Book Review: Ditching Description: From Data to Abstraction

Susan Stillman, Director of Ed for Six Seconds

Barney G. Glaser (2013). Getting Out of the Data: Grounded Theory Conceptualization, Mill Valley: Sociology Press

Overview

In the first chapter of this book, Glaser explains his purpose to help the researcher use the constant comparative method to discover and name patterns in the data, relate them to each other, generate a “conceptual hypothesis” (p. 2), and allow a multivariate substantive theory to emerge around a core variable. The first sentence demonstrates his intent to help the reader in “getting out of the data” into “conceptualization” (p. 1).  In preparation for writing this review, I showed the book to a friend who has only a passing familiarity with grounded theory (GT). His immediate reaction upon reading the first sentence was to ask me whether the reader was already supposed to know what “getting out of the data” and “emergence of conceptualization” meant.  “All is data” (p. 67) and what good data for me, the reviewer!

I had made an assumption that all readers who picked up this book would understand the language used, and my friend’s comment made me realize this might not be the case.  This small volume is probably not the book for a casual reader with some curiosity about GT.  Glaser’s stated audience is the dissertation researcher, whom he believes would have the most energy, interest, and motivation to preserve the fidelity of classic GT (p. 4), and, therefore, would be looking for additional insights and strategies from newer works. This current book is intended as a “synopsis” (p. 1), to be supplemented by reading Glaser’s other works. Glaser’s process, familiar to his readers, is to do GT, not just write about it. Glaser states, “This book will be a GT of GT use, as is my usual style” (p. 2).

In Getting Out of the Data, Glaser emphasizes, as he has done since 1965, the importance of constant comparative analysis for getting to conceptualization, and offers “help in getting out of the data” (p. 2).  Help comes in the form of his always evolving thinking on both constant comparative methodological steps such as eliminating preconceptions, collecting data, coding and naming patterns, and in his discussion of the underlying competencies needed for GT method success, such as motivation, patience, and the ability to tolerate ambiguity.  In this regard, Glaser shares his recent thinking on blocks to conceptualization, with specific suggestions and motivational support for getting through these obstacles successfully.

In preparing to write this review, rather than employ a linear chapter by chapter approach, I used the “skipping and dipping” (p. 75) method to memo and categorize ideas that caught my attention.  I’ve called the first section Back to Basics, as Glaser deepens aspects of constant comparative analysis familiar to many readers. In the second section, Blocks and Counter Blocks, I focus on these newer valuable additions to Glaser’s established repertoire of directives to researchers.

Back to Basics

Term clarification:  As an experienced teacher of GT methodology, I have often fielded questions on the meaning of common GT terms. Glaser provides a worthwhile clarification of these terms when he reiterates that code, category, property, and concept are all synonymous names for patterns. I have had students stressed by their attempts at distinguishing between them, and this clarification will help.  Glaser also refutes the use of non-GT terms, such as findings. “Treating a code as a finding is a misnomer “ (p. 61).  Validity is another concept often misunderstood by GT students, and Glaser reminds the reader that GT is concerned only with grab, fit, relevance, workability, and integration, not proof.  Similarly, Glaser does not skirt around QDA-like terms. He succinctly pronounces, “notions of objectivist, constructionist, subjectivist, reflexivist…are just irrelevant for GT. They are notions relevant only for QDA description” (p. 68).

Preventing preconceptions: In Chapter 3, Glaser provides an extensive explanation of preconception through careful attention to open questioning, open coding, and temporarily setting aside both literature and professional or ideological biases.  He highlights the joy and autonomy in staying open to what participants are really working on.  He stresses the conflicts that may arise when one’s GT analysis is not in sync with prevailing principles and beliefs in one’s field of study. Glaser explains that the “jargonized multiple version view of GT” (p. 2) does not have the same directives, and by using preconceived concepts or questions, the researcher does not stay open “to what is really going on” (p. 27). Glaser reminds the reader of the importance of studying not “what ought to be” but “what is” (p. 28). One significant point for me regards the application of GT in the world beyond the dissertation.  Glaser stressed that in many fields, such as education, health care, and management, effective leaders must be attuned to the importance of not preconceiving what their clients or customers want. The value of a GT trained analyst in any organization should not be underestimated.

