported in the models are those that 
best fitted our set of data (qualitative and quantitative) within the 
substantive area and the context that were being investigated. Where 
possible, however, we used a reflexive approach and extended our 
results in order for them to be applied in further research in other 
substantive areas and contexts. When we understood the collected data 
as sufficient to demonstrate that a proposition would apply whatever 
the substantive area and context investigated, we added the word 
mostly in the wording of the corresponding proposition. Mostly, 
however, is used only as a heuristic device and should not be taken at 
face value. It means that, based on the data we collected, the proposition 
appears to extend to other substantive areas and other contexts beyond 
those researched for the present study; this would, of course, need to 
be verified in further research. When mostly is not used in the wording 
of a proposition, it indicates that the corresponding proposition may 
hold true only within our substantive area and investigated context.
As our main concern was to investigate the path between users’ 
individual IT culture and their utilization of the e-learning platform, 
and how the various constructs related to their perceived IT needs 
(global, contextual and situational) fit in this path, we first describe in 
this section the new path to IT use that we propose through these new 
concepts. We then embed one of our newly defined variables in the 
TAM nomological framework to position our work with respect to past 
literature.
Qualitative and quantitative analyses were embedded, and our 
results emerged through constant iterations between quantitative and 
qualitative data as they were collected and through the analysis of all 
our data as one set. Therefore, for each model, we present our results 
in a logical sequence that relates our findings as closely as possible to 
how we arrived at them.
For each proposition, we detail elements from the literature (if 
available) and/or qualitative clues that led us to it. To avoid unnecessary 
length, we cite only when needed some verbatim that we consider as 
most illustrative for our arguments. We also report on the quantitative 


CLASSIC GROUNDED THEORY
200
results that were obtained as we explored the various quantitative 
paths between the newly defined variables. We report on these through 
(1) the standardized coefficients β of the investigated paths, which 
indicate the strength of the relationships between each pair of variables, 
and have values between 0 and 1; (2) the significance of these paths, 
obtained through the bootstrapping procedure and expressed by the 
probability p that the hypothesis underlying this path might not be 
verified; and (3) the R2 values for each variable, which inform us how 
much of its variance is explained by its antecedent(s).
With the help of our whole data set, we found that individual IT 
culture mostly positively influences the global IT needs perceived by 
users (Proposition 1). In our substantive area and investigated context, 
individual IT culture also happened to positively influence the contextual 
IT needs perceived by professors and students (Proposition 2). Both 
global IT needs and contextual IT needs were found to mostly influence 
the situational IT needs perceived by users for a given IT proposed in 
their work context and supposed, by deciding instances, to be of some 
help in fulfilling some of the users’ tasks (Propositions 3 and 4). These 
perceived situational IT needs in turn mostly significantly explain the 
use of this IT (Proposition 5). The position held by an individual within 
an organization will mostly influence his or her contextual and 
situational IT needs (Propositions 6a and 6b); in our substantive area 
and investigated context, it also moderated the effect of the perceived 
situational IT needs on the actual utilization of the investigated IT 
(Proposition 6c).
ENDNOTES
1.	 “We define IT-utilization as the actual, objectively assessed, use of an IT, 
and IT-usage as a socially constructed . . . phenomenon.” (Walsh, 2010, p. 8)
2.	 “The fabled ‘Texas sharpshooter’ fires a shotgun at a barn and then 
paints the target around the most significant cluster of bullet holes in the 
wall. Accordingly, the Texas sharpshooter fallacy describes a false con­
clusion that occurs whenever ex post explanations are presented to inter­
pret a random cluster in some data” (Biemann, 2012, p. 2).
3.	 The investigated scales were the determination scale (Sheldon, Ryan, & 
Reis, 1996), basic psychological needs satisfaction scales (currently being 
researched by a team of researchers, including Deci and Ryan), global 
motivation scale (Guay, Mageau, & Vallerand, 2003), and the fundamen­
tal needs satisfaction scale in the context of sports (Gillet, Rosnet, & 
Vallerand, 2008).


