Making Sense of Qualitative Data: The Grounded Theory Approach to Discovery

1. Making Sense of Qualitative Data: The Grounded Theory Approach to Discovery Karen Locke, Ph D W. Brooks George Professor Mason School of Business College of William and Mary

2. Context • What is the situation in which we come to be participating in a workshop on grounded theory today?

3. Qualitative Research in management & Organization studies • Prosecuted within an increasingly differentiated institutional context • Without an infrastructure of courses and apprenticeships to match interest • An expanding “umbrella domain”

4. Where is the grounded theory tradition today 7,640,000

5. Contest & evolution

6. GROUNDED THEORY IN MANAGEMENT & ORG. STUDIES TODAY • GT as a signifier typifying non hypothetico-deductive research (the theory building)?? • GT as a ceremonial citation masking “common sensing” your way through data?? • GT as site of contested intellectual property rights • GT as part of a new landscape of methodological multilingualism?? • GT as part of researchers’ individually shaped methodological vocabularies and practices?? • GT as one vocabulary and set of practical heuristics evident in “discovery oriented” research practice.

7. Workshop Objectives • Understand grounded theory’s intellectual tradition • Understand the general contours of and logic underlying this foundational tradition in qualitative research • Learn and experience the practice of its operational procedures (e.g. theoretical sampling, category development, constant comparison, memoing etc) • Appreciate their contribution to “discovery”

8. Method Separates From “Discovery” • “Discovery concerns the origin, creation, genesis, and invention of scientific theories and hypotheses. Justification concerns their evaluation, test, defense, success, truth, and confirmation. Discovery is for description alone, for psychology and the history of the sociology of science. Justification, however, is for the philosophy of science and epistemology “ (Kordig, 1978; p. 110).

9. Yet… advantages of discovery oriented qualitative approaches? • Allow the “real” world of work to inform, press against and shape theorizing (urges theoretical open mindedness) • Can capture complexity through multi-faced accounts of action • Identify the process or “how” discovered outcomes are generated • Serendipitous findings can spur new research

10. …advantages of discovery oriented qualitative approaches? • Investigate how participants make sense of events and behaviors at work • Identify how local meanings influence behavior • Explore in detail how context shapes meaning, experience, and behavior -link to practice • Investigate new developments in the world of work

11. Discovery Achievements • Documenting Evolution and Change • Challenging Rational Perspectives on Workplace Behavior • Troubling Existing Theoretical Conceptions (Locke, 2011 Academy of Management Annals)

12. Discovery Necessitates A Recursive, Contingent Research Design “Funnel Shaped”(Hammersley & Atkinson, 1983) “Garbage Can II”(Grady and Wallston, 1988) “An iterative process that involves “tacking” back and forth between implications of components of design”(Maxwell,2005) Implications “The complete analysis isn’t” (Michael Quinn Patton, 2002) Flexibility in research question, data gathering, theoretical formulations The experience of ambiguity and instability in the process

13. An Interactive /Recursive Model of Research Design Conceptual Context Purposes Research Questions Methods Quality (Maxwell, J. 2005. Qualitative Research Design. Sage)

15. Openness & Emergence Embarking on a project without knowing where we will end up. That is being unsure • As to what our specific research question will be (Barley, 1990) • What data we will have gathered (Van Maanen, 1998) • What the project will end up being ‘a case of’ (Becker/ Ragin, 1992) • What concepts we will have developed (Agar 2006)

17. Observed Particulars Dave is research scientist who owns pants with duct-taped pockets to accommodate a set of forty-odd keys carried on two separate rings connected by a spring clip. In the 4 years since Dave configured his keys in this way, “the only times Dave has actually used the spring clip feature to separate his keys were to temporarily loan out one side or the other, take a leisure trip to Europe, and once while he moved house” (Nippert-Eng, 1996; p. 49).

18. Karen Locke Ph D College of William and Mary

19. Conjectural Work “There is nothing but imagination that can ever supply [us] an inkling of the truth. [We] can stare stupidly at phenomena; but in the absence of imagination they will not connect themselves together in any rational way” (C. S. Peirce CP 1:46) The ability to engage the confounding array of talk, action, feelings, images, and texts that is field data. And, in the context of this engagement, [without recourse to a prior theoretical specification] to imagine processes, structures, or characterizations such that were they operative, they would render the data intelligible.

