Data Analysis, Coding, and CAQDAS

1. Data Analysis, Coding, and CAQDAS prepared by Jane M. Gangi, Ph.D. April 28, 2011

2. A reminder: • As you begin data analysis, you can review my February 17 powerpoint on data analysis • You can also review Bogdan and Biklen’s chapter on data analysis • And, some of the profiles also referred to data analysis

3. Why do data analysis?

4. What do you make of this text? “A young man walked along a country road and met an older man. They quarreled and the young man killed the other. The young man went on to a city, where he met an older woman and married her. Then the young man put his eyes out and left the city” (Erickson, as cited in Dyson and Genishi, 2005, p. 84)

5. Email me: gangij@wcsu.edu if you need help with analyzing the text on slide 3

6. What is data analysis? Glesne’s (2011) definition: • “Data analysis involves organizing what you have seen, heard, and read so that you can figure out what you have learned and makes sense of what you have experienced. Working with the data, you describe, compare, create explanations, link your story to other stories, and possibly pose hypotheses or develop theories” (p. 184)

7. Kinds of data analysis • Conversation • Narrative • Semiotic • Thematic (Glesne, 2011, p. 185) And, • Grounded • Discourse (and others)

8. What is a theme? “A theme is a pattern found in the information that at the minimum describes and organizes possible observations or at the maximum interprets aspects of the phenomenon” (Boyatzis, 1990, p. vii).

9. Thematic analysis • A search for themes and patterns • Constant case comparison: A search for variation in the data • Memo writing All which lead to………..coding (Glesne, 2011)

10. Obstacles to thematic analysis: “Projection is one our ego defense mechanisms….[that] can also become an obstacle to effective and insightful thematic analysis. It is simply ‘reading into’ or ‘attributing to’ another person something that is your own characteristic, emotion, value, attitude, or such” (Boyatzis, 1990, p. 13).

11. Half-empty or half-full: If you tend to see the glass half-empty, you may unconsciously see the glass-half empty in your research. If you tend to see the glass half-full, you may unconsciously see the glass half-full in your research.

12. What is a code? Saldaña’s (2009) definition: “A code in qualitative inquiry is most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data.….Just as a title represents and captures a book or film or poem’s primary content and essence, so does a code represent and capture a datum’s primary content and essence”(p. 3, emphasis added).

13. A second definition of code--Coffey and Atkinson’s (1996) “Coding can be thought about as a way of relating our data to our ideas about these data” (as cited in Boyatzis, 1990, p. 5)

14. A third definition of code: Lewins and Silver’s (2007) “Qualitative coding is the process by which segments of data are identified as relating to, or being an example, of a more general idea, instance, theme or category” (p. 81)

15. What contributes to a quality code? Includes • A label • Description or definition • Indicators • Examples • Exclusions, or special conditions (Boyatzis, 1990, p. 49)

16. Codes: Bogdan and Biklen (2007, pp. 174-180)

17. Codes: Bogdan and Biklen (2007, pp. 174-180), continued

18. Codes: Bogdan and Biklen (2007, pp. 174-180), continued

19. Coding • may be inductive (from the data) or • deductive (from theory or from previous research)

20. Saldaña (2009) …conceptualizes 30 codes. A select few: • In Vivo—select phrases from participants’ language • Emotion • Simultaneous—”multiple meanings” (p. 62)

21. Coding can be generated from • 1. themes or topics—initially from an interview or identified within the data • 2. ideas or concepts—derived from existing literature in the research area or developed from close reading and thinking about data • 3. language or terminology used in the data—whether that be words or phrases used by respondents or documentary evidence (Lewins & Silver, 2007, pp. 83-84)

22. To develop codes, ask: “What are people trying to do and through what means and strategies? How do people characterize others or their own situation? What sorts of assumptions about…student-teacher relationships, institutional expectations, normal childhoods, or good families undergird their actions?” (Dyson & Genishi, 2005, pp. 84-85).