Appendix D
Some Examples of Theoretical Coding Using Mixed 
Qualitative and Quantitative Data
A
lthough referring to Walsh (2014a), which is an empirical article, 
this appendix is extracted and adapted from Walsh (2014c), 
which is a methodological article. The examples provided refer 
to the same research project as Appendix C and provides some 
examples of theoretical coding using mixed qualitative and quantitative 
data in an embedded mixed GT design. This appendix also illustrates 
how, once the core category of a grounded theory has emerged, the 
literature may be used as secondary data.
201


202
Theoretical 
codes
Generalizability
Constant comparative analysis of all data as one set
Secondary data (from previous phases and 
from the literature)
Primary qualitative data
Primary 
quantitative data
Proposition 1: 
Individual 
information 
technology 
(IT) culture 
has a positive 
influence 
on the 
individual’s 
global IT 
needs.
Substantively 
formalized
We found, during previous phases of 
our research, three main attitudinal 
user groups (proactive, passive, and 
refusal). These groups include 
different user profiles, with differing 
degrees of IT acculturation. We also 
found that the most IT-acculturated 
users are proactive. They are perceived 
by managers as having a facilitating 
influence during the implementation 
of new IT in organizations. They are 
also those users with high levels of 
global IT needs, whereas passive users 
have lower levels of global IT needs 
and refusal users have none.
“For me, there is a clear link between an 
individual’s level of IT acculturation 
and the need they perceive for IT in 
their life.” (CEO of an IT firm)
IITC à 
GLOBITNEE: 
β = 0.598, 
p < 0.001, 
R² for 
GLOBITNEE 
= 35.8%
Proposition 2: 
Individual IT 
culture has a 
positive 
influence on 
contextual IT 
needs.
Bounded by 
context
We found during previous phases of 
our research that the users who were 
highly IT-acculturated also had high 
levels of contextual IT needs.
“I certainly could not do my job in X 
school without IT. This was not the case 
in my previous institution, although I 
personally feel I need IT to do my job as a 
professor . . . For instance, it’s the first 
time I have been asked to use PowerPoint 
slides to teach. In my old school, nobody 
ever used PowerPoint.” (Young assistant 
professor with a couple of previous 
teaching experiences and who has started 
a new job in X school; she grew up in a 
computerized home environment with a 
father who is an IT engineer)
IITC à 
CONITNEE: 
β = 0.400, 
p < 0.001


203
Proposition 3: 
Global IT 
needs 
influence 
situational IT 
needs, 
positively or 
negatively.
Substantively 
formalized
Vallerand (1997, 2001) showed that 
motivation at the situational level 
results from the effects of both global 
motivation and “social factors at the 
appropriate level of generality.” 
(Vallerand, 1997, p. 275)
“I have fairly important IT needs; for 
example, I have several computers, and 
I always need to be connected through 
the Web. When I travel abroad, I remain 
connected even if it costs me a lot of 
money. . . . You evaluate the platform 
and how it helps you fulfill needs 
linked with your responsibilities. I 
found that Moodle helped only with 
uploading the files that I wanted the 
students to have, and I found other 
functionalities inadequate, so I use the 
platform only minimally.” (Young 
informations systems [IS] assistant 
professor)
POSITION à 
SITITNEE:  
β = –0.138,  
p < 0.001
Proposition 4: 
Contextual IT 
needs have a 
positive 
influence on 
situational IT 
needs.
Bounded by 
context
N/A
“The link between the need for IT that I 
perceived as related to the school where 
I did my Executive Master of Business 
Administration degree, and the need for 
the specific tool that was proposed to 
help us in our tasks as students is 
significant. . . . It was made clear to us, 
from the beginning of the course that 
we were supposed to be IT-proficient.” 
(Mature EMBA student)
CONITNEE à 
SITITNEE: 
β = 0.280,  
p < 0.001
(Continued)


204
Theoretical 
codes
Generalizability
Constant comparative analysis of all data as one set
Secondary data (from previous phases and 
from the literature)
Primary qualitative data
Primary 
quantitative data
Proposition 5: 
Situational IT 
needs, related 
to some 
specific 
system or 
software, 
have a 
positive 
influence on 
the use of this 
specific 
system or 
software.
Substantively 
formalized
If users perceive needs for some 
specific IT in order to fulfill given 
tasks, they will be driven to fulfill 
these needs (Maslow, 1954) and hence 
use this specific IT.
N/A
SITITNEE à 
UTILIZATION: 
β = 0.676, 
p > 0.001
Proposition 6a: 
The position 
held by an 
individual 
within a given 
organization 
affects the 
individual’s 
contextual IT 
needs.
Substantively 
formalized
Post et al. (1999) show that 
organization size and users’ academic 
education influence the level of users’ 
contextual IT needs.
“I have done several summer jobs 
during my studies—from stable work 
with horses, through factory work, to 
helping candidates in local elections; 
obviously, I did not have the same 
needs for IT in all these jobs. It is 
probably in my current teaching 
position that I need IT the most.” 
(Young assistant professor)
POSITION à 

