Pamina M. Gorbach, MHS, DrPH University of California, Los Angeles (UCLA)

1. Using Qualitative Methods in STD/HIV Behavioral Research Pamina M. Gorbach, MHS, DrPH University of California, Los Angeles (UCLA)

2. What is qualitative research? "Qualitative" research is the study of the words and the meaning behind behaviors. Seeks to answer "WHY" people practice certain behaviors Collects words for data.

3. What is quantitative research? ”Quantitative" research is the study of numbers. Asks "how many" people practice "how many" different behaviors. Main tool is the survey. The aim is to find numerical patterns in responses to survey questionnaires or observed behaviors. Indicates magnitude of people's decisions and behaviors.

4. Uses of Qualitative methods Model 1 RESULTS QUALITATIVE QUANTITATIVE 1. A tool to generate ideas. 2. A preliminary step in developing a quantitative study. Steckler et al. 1992

5. Uses of Qualitative methods Model 2 RESULTS QUANTITATIVE QUALITATIVE 3. To help understand the results of a quantitative study. Steckler et al. 1992

6. Uses of Qualitative methods Model 3 RESULTS QUALITATIVE QUANTITATIVE 4. The primary data collection method – sometimes but not necessarily along with a quantitative study like a survey Steckler et al. 1992

7. Main Data Collection Methods • In-depth individual interviews • Focus groups • Participant observation • Pile sorting

8. Individual Interviews • AKA Ethnographic interview = open-ended, in-depth • interview with one individual. • Probes; collects respondent’s perspective • and words; follows guide; level of structure varies. • WHEN??? • Complex subject matter and knowledgeable • respondents • Highly sensitive subject matter • Geographically dispersed respondents • Peer pressure an issue - social desirability a threat

9. ADVANTAGES: Most in-depth. Why behaviors are practiced. Data on how people think and talk; conceptualizations of behavior. Exact words people & language use amongst themselves. “Emic” perspective = insider’s perspective. DISADVANTAGES: Based on a few people, usually not systematic sample, but purposeful or convenience sample. Not generalizable. Interviews very long, lots of data! Time consuming to analyze. Researchers opinions of what the data means. Individual Interviews

10. Focus Groups • Open-ended group interviews that promote • discussions between participants on specific topics. Usually 6-8 individuals matched by age, gender or ethnicity. • WHEN? • Group interaction important • Cost and timing issues • Idea generation • Problem identification and definition goal • Evaluating messages for an intervention

11. ADVANTAGES: Some people more comfortable and talk more openly in group settings. Natural way some people talk about problems and personal issues in some cultures. Collects information on social norms. DISADVANTAGES Difficult to access practice of very personal or sensitive behaviors in groups - may only learn about behavior people will admit in front of others. Not GENERALIZABLE Transcribing time consuming - often 30-40 pages each! Difficult to identify speakers. analytic challenge! Focus Groups

12. Participant Observation The researcher becomes participant in social event or group under study and records observations. Tell or not tell group being studied that it’s a study?? Advantages: Data is very deep and detailed. Disadvantages: Difficult to systematically collect; especially in middle of important moment - hard to take notes so details may be forgotten. Analytic methods for observation notes not well described.

13. Guidelines for Qualitative Data Collection • Don’t let interviewer/ facilitator dominate • Control interviews/ FGD • Avoid questions TOO open – burden on respondent • LISTEN!!! Ask followup questions • Record so have direct quotations • Use probes – start with neutral ones like pauses & repetition

14. Common Pitfalls in Data Collection • Equipment failure – tape recorder batteries die, machine breaks, tapes break, or ??? • Environmental Hazards – background noise Easton KL, McComish JF, Greenberg R. Avoiding Common Pitfalls in Qualitative Data Collection and Transcription. QUALITATIVE HEALTH RESEARCH, Vol. 10 No. 5, September 2000 703-707.

15. Transcription Errors Misinterpreted Words: Misheard Words: Jargon or Language Barrier Problems: Easton KL, McComish JF, Greenberg R. Avoiding Common Pitfalls in Qualitative Data Collection and Transcription. QUALITATIVE HEALTH RESEARCH, Vol. 10 No. 5, September 2000 703-707.