23. Phases of coding: • First phase: Open (which may lead to fragmentation) • Second phase: Axial—compare, contrast, possibly merge codes from the first phase • Third phase: Selective—make decisions about what seems most relevant, and support your conclusions from your data (Lewins & Silver, 2007)

24. CAQDAS (computer assisted qualitative data analysis) • HyperRESEARCH 3.02: http://www.researchware.com/products/hyperresearch.html See two handouts on HyperRESEARCH • Nvivo7 • Atlas.ti5 • Ethnograph • MAXqda2 • QDA Miner 2.0 • Qualrus • Transana2

25. To use, or not to use, CAQDAS? The ethnographer who lived in a group home for a year, Edmond (2005): “In terms of my own research, I decided to transcribe all the tape-recordings and to incorporate them into my diary. While highly time-consuming, this approach allowed me to gain a real sense of the emerging themes, the structures and patterns of interactions and the characters involved” (p. 135)

26. Resources for CAQDAS • Computer Assisted Qualitative Data Analysis: http://caqdas.soc.surrey.ac.uk/ • Ethnograph: http://www.qualisresearch.com/ • HyperRESEARCH Teaching videos: http://faculty.education.ufl.edu/tsadler/hyperr/index.html

27. References Bogdan, R. C., & Biklen, S. K. (2007). Qualitative research for education: An introduction to theories and methods (5th ed.). Boston, MA: Allyn & Bacon. Boyatzis, R. E. (1990). Transforming qualitative information: Thematic analysis and code development. Thousand Oaks, CA: Sage. Dyson, A. H., & Genishi, C. (2005). On the case: Approaches to language and literacy research. New York, NY: Teachers College Press. Edmond, R. (2005). Ethnographic research methods with children and young people. In S. Greene & D. Hogan (Eds.), Researching children’s experiences: Approaches and methods (pp. 123-139). London, U.K.: Sage.

28. References, continued Glesne, C. (2011). Becoming qualitative researchers: An introduction (4th ed.). Boston, MA: Pearson. Lewins, A., & Silver, C. (2007). Using software in qualitative research: A step-by-step guide. Los Angeles, CA: Sage Publications. Saldaña, J. (2009). The coding manual for qualitative researchers. Thousand Oaks, CA: Sage.

29. Supplementary: Powerpoint slides I would share if we had more time

30. Add to last week’s rubric….Generic Approach Lichtman (2010): "Although many researchers choose a particular orientation or combination of approaches, others do not make such a choice; rather, they take a generic approach. Chenail discusses this idea in an interview (Lichtman, 2004). While many may have operated this way, only fairly recently has it been articulated as a generic approach....“ (p. 88).

31. Lewins and Silver (2007): Approaches to coding • Open, axial, and selective coding (Strauss and Corbin, 1998) • Descriptive, topic and analytic coding (Richards, 2005) • Provisional, core and satellite codes (Layder, 1998) • Literal, interpretive and reflexive indexing (Mason, 2002) • Descriptive, interpretive and pattern coding (Miles and Huberman, 1994) • Objectivist and heuristic codes (Seidel, 1998) (p. 82)

32. Inductive approaches to coding • Open coding—first coding stage “in which small segments of data…are considered in detail and compared with one another” • Axial coding—second coding stage: “Code labels and the data linked to them are rethought in terms of similarity and difference. Similar codes may be grouped together, merged…, subdivided…” • Selective coding—third stage: “Instances in the data which most pertinently illustrate themes, concepts, relationships, etc. are identified. Conclusions are validated by illustrating instances represented by and grounded in the data” (Lewins & Silver, 2007, pp. 84-85)

33. Deductive approaches to coding • Descriptive codes—the data is organized by descriptions found in the data, and emerge from “predefined areas of interest”—factual or theoretical • Interpretive codes—codes are “revisited….Similar aspects may be recodes where they exemplify a meaningful concept or relationship” • Pattern codes—”move to a more inferential and explanatory level.” For example, “respondents with certain similar characteristics”; the goal is to “identify meaningful and illustrative patterns in the data” (Lewins & Silver (2007), summarizing Miles & Huberman (1994), p. 86)