20. GT practices drive this interplay

21. The Grounded Theory Approach An approach to qualitative analysis that develops abstract conceptualizations through microscopic data analysis.

22. Exercise: For your selected study… • What “phenomenon” was the researcher/s interested in exploring – what was the study’s purpose? • What did they do – what was their research design? • What references to GT did they make? • What practices did these point to? • How well did the study’s design and data gathering approach access the phenomenon? • What did the study yield? What categories? What theoretical narrative?

23. What practices make a theory grounded? • approach your study leary of the ways in which the phenomena has been framed and explained • organize your data gathering so that you can access in detail the perspectives of the actors involved in the phenomenon (how they understand their world, what they are doing and why) • sample theoretically • Conceptualize and code microscopically • analyze the data systematically and comparatively • continually enrich your thinking by reading widely around the phenomena • grow your theory by going back and forth between progressively more focused data and successively more abstract conceptualizations of them • integrate the emergent categories around a narrative

24. Analysis: “Heads Up” • Assigning meaning to unstructured and ambiguous data, within a flexible recursive process • Begin analysis as you gather data NOT after • Progressively narrow to one process within the data (this will not be linear!) • Narrow to one part of that process • Data gathering and analysis are iterative (expect analysis to impact data collection)

25. THE DEVELOPMENT OF CONCEPTUAL CATEGORIES (c.f. Grounded Theory) Fracture, Chunk and Name Data Compare, Compile & Rename Data Fragments Theoretically Driven Data Sampling Generate Provisional Conceptualizations with their Indicators Develop Robust Working Conceptualizations Process is “finished” when core categories are theoretically saturated – (in theory)

26. Character and Experience of Grounded Theory Data Sense Making Process • Creative opened-ended dimension • Mechanical procedural dimension • Individual analyst is central agent • Meaning making is richer when the process is a social one

27. I. Sampling in Grounded Theory Information not statistical generalization NOT random … never, never, never, never … BTW, did I mention sampling is never random?

28. Purposive & Theoretical Sampling -Driven by Information Needs • The logic of theoretical or purposive sampling is that you select units which will provide you rich information relative to your orienting research questions • Theoretical sampling to expand, check and refine conceptual categories. Conceptually-driven sequential sampling- usually not wholly pre-specified, but can evolve once fieldwork begins. • Sampling reflects researcher interests in the evolving fit between gathered data and the emerging theory. You sample for the purpose of developing your emerging theory.

29. Ensuring Sampling Focus: Clarifying units of Social Organization Expressing Our Phenomena • Social Practices – recurring categories of normal talk or action • Episodes – significant events in the life of… • Encounters – 2 or more persons mutually involved • Roles – categories of person (formal roles, informal roles, social types) • Relationships – parties interacting over period of time, viewing themselves as connected in some way (vary in a gazillion ways, interdependence, power, trust, information regarding each other, etc) • Groups • Organizations – goal pursuing entities with formal and informal strategies • Processes • May have interpretive, emotional as well as agency aspects (Adapted from Lofland & Lofland, 1995)

30. Data Forms • Interview transcripts – e.g. accounts of people’s experiences • Field notes – observations of what people do, say, and understand • Documentary evidence – policy documents, texts, memos, emails, accident reports, etc. • Visual elements, e.g. drawings, photographs, graphics, etc. (We end up with a large amount of unstructured and uncategorized data)

31. Ensuring Data Quality • Is it the right data for the job? • Does the data fit with your phenomenon? • Is it “thick” enough? • Do you have a detailed record of what those you are researching, do, see, hear, experience and understand • Do you have examples, histories, stories, explanations • Do you only have “opinions” … I think

32. Creating “Data” and “Making Meaning” Interactive, Recursive, and Interdependent Relying on multiple “meaning making” activities

33. Articulating What Your Data Might Mean • Meaning Making Activities • Developing contact summaries • Creating data units • Creating ideas about /Inventively naming data chunks (developing categories) • Conceptually developing categories through constant comparison • Analytic Memoing • Creating an overarching organization for your data (Action strategy models, conditions/consequences models, stage models, typologies, etc.) • Framing theoretical implications – contributing to the literature

34. A Different Analytic Procedure: Domain Analysis DOMAIN Professor (Cover Term) Is a kind of Semantic Relationship Motivator Decision maker Included Terms Spradley, J. (1980) Participant Observation

35. Other Potential Semantic Relationships • Spatial X is a place in Y / X is a part of Y • Cause-effect X is a result of Y • Rationale X is a reason for doing Y • Location for action X is a place for doing Y • Function X is used for Y • Means-end X is a way to Y • Sequence X is a step (stage) in Y • Attribution X is a characteristic of Y

36. Articulating What Your Data Might Mean How does identity expanding learning (learning that transforms who you are) occur?