CONITNEE: 
β = 0.277, 
p > 0.001, 
R² for 
CONITNEE 
explained by 
IITC and 
POSITION = 
27.7%
(Continued)


205
Proposition 6b: 
The position 
held by an 
individual 
within an 
organization 
affects his or 
her situational 
IT needs.
Substantively 
formalized
N/A
N/A
POSITION à 
SITITNEE:  
β = 0.454,  
p < 0.001, R² for 
SITITNEE 
explained by 
CONITNEE, 
GLOBITNEE, 
and POSITION 
= 35.8%
Proposition 6c: 
Position has a 
moderating 
influence 
between 
situational IT 
needs and 
utilization.
Bounded by 
context
N/A
“I teach in several schools and I am not 
very much into IT. If I were to get used 
to all the different IT tools in the 
different schools where I teach, it would 
take me hours that I don’t have. Unless I 
am ‘officially’ obliged to utilize a 
platform, I will not do it even if I need it 
. . . even if it would make my exchange 
with the students somehow easier.” 
(Professor who shares his time between 
consulting and teaching in business 
schools, and who feels, as he says so 
himself, “constrained to utilize IT.”)
Quantitative 
analyses with 
and without the 
moderating 
effect


Appendix E
Examples of Diagrams as Precursors of a Theory
A
s we saw in Chapter 7, a diagram is not a theory. However, it 
may help the researcher’s theoretical sensitivity, thus nurturing 
the emergence of a theory, especially if the theory is complex 
and multiple theoretical codes are emerging from the data.
Two of the following diagrams are extracted from Walsh, Renaud, 
and Kalika (2013); two are unpublished and were intermediary memos.
Users’
IT Needs
Tasks’ 
IT Needs
Mana-
gerial
IT Needs
Hardware
Needs
Software
Needs
Alignment 4
Alignment 5
Alignment 6
Alignment 7
Alignment 1
Alignment 3
Alignment 2

Memo 1:    Unpublished
206


Examples of Diagrams as Precursors of a Theory
207
Memo 2:    Extracted from Walsh, Renaud, and Kalika, 2013
Users’
IT Needs
Tasks’ 
IT Needs
Mana-
gerial
IT Needs
Top Managers
Hardware
Needs
Software
Needs
Alignment 4
Alignment 5
Alignment 6
Alignment 7
Alignment 1
Alignment 3
Alignment 2
IT Utilization
IS  Strategy
and Infrastructure
Users’ 
IT Culture
IT Technicians
Software Analysts
Users’ Tasks
 Business  Strategy
and Infrastructure
Job Experts


CLASSIC GROUNDED THEORY
208
Memo 3:     Unpublished
Users’
IT Needs
Tasks’ 
Needs
Mana-
gerial
Needs
Hardware
Needs
Software
Needs
Alignment 4
Alignment 5
Alignment 6
Alignment 7
Alignment 1
Alignment 3
IS Strategy and Structure
Users’ IT Culture
IT Utilization
Users’ Tasks
Job Experts
Top
Managers
Business Strategy
and Structure
IT
Technicians
Software
Analysts
Translators
Translators
Translators
Translators
Translated
Strategic
Alignment
Alignment 2


Examples of Diagrams as Precursors of a Theory
209
Users’
IT Needs
Tasks’ 
IT Needs
Mana-
gerial
IT Needs
Top Managers
Hardware
Needs
Software
Needs
Alignment 4
Alignment 5
Alignment 6
Alignment 7
Alignment 1
Alignment 3
Alignment 2
IT Utilization
IS Strategy
and Infrastructure
Users’ 
IT Culture
IT 
Technicians
Software
Analysts
Business Strategy 
and Infrastructure
Job Experts
Translation
Users’ Tasks
T
r
a
n
s
l
a
t
o
r
(
s
)
 
=
 
P
r
o
j
e
c
t
 
M
a
n
a
g
e
r
—
 
C
o
n
s
u
l
t
a
n
t
s
…
Translation
Translation
Level III = Delegates
Level II = Intermediaries
Level I = Actants
Translation
Translation
Alignments investigated in traditional IS strategic alignment models
User-centered alignments mostly ignored in the literature
Memo 4:    Extracted from Walsh, Renaud, and Kalika, 2013