Naming patterns: Throughout this book, and in the dedicated chapter 6, Glaser shares ways to sharpen skills in naming codes and expounds on what patterns are and how to name them, thus moving from descriptive to conceptual. He cites Simmons, who said, “codes are abstractions of patterns, not mere descriptive summaries“ (p. 17).  Having a repertoire of substantive code names aids in one’s ability to “get it” and think conceptually. In our GT summer sessions at Fielding, one exercise we used to do involved having participants say their “favorite” core variable, from past GT studies. Not only did this encourage familiarity with published GT research, it also helped students become familiar with naming patterns and excited at the possibilities for their own theory development.

In chapter 6, Glaser discusses the use of gerunds in naming patterns and the danger of forcing gerunds or over-gerunding (p. 52).  In the same chapter, Glaser explores issues associated with using in-vivo codes.  Glaser ends by commenting on a question that students frequently ask.  Once a core category is established, and selective coding begins, does the analyst use that named category in the grand tour question of future interviews?  Glaser adamantly replies that one must keep to the respondent’s “drift and emotion WITHOUT mentioning the name of the new core category to distract or bait them out of their venting” (p. 58). Appreciating the directness of his response, I feel this question could bear more discussion.

Trust in emergence: Knowledgeable readers will know that emergence means emergence of conceptualization and that the path to conceptualization is through constant comparative analysis (p. 1), but, in this volume, Glaser takes a fresh look at this important GT concept, a way out of the “fear zone” (p. 88). “Trust in emergence” (p.1) is a mantra that must continually be revisited. Students often tell me that when they return to a concept in one of Glaser’s books, after not understanding it earlier, it gradually, or sometimes suddenly, begins to make sense. I believe that “trust in emergence” is not only trusting the constant comparative process, but trusting also one’s training and one’s ability to develop skills experientially and incrementally.

Resist succumbing to one-incident codes: In the first few chapters, Glaser repeats the familiar exhortation to look for interchangeable indicators that show a pattern and to not succumb to the descriptive proliferation of one-incident codes. Glaser emphasizes that one indicator does not a pattern make. In my experience, students often end up with dozens of codes, because they create them from single indicators, rather than using the constant comparative method to pare down to the relevant, precise pattern name that has the most imagery and grab. This advice helps students to not get mired down in particularistic qualitative data analysis (QDA)-like description; it gives students something to guard against.

Accepting one’s limitations: Glaser (2004) wrote:

A researcher requires two essential characteristics for the development of theoretical sensitivity. First, he or she must have the personal and temperamental bent to maintain analytic distance, tolerate confusion and regression while remaining open, trusting to preconscious processing and to conceptual emergence. Second, he/she must have the ability to develop theoretical insight into the area of research combined with the ability to make something of these insights. He/she must have the ability to conceptualize and organize, make abstract connections, visualize and think multivariately. (pp. 9-10)

The ability to conceptualize is a primary characteristic of a successful GT researcher. I have had many students struggle with conceptualization and others for whom it comes naturally. Conceptualization can be developed through incremental learning (Simmons, as cited in Glaser, 2011, p. 38). That’s good news for students and their GT trained mentors. A caveat — learners must not get assistance from non-GT supervisors, as this will derail their emergent understanding and process development.

While many people can be taught to conceptualize, Glaser insists that conceptualization is not for everyone. This is a powerful statement worth more discussion. Do people self-select to do a GT study? Do they do so because they know that they already conceptualize naturally? What if they are not aware of their limitations? In my experience, many students do not realize their conceptual shortcomings until well into their study. Glaser advises returning to QDA if one experiences continued difficulty in conceptualizing, but, for dissertation students who have already written proposals and IRBs, this is easier said than done. Students must also tolerate confusion and to tolerate “confusions [sic] attendant regression/depression at times” (p. 22). An experienced mentor may be needed to assist a student in evaluating his or her abilities in these three and other related abilities and accepting limitations.

Valuing the preconscious: Related to tolerating confusion and trusting in emergence, the preconscious is an important precursor to conceptualization. Glaser instructs researchers to, again, trust in emergence and allow the preconscious to do its work. According to Glaser, submitting to preconscious processing obviates the need to preconceive. Further discussion of this concept would be worthwhile in future works.