37. II. Contact Summaries Begin actively thinking about data Create a general indexing system Miles & Huberman 1994

38. Contact Summary-How does identity expanding learning occur? • What people, events or situations were involved? • What were the main themes or issues? • What research questions did the contact bear most closely on? • What new speculations or guesses were suggested by the contact? • Where should you place most energy during the next contact, what sorts of information should be collected? [Miles & Huberman, 1994]

39. III. Chunking Observations to create “data” How do you take streams of observations (field notes, archival documents, transcripts) and turn them into little pieces of data that can be worked with analytically?

40. The Development of Conceptual Categories (c.f. Grounded Theory) Fracture, Chunk and Name Data Compare, Compile & Rename Data Fragments Theoretically Driven Data Sampling Generate Provisional Conceptualizations with their Indicators Develop Robust Working Conceptualizations Process is “finished” when core categories are theoretically saturated – (theoretically)

41. How would you chunk this data? SUBJECT: So right away, I know I'm not 3 making a decision, I'm making a 4 recommendation which is always easy to waffle 5 on, apologize for, or mitigate. 6 This also allows me to make, sort of – 7 I won't feel as constrained. I can probably 8 actually make a little bit more, the kind of 9 decision I want to make here, which is more 10 gut based than factual. 11 I can already tell this is going to get 12 political. I can already discern this is 13 going to be a powder keg.

42. Developing Conceptual Categories: Chunking Fracturing and Naming Data • Convention is line by line • In practice this may be a few words, a sentence, several sentences • It should be a meaningful, discrete unit • Name what’s happening (keep it active and dynamic) * You are trying to avoid generating impressionistic “themes” rather than data specified grounded categories

43. IV. Meaning Making / Open Coding Active Naming Imagining Comparing

44. Data as an indicator – “Seeing as…” Ceci n’est pas la soupe!

45. “SEEING AS” INVENTIVE NAMING OR CONCEPTUALIZING [We] “can stare stupidly at phenomena; but in the absence of imagination they will not connect themselves together in any rational way.” Peirce, 1896 This work of imagining what might be – accounting for, and at the same time remaining accountable to the data - is our concern

46. It was more than fifteen years ago that I entered the laboratory of Professor Agassiz, and told him I had enrolled my name in the scientific school as a student of natural history. He asked me a few questions about my object in coming, my antecedents generally, the mode in which I afterwards proposed to use the knowledge I might acquire, and finally, whether I wished to study any special branch. To the latter I replied that while I wished to be well grounded in all departments of zoology, I purposed to devote myself specially to insects. "When do you wish to begin?" he asked. "Now," I replied. This seemed to please him, and with an energetic "Very well," he reached from a shelf a huge jar of specimens in yellow alcohol. "Take this fish," he said, "and look at it; we call it a Haemulon; by and by I will ask what you have seen." With that he left me, but in a moment returned with explicit instructions as to the care of the object entrusted to me. "No man is fit to be a naturalist," said he, "who does not know how to take care of specimens."

47. Generative questions to pose to your data that facilitate conceptualization • What is happening? • What might this be a case of / what can I think of this as being about? • What is the actor doing? • What is the basic problem faced by the actor/s here? • What do these actions and statements take for granted? • What question about my topic does this data suggest? • What imagery or metaphor does this data evoke? How is this fragment the same as and different from others?

48. V. Meaning Making Developing Conceptual Categories

49. The Development of Conceptual Categories (c.f. Grounded Theory) Fracture, Chunk and Name Data Compare, Compile & Rename Data Fragments Theoretically Driven Data Sampling Generate Provisional Conceptualizations with their Indicators Develop Robust Working Conceptualizations Process is “finished” when core categories are theoretically saturated

50. Developing Categories From Data Chunks Category Working Label: Data Source: Data Samples Constituting Category: Interview # Interview #