210
Glossary
Basic Social Process: A generic theoretical construct explaining funda­
mental patterns in the organization of social behavior as it occurs over 
time, involving a change over time with discernible breaking points or 
points of transition or passage from one stage to another. Basic social 
processes may be psychological (e.g., rehumanizing, becoming, etc.) or 
structural (network, bureaucracy, routine) or may emerge as a combi­
nation of the two (e.g., rehumanizing knowledge work through fluctu­
ating support networks).
Category: The abstraction of individual concepts as a latent pattern.
Classic grounded theory: The systematic generation of theory from 
data as an integrated set of concepts and as hypotheses about the inter­
relationships among these concepts that focus on a main issue or con­
cern in the area under study and explain how that issue or concern is 
processed or resolved.
Code: The label attached to an incident during the substantive coding 
stage of analysis. Codes are often descriptive, particularly in the initial 
stages of data analysis.
Concept: The resultant higher level abstraction of a conceptual idea as 
emerging through the cumulative comparison of codes.
Constant comparison: A foundational pillar of classic GT in which 
data are analyzed as they are collected and constantly compared with 
previously collected data for interchangeable indicators of emerging 
concepts. Comparison first of indicators to indicators, then indicators 
to emerging concepts and finally concepts to concepts for emerging 
theoretical integration. Constant comparison is conducted in tandem 


Glossary
211
with theoretical sampling as a means of guiding the direction of further 
data sampling.
Core category: The category (i.e., theoretical model) that appears to 
explain how the main concern in the area under study is processed, 
managed, or resolved by its accounting for much of the variation in the 
way the main concern is addressed.
Data: The empirical basis for theory building. In GT, “all is data” mean­
ing that data are information that can be counted or expressed numeri­
cally (quantitative) or in the form of words (qualitative).
Dimension: A measurable component of a concept or category. Dimen­
sions are not interchangeable; they are complementary to define a 
concept or category.
Emergence: A result of GT’s open and exploratory stance in relation to 
approaching the research field. A foundational pillar of classic GT, 
emergence requires the researcher to remain open to discovering a 
main concern motivating the interest or action within the area under 
study.
Explication de texte: A technique in French literary analysis describing 
the close reading of text, line by line, to ascertain precisely what the 
author is saying without reinterpreting or implying meaning. In GT, expli­
cation de texte aids in generating concepts that closely fit what is going on 
in the substantive area by simply naming the concept as an abstraction.
Field notes: Notes recorded by the researcher while in the research 

setting that serve as in the moment reminders of incidents that may 
indicate potential concepts.
Formal grounded theory: A grounded theory developed at a higher 
level of empirical generalization and/or conceptual abstraction by 
extending the general implications of the core category of a substantive 
grounded theory through subsequent theoretical sampling across a 
wide range of settings and/or disciplines.
Framework: The general set of guidelines proposed for a specific 
approach to research, for example, action research, case-study research, 
classic grounded theory.


CLASSIC GROUNDED THEORY
212
Incidents: Indicators of phenomena or experiences as observed or 
articulated in data.
Interchangeability of indicators: Empirical incidents in data that are 
interchangeable in indicating a concept (i.e., indicating the same con­
cept) but that may also bring out different properties or values of the 
conceptual idea to enhance theory elaboration.
Main concern: The issue that emerges across the coded data as the 
prime motivator, interest, or problem in the research setting.
Memoing: The core ideational processing of theoretical ideas as they 
emerge through coding and constant comparative analysis, memoing 
is a continual process that helps to raise the data to a conceptual level 
and develop the properties of each category.
Methodology: The specific combination of research methods and tech­
niques used in a research project.
Methods: The ways in which data are collected, for example, inter­
views, observation, filming, or surveys.
Open coding: The initial coding stage in classic GT in which the ana­
lyst codes each incident in the data for as many concepts as possibly 
relevant. Open coding proceeds (in tandem with memoing) until the 
analyst perceives a pattern emerging that may indicate a potential core 
category.
Paradigm: A worldview, epistemological stance, or system of shared 
beliefs held by a community of research practice.
Preconceptions: Assumptions embedded within a researcher’s world­
view or professional discipline that are taken for granted and assumed 
to be of relevance.
Preconscious processing: Creative insights and theoretical realizations 
that occur seemingly serendipitously outside the analyst’s conscious 
efforts to analyze the data.
Property: A latent characteristic of a concept or category (e.g., 
authenticity, trust, healing) that cannot be directly and objectively 
assessed.