Jumping in and jumping out: The way to learn is to let go of preconceptions and jump in, or as Glaser says, “ just do it” (p. 16). It takes courage to jump in and stay motivated through confusion and blocks. It also takes skill to terminate the constant comparative method when theoretical saturation is reached. After open coding, many GT researchers find themselves overloaded with potential core variables. I found it refreshing that in the discussion of code overload, Glaser advises the researcher overwhelmed by too many codes to “take a chance” on a core category to prevent “over-coding and to get to a parsimonious substantive theory” (p. 8). Glaser advises the researcher recognize the many codes that may not fit the core variable and file them away for a later study.

Writing it up: In a brief but powerful chapter, Glaser reviews some key points about writing. He directs the reader to earlier extensive literature on writing (1978) and to his Appendix, a detailed examination of conceptual writing issues, directives, and strategies, also found in Glaser (1998). Conceptual writing is a logical re-ordering of memos from a memo bank, sorted and related to each other and to a conceptual code, the core variable, and often to a theoretical code.  Many a student can come up with excellent pattern naming and write conceptual memos, but will not carry over the conceptual rigor to the actual dissertation. Experienced GT mentors can help students keep their GT writing concise, logical, and focused on concepts not description. Glaser’s two conceptual writing rules, “think theory write substance” (p. 109) and “relate concept to concept instead of concept to people” (p. 109) are worth further exploration.

Blocks and counter blocks

Recognizing blocks: In the second portion of this volume, Glaser addresses blocks to conceptualization, reiterating many earlier points in the volume. He provides a substantial list of blocks, with the intention to focus attention and thought and help readers avoid or handle them with a variety of GT-tested strategies. A partial list of blocks follows:

Authoritative blocks, preconceptions, inability to adequately conceptualize, the initial confusion and regression, multi-version view of GT, QDA requirement blocks, data collection overload, peer reviews, dealing with jargonizing GT, and being a novice both in experience and in scholarship with GT. (p. 83)

The erosion of classic GT by remodeled GT is a familiar theme in Glaser’s writing, yet here, Glaser alludes to its detrimental influence on conceptual coding.  Glaser reiterates that not only researcher predilection for QDA, but also prior academic training, often makes the GT method more difficult and confusing for novices, due to the “many positivistic rules and methods procedures for description that inhibit their openness to knowing and that keep them preconceiving” (p. 40).

Glaser’s responses to quotes from social constructivists are fascinating for elucidating the repeated concerns that may derail GT students.  In one example, Glaser replies to the frequent QDA admonition that all meanings are co-created.

Quote: “constructionists acknowledge the mediating role of how categories and concepts are constructed by the interviewer and respondents as co-producers of knowledge.”

My Comment: Thinking about this statement would block anyone from coding. It sews [sic] doubts about codes using the cc method for abstraction in favor of accurate description, if ever achieved without argument doubts. It puts more block on abstract coding by emphasizing coverage of descriptive data and worse yet, by emphasizing the particularism of each individual respondent, so impossible to generalize. If a bias exists in any one interview, it is just another variable to be conceptualized. It is hard to jump into GT conceptual coding thinking about all this, which has a series of descriptive concerns with no realization that GRT coding follows a pure, variable conceptual track. (pp.101-2)

Students will be familiar with many additional blocks noted by Glaser: “School PhD requirement structures, PhD formats, department structures and perspectives, inexperienced GT professors as supervisors or external critics, preconceptions from many sources, IRB requirements…tape recording…” (p. 100). Glaser’s comments will be useful to students who have had to defend their codes to dissertation committees who demand quantifiable responses to “how many participants said this?” Having to report “findings” to dissertation committees is still a reality for many GT learners. The encouragement to GT students to keep away from “validity” and “proof” concerns and rather focus on “fit” and “relevance” may help writers prepare their dissertation drafts for committee review.

Glaser also responds to the perennial question about recording interviews, which he views as another block to conceptualization.  Whether or not one records, Glaser urges the analyst to take field notes for immediate coding and not get bogged down with waiting for transcriptions. He also invokes “all is data,” and urges the student to use every opportunity for data gathering, whether in casual conversation, observations, or events, without worrying about IRB approval, which would be virtually impossible to get.

In my experience, skilled GT supervisors can help students shape their proposals and manuscripts into whatever preconceived university template is required, without sacrificing the basic tenets of GT.  This concern could use even more detailed advice, perhaps in future volumes, from mentors who have shepherded GT dissertation students to successful completion at universities with strict structuring requirements.