Glossary
213
Readiness to sort: The moment of ideational overload following data 
analysis when the analyst feels a need to hand sort memos for 
­
theoretical realization.
Readiness to write: The moment when theoretical completeness 
appears to be achieved and memos are sorted, prompting the analyst 
to write the first draft of the theory.
Secondary data: Data that have been collected for some other 
purpose.
Selective coding: Subsequent to the emergence of the core category, 
further data collection and analysis is delimited to achieving theoreti­
cal saturation of the core category and related concepts.
Substantive coding: The process of conceptualizing the empirical data 
in which the theory is grounded. Incidents in the empirical data are 
coded for indicators of concepts from which a grounded theory is then 
generated. Substantive coding consists of both the initial open coding 
of data as well as the selective coding of data once the core category has 
emerged.
Substantive grounded theory: A grounded theory developed within a 
specific setting and context and generalizable to a limited number of 
similar settings or groups.
Techniques: The instruments used to help analyze and make sense of 
the collected data, for example, text analysis, elaboration analysis, clus­
ter analysis, and structural equation modeling.
Theoretical coding: The final stage in classic GT theory genera­
tion, theoretical coding conceptualizes and models how the sub­
stantive codes may relate to each other as hypotheses to be 
integrated into the final theory. Theoretical coding is not selected 
and imposed on the theory but emerges through earned relevance 
as the best fitting theoretical integrator of the core category and 
related concepts.
Theoretical sampling: A foundational pillar of classic GT, the process 
through which empirical data are selected and collected while guided 
by the emerging theory as opposed to some predefined sample or 
unit.


CLASSIC GROUNDED THEORY
214
Theoretical saturation: When in coding and analyzing data, no new 
properties emerge but rather more indicators of the same properties 
continually emerge.
Theoretical sorting: The hand sorting of memos to provide emergent 
theoretical order and integration of ideas as the theorist seeks to 
theoretically discriminate as to where each idea fits in the emerging 
theory.


215
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228
Abstraction, conceptual, 10–11, 34
Academy of Management Journal, 28
Alvesson, M., 32
Analytic coding, 81
Analytic rules, 113–117
Audit trails, 94
Awareness, 5, 19, 108
general substantive theory and, 21
Axial coding, 108
Bacharach, S. B., 16, 104–105, 154, 
156–157
Bailey, D. E., 59
Bailyn, L., xv, 145
Bala, H., 153
Barrett, M., xiii
Basic social process, 32, 54, 107, 
111–112
Basics of Grounded Theory Analysis: 
Emergence vs. Forcing, 9
Baskerville, R. L., 20, 21, 88, 101, 135
Bias, perspective, 31
Big data, 62
Blocks, writing, 128–132
Blumer, Herbert, 4
Boxed-in research, 32
Brackenridge, C. H., 95
Breckenridge, J., 92, 95, 96
Bringer, J. D., 95
Brown, S. A., 153
Caricatures, philosophical, 17–19
Carry forward, 115
Categories
core, 77, 87–89, 103–104, 172
incident, 35–36
Causality, xiii
Cezanne, Paul, 9–10
Charmaz, K., 48, 92, 109, 121, 
159–160
Christiansen, Ólavur, 47
Classic grounded theory. See 
Grounded theory (GT)
Cluster analysis, 131
Coding, 7
axial, 108
defined, 98n1
examples, 201–205
memoing and, 6, 61, 89–94
open, 81–83, 169
selective, 53, 83–86, 173
substantive versus theoretical, 
52–54
theoretical, xiv–xv, 52–54, 
86, 87 (table), 104–109
types and stages in grounded 
­
theory, 80–86, 87 (table)
See also Data; Data analysis
Computer-assisted data analysis, 
95–96
Concepts, 3
comparing, 172
validity, 79
Conceptual abstraction, 10–11, 34
Conceptual analysis, 78
Conceptual elaboration, 37–38, 173–187
Index


Index
229
Conceptual formalization, 
22, 24 (figure)
Conceptual integration, 187–188
Conceptualization, 33–34
description versus, 50–52
Conceptual leap, 52
Conceptual modeling, 172
Conceptual tunnel vision, 37
Confusion in grounded theory, 46–47
description versus 
­
conceptualization, 50–52
emergence versus preconception, 
47–50
substantive versus theoretical 
coding, 52–54
Constant comparative analysis, 
33–37, 78–80, 169–172
Constant comparison, xii
Conventions, writing, 146–147, 
148 (figure)
Corbin, Juliet, 6, 8, 9, 108, 159–160
Core categories, 77, 103–104
emergence, 87–89, 172
Core variables, 115
Creswell, J. W., xiv, 58
Critical realism, xii–xiii
Cutting off rules, 116
Dahl, R., 102
Data, xiv
big, 62
conceptual abstraction of, 10–11, 34
conceptual leaps in, 52
constant comparative analysis, 
33–37
doing classic grounded theory 
using mixed, 190–200
earned relevance, 37
explanatory power of, 57
literature reviews as source of, 63, 
145–146
as necessary basis for theory, 
58–62
overlooked sources of available, 
62–64
overwhelm, 79
philosophical stances and use of, 19
pink elephant syndrome, 33, 37
qualitative, xiv, 8, 58, 167–188
quantitative, xiv, 31, 58, 130–131
secondary, xiv, 58, 62–63
slices, 40, 59
substantive coding of, 7, 52–54
theoretical coding of, xiv–xv, 
52–54
theoretical sampling, 37–40
writing classic grounded theory 
using mixed, 143–146
writing grounded theory using 
mixed, 125–126
See also Coding; Data collection
Data analysis, 76–77
classic grounded theory, 164–166
and collection as iterative process, 
72–73, 94–95
computer-assisted, 95–96
conceptual analysis as distinct 
from descriptive analysis, 78
constant comparative analysis, 
33–37, 78–80, 169–172
qualitative, 162–164
See also Coding; Shaping of theory
Data collection
and analysis as iterative process, 
72–73
classic grounded theory, 164–166
core category emergence during, 
87–89
field notes, 70–72
focusing of, 40
gaining access for, 64–67
instilling a spill for, 67–70
qualitative, 162–164
quantitative, 66–67
staying too long “in the field” 
and, 83
Deegan, J. G., 91
Delimiting, 84–85
Description versus 
­
conceptualization, 50–52


CLASSIC GROUNDED THEORY
230
Descriptive analysis, 78
Diagrams, 206–209
Dialogue, inner, 63
Digital culture index, 19
Discovery of Grounded Theory, 
The, xi, 1, 2, 5, 6
Earned relevance, 37
Eisenhardt, K. M., 154
Elaboration, conceptual, 37–38, 
173–187
Embedded data, xiii
Emergence, 7, 30–33, 107, 165–166, 168
core category, 87–89, 172
versus preconception, 47–50
Ethical issues in writing, 137–138
Explication de texte, 3, 35
Extant theory, 105–106
External/contextual blocks, 131–132
Factor analysis, 31
Fernandez, W., xv, 145
Fetters, M., 137
Field notes, 4, 70–72
coding, 81–82
Fine, G. A., 91
First draft writing, 127–128
Fit and validity, 155
Focus groups, 59–60, 61
Formal grounded theory, 20–22, 
23–24 (figure)
Formalization, 22, 24 (figure)
Framework, xv
Free style memoing, 91–92
Freshwater, D., 137
Fridlund, B., 59, 65, 68, 107
Gatekeepers, organizational, 65
Generalizability, 20
Generative mechanisms, xiii
Gibson, B., 2, 8–9, 53, 100
Glaser, Barney, xiv, xv, 145, 
159–161, 168
on analytic rules, 113–116
Anselm Strauss and, 1, 4–5
awareness index, 19
on coding, 81–83
on conceptual elaboration, 37–38
constant comparative analysis 
and, 34–35
on core categories, 88–89
on data slices, 40
on data sources, 62
on description, 78
on drugless trip, 77
early development of grounded 
theory and, 1–5
early influences and ideas, 2–4
on emergence of natural 
­
integration, 105
on evaluating grounded theory, 
155–157
on extant theory, 29
on field work, 70
on first draft writing, 127
on focusing data collection, 40
on formal grounded theory, 20–22
on grounded theory process 
­
properties and dimensions, 79
grounded theory reconcilable 
with Straussian grounded 
theory, 6–10
on “instilling a spill,” 68
on interative nature of grounded 
theory, 94–95
on interchangeability of 
­
indicators, 80
on line by line coding, 81
on memoing, 90–93
on memo sorting, 122–123
on methodological blocks, 130
on preconception, 47, 48
on preconscious processing, 
73, 92, 95
on selecting comparison groups, 38
on stance, 41
on theoretical coding, 106–107, 
109, 112–113
on theoretical sampling, 58–59, 
100–102