Countering blocks: The inclusion of indicators of conceptual blocks is perhaps the most important contribution of this volume. Normalizing these issues, as Glaser has done, should help GT researchers to not feel personally at fault if they encounter a block.

Not only must one acknowledge blocks, one must be able to overcome them. The following list of attitudes and strategies summarize the skills needed.

1. skill of tolerating ambiguity; willingness to not know
2. ability to let go of preconceptions
3. acceptance of the occasional depression/regression when one feels stuck
4. ability and motivation to conceptualize
5. patience
6. ability to overcome fear
7. willingness to put aside issue orientations of everyday life (p. 83)
8. orientation to resist authoritative guidance
9. personal pacing

10. avoidance of speculation and meaning making

11. seeking out knowledgeable GT mentoring

Motivational support

Motivation to do GT begins with excitement for discovering what is really going on, yet, to sustain it, one must be aware of potential blocks and adopt strategies to avoid and overcome. Glaser explains that success in generating preliminary codes seems to aid most researchers in developing confidence. Glaser references Holton’s cogent explanation of how successful attempts generate motivation (p. 41) and Simmons, who wrote “the ‘aha’ moments of experiential coding generate excitement and a feeling of satisfaction that provide motivation and keep the learner moving forward in the learning process” (p. 38). Another source of motivation is the idea that people pattern naturally. Glaser might expand on this concept, exampling and encouraging practice in naming daily patterns, to give additional comfort to those who are less sure of their coding ability.

I was pleasantly surprised to note Glaser’s strong support for online GT groups. Several years ago, Glen Gatin and I, both former students of Odis Simmons, initiated an online group to support GT students at our university. As students completed their dissertations and new students joined, the group evolved into a self-directed, self-empowered learning community, able to provide sound, consistent assistance and clarity to its members as they navigate doing GT. Yalof (2012), a group member, discovered the core variable, Marshaling Resources that explains the process whereby online learners create their own supports, in direct relation to the absence of institutional resources, as many students do who join online groups. “Exampling makes for strong motivation to code and generate discoveries” (Glaser, 2011, p. 21). This group’s work together constitutes a form of exampling, wherein students learn from each other’s struggles to navigate the constant comparative method. Motivation is lent further momentum by the successful completion of other students in the group and their continued resourceful support.

In conclusion

Some of the most interesting aspects of this book are not necessarily new ideas, but strengthened, reflective sections on many topics that Glaser has previously written about. The focus on conceptualization, pattern naming, and the motivation and skills needed to overcome blocks are the highlights of this work. Glaser’s response to comments about both the GT process and QDA methods are extremely enlightening.

The processes of online group support might well be expanded in further work. As additional assistance to the struggling conceptualizer, I would like to see descriptive codes and conceptualization displayed side by side next to the same passage of text, with Glaser’s expert commentary.  An index and editing to correct typos would be a worthwhile addition to this inspiring, highly recommended book. In my opinion, not only dissertation researchers, but also all GT readers, who want to deepen their conceptual understanding, experience Glaser’s matchless tone, and enjoy his response to comments and questions, would do well to delve into this volume.

References

Glaser, B. G. (1965). The constant comparative method of qualitative analysis. Social        Problems, 12(4), 436-445.

Glaser, B. G. (1978). Theoretical sensitivity: Advances in the methodology of grounded      theory. Mill Valley, CA: Sociology Press.

Glaser, B. G. (1998). Doing grounded theory: Issues and discussions. Mill Valley, CA: Sociology Press.

Glaser, B. G., & Holton, J. (2004). Remodeling grounded theory. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 5(2). Retrieved from        http://www.qualitative-research.net/index.php/fqs/article/view/607

Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research.  New York, NY: Aldine de Grutyer.

Holton, J. (2010). The coding process and its challenges. Grounded Theory Review, 09(1). Retrieved from http://groundedtheoryreview.com/2010/04/02/the-coding-process-and-its-challenges/

Yalof, B. (2012). Marshaling resources: A grounded theory of online learners. [Doctoral Dissertation}. ProQuest Dissertations and Theses, 226. Retrieved from http://search.proquest.com.proxy1.ncu.edu/docview/1266249434?accountid=28180. (prod.academic_MSTAR_1266249434).