Index
231
on theoretical saturation, 103–104
on theoretical sensitivity, 41–42
on theory, 16
on types of data, 60
on in vivo and analytic 
coding, 81
on worrisome accuracy, 130
on writing blocks, 128
Gouldner, Alvin, 48
Grand theory, 22
Grand tour questions, 60, 68
Gregor, S., 16
Grounded theory (GT), xi–xii, 159–161
as basic social process, 32, 54, 107, 
111–112
consequences of paradigm war, 
152–154
constant comparative analysis in, 
33–37
continual remodeling of, 161
criteria for evaluating, 154–157
definitions in, xiii–xv
doing versus writing, 134–143
drive from substantive to formal, 
20–22, 23–24 (figure)
early development of, 1–5
emergence in, 7, 30–33
emergence of classic GT 
­
paradigm, 11–12
evaluating, 152–157
guidelines for publishing, 
148–150
iterative nature of data analysis 
in, 94–95
literature reviews and, 33, 
49–50
nature and power of classic, 
10–11
preconceptions in, 25, 30, 31–33
reconciling Glaserian and 
­
Straussian, 6–10
rupture theorizing, 25
stance, 41–43, 130–131
theoretical sampling, 37–40, 
58–59
theoretical sensitivity in, 5, 6, 
41–42
theory in, 15, 16–19
types and stages of coding in, 
80–86, 87 (table)
using qualitative data, 167–188
vocabulary, 9
writing using mixed data, 
143–146
See also Confusion in grounded 
theory; Writing
Guba, E. G., 153
Hämäläinen, M., 59, 68
Hartman, J., 2, 8–9, 53, 100
Hernandez, C. A., 9
Holton, J. A., xv, 88–89, 107, 113, 145, 
160, 167
Idea problems, 116
Iivari, J., 135
Incidents
categories of, 35–36
comparisons, 170–172
open coding of, 81–83
Incremental theorizing, 25
Information technology culture 
creep, 86, 128
Inner dialogue, 63
Integration, conceptual, 187–188
Integrative fit, 115–116
Interchangeability of indicators, 80
Interpretivism, 18
Interviews, 58–60
field notes during, 70–72
gaining access for, 64–67
instilling a spill in, 67–70
number of, 61–62
re-, 67
taping of, 64–65
See also Data collection
Intrasitive domain, xii
In vivo coding, 81
Iterative nature of data collection, 
72–73


CLASSIC GROUNDED THEORY
232
Jargonizing: Using the Grounded 
­
Theory Vocabulary, 9
Johnston, L. H., 95
Jones, R., 59, 68
Jørgensen, L. B., 102
Kalika, M., 206–209
Kefi, H., 21, 88, 101
Kelle, U., 42–43
Klag, M., 42, 52, 90, 91
Kriflik, G., 59, 68
Langley, A., 42, 52, 90, 91
Latent structure analysis, 34
Lazarsfeld, Paul, 2–5, 34, 80, 146, 191
Lee, A. S., 20
Leonardi, P. M., 59
Levina, N., xv, 145
Line by line coding, 81
Literature reviews, 33, 49–50
initial, 63–64
as source of data, 63, 145–146
use in writing, 124–125
Locke, K., 53, 91
Lomborg, K., 102
Lyytinen, K., 135
Main concern in area under study, 8
McCallin, A., 43
Mechanics of sorting, 116
Memoing, 6, 61, 89–94, 115
sorting, 121–123
Merton, Robert, 2, 3–4, 22
Metatheory, 20
Methodological blocks, 130
Methodological issues in writing, 
138–143
Methodology, xv
Methods, xiv
Middle-range theory, 22
Mingers, J., xiv
Mitcham, C., 33, 37
Mixed design grounded theory, 
­
125–126, 143–146, 190–200
Modeling, conceptual, 172
Modifiability, 155, 156
Monet, Claude, 9–10
Morgan, D., xv, 161
Morse, J. M., 33, 37
Nathaniel, A., 68
Natural science model, 18
Netting, F. E., 155
Observation, 61
O’Connor, M. K., 155
Open coding, 81–83, 169
Osler, William, 70
Ostman, M., 59–60
Overlooked sources of data, 62–64
Pacing, theoretical, 116
Paradigms, xv, 161
war, 152–154
Partial least squares (PLS) 
approach, 31
Partington, D., 7, 28, 29
Patton, M. Q., 152
Pedersen, P. U., 102
Perpetual outgrowing, 129
Personal blocks, 128–129
Perspective bias, 31
Petersson, K., 59, 65, 68, 107
Philosophical caricatures, 17–19
Piko, L. M., 109
Pink elephant syndrome, 33, 37
Positivism, 18
Post-it notes, 109, 110 (figure)
Preconceptions, 25, 30, 31–33
versus emergence, 47–50
Preconscious processing, 73, 92, 95
Qualitative data, xiv, 8, 58
classic grounded theory using, 
167–188
collection and analysis, 162–164
description, 51–52
doing classic grounded theory 
using mixed quantitative and, 
190–200
writing grounded theory with 
mixed quantitative, 125–126


Index
233
Quantitative data, xiv, 31, 58
doing classic grounded theory 
using mixed qualitative and, 
190–200
techniques in GT stance, 130–131
writing grounded theory with 
mixed qualitative and, 
125–126
“Quantomania,” 2
Readiness to sort, 121
Readiness to write, 121–122
Reasoning, 19
Reinterviews, 67
Relevance, 155, 156
earned, 37
Renaud, A., 206–209
Rhetorical issues in writing, 
135–137
Richardson, L., 137
Rules, analytic, 113–117
Rupture theorizing, 25
Sampling, theoretical, 37–40, 58–59, 
100–103, 172
Sandberg, J., 32
Sandgren, A., 59, 65, 68, 85, 107
Saturation, theoretical, 35, 
103–104, 173
Scott, S., xiii
Secondary data, xiv, 58, 62–63
Selective coding, 53, 83–86, 173
Sensitivity, theoretical, 5, 6, 
41–42, 102
Serendipity, 91
Shaping of theory, 99
analytic rules and, 113–117
theoretical coding and, 104–109
theoretical sampling and, 100–103
theoretical saturation and, 
103–104
theoretical sorting and, 109–113
See also Data analysis
Slices, data, 40, 59
Snowballing sampling, 59
Software analysis, 95–96
Sorting, theoretical, 91, 109–113
analytical rules, 115–116
readiness, 121
starting, 115
Stale dated, description as, 51–52
Stance, 41–43, 130–131
Statistical insignificance, 40
Stjernsward, S., 59–60
Strauss, Anselm, 1, 4–5, 22, 159–160
awareness index, 19
on axial coding, 108
on data slices, 40
on data sources, 62
on emergence of natural 
­
integration, 105
on evaluating grounded theory, 
155–157
on extant theory, 29
grounded theory reconcilable 
with Glaserian grounded 
­
theory, 6–10
on methodological blocks, 130
on selecting comparison 
groups, 38
on theoretical sampling, 
58–59, 100
on theoretical sensitivity, 41–42
Structural equation modeling (SEM), 
31, 131
Substantive coding, 7
versus theoretical coding, 52–54
Substantive formalization, 
22, 24 (figure)
Substantive grounded theory, 20–22, 
23–24 (figure)
Suddaby, R., 28–29, 41–42, 77
Techniques, xiv–xv
Te’eni, D., 135
Theoretical coding, xiv–xv, 
86, 87 (table), 104–109
versus substantive coding, 52–54
Theoretical pacing, 116
Theoretical sampling, xii, 58–59, 172
Theoretical saturation, 35, 
103–104, 173


CLASSIC GROUNDED THEORY
234
Theoretical sensitivity, 5, 6, 41–42, 102
Theoretical Sensitivity, 5, 6, 41
Theoretical sorting, 91, 109–113
analytical rules, 115–116
levels, 116
readiness, 121
starting, 115
Theorizing, incremental and 
­
rupture, 25
Theory, 15, 16–19, 99
diagrams as precursors of, 
206–209
emergence, 165–166, 168
extant, 105–106
theoretical sampling and shaping 
of, 100–103
Thomas, M. L., 155
Thulesius, Hans, 59, 65, 68, 70, 
91–92, 107
Triangulation, 31
Tukey, J. W., 31
Validity, concept, 79
Variables, core, 115
Venkatesh, V., 153
Walsh, I., xiii, xv, 19, 21, 57, 59, 88, 
101, 126, 138, 145, 153, 160
coding examples, 201–205
diagram examples, 206–209
on doing classic grounded theory 
using mixed data, 190–200
Weick, K. E., 16, 20
Whetten, D. A., 154
Workability, 155, 156
Writing, 120–121
blocks, 128–132
classic grounded theory using 
mixed data, 143–146
conceptual integration and, 
187–188
conventions, 146–147, 
148 (figure)
doing versus, 134–143
ethical issues in, 137–138
first draft, 127–128
formats, 136–137
grounded theory with mixed 
quantitative and qualitative 
data, 125–126
guidelines for publishing classic 
grounded theory in top tier 
research journals, 148–150
literature reviews used in, 
124–125
methodological issues in, 
138–143
process leading to readiness to 
write moment in, 121–123, 
124 (figure)
rhetorical issues in, 135–137
Zachariadis, M., xiii
Zetterberg, Hans, 2, 3–4