17. ANALYTIC APPROACH CASE: focuses on each person or each unit. When variation in individuals primary focus of the study. Use all the data from each person. Cross-Case: Groups together answers from different people to common questions; analyze different perspectives on central issues. BOTH: Start with case descriptions then describe variations in answers to common questions.

18. How will Theory be Used? • Apply existing? • Develop grounded?

19. Content or Theme Analysis WHAT: The identification of common themes and exploration of differences in how these themes are expressed and applied in a particular cultural context. HOW: Careful review of written transcripts noting specific words used by respondents to understand their systems of meaning. Spradley; 1979

20. CODING TEXT CODE: an abbreviation or symbol used to classify words in the text by categories. CODING: process to identify categories of meaning. Miles and Huberman; 1984

21. TYPES OF CODES DESCRIPTIVE: Summarizes, describes, or defines. e.g., CONCEPTUAL: Addresses the deeper meeting or concept behind something e.g.,

22. Coded Text

23. Coding Strategies • Individual – review and code alone • Consensual – review and code transcripts “together” and come to a consensus regarding codes in dispute • Separate – two or more researchers code text separately then compare

24. Making a Matrix or Display • Once the data are coded, develop a matrix • that displays identified categories by groups • of respondents. • Enter short quotes or summarizing remarks • from coded text into appropriate boxes in the • matrix by category following a precise set of • decision rules and criteria.

25. Making a Display Gorbach, PM et al 1997

26. More Displays Gorbach, PM et al 1997

27. And More Displays! Partnership Type by Group

28. Themes • Underlying constructs that emerge from the codes and patterns observed in the data and/or were predetermined by the researcher and are later verified by the data

29. Themes From Display: “Emerge” (1) Look for patterns (2) Build a logical chain of evidence (3) Make contrasts and comparisons (4) Cluster (5) Count - carefully

30. VERIFYING THEMES (1) Follow up on surprises (2) Triangulate - > other interviews, surveys (3) Make some if-then tests (4) Check out rival explanations (5) FEEDBACK - from informants, experts, colleagues, friends!

31. Presenting Themes

32. A Continuum of Disclosure Factors that influence HIV disclosure for MSM who disclose at different rates Disclosure much less likely Disclosure much more likely Sometimes Disclosure Private Place Knew his Status He asked me More than “Just Sex” I am responsible Rejection Drugs Public Place Nobody’s Business Denial Undetectable VL Jail Just Sex Shared Responsibility

33. Using Quotes: In Text Reasons to Not Notify: Hetero Men and Women Fear of gossip “I’m not going to tell noone because then it would be embarrassing on me - they would tell everybody else and I don’t want to be know as having no STDs.” 19 year old African American Male - GC “No, I figured they would go tell. That would be the talk of Seattle for me.”15 year old African American Woman - CT

34. Feelings Regarding Sex: Women Who Test Positve for HPV Doesn’t want to have sex “Oh yeah, it did. Right now, like I said, I don't want to really meet no guys, I mean, I could meet them, talk to them, but as further as having sex, no.” L2: 21/L/new/STD Scared/concerned to infect partners “Yeah I felt depressed, I'm like damn, can't have sexual, I'm scared to have sex with somebody, cause I'm scared I might give it to him…” A9: 21/AA/middle/STD Feel more vulnerable can acquire something else “I'm really not messing around, you know, that much no more, I don't, cause I don't, I just don't like the fact that, you know, like, somebody coulda, gave me something, or something like that, you know, and the act like, cool, they act like they don't have nothing, probably because they didn't' go and check” A5: 22/A/middle/STD

35. Shared Responsibility Sometimes Disclosure “It’s a 50-50 street…they should ask as well as I should tell.”(30/W/SEA) “We have responsibility in the sense that, yes, we should tell them…but they have the responsibility to ask. It’s a 50-50 thing. It just depends on who initiates it.”(35/AA/LA) “I am usually expecting someone else to say something, and if they don’t, I assume that they are positive or that it does not make any difference to him.”(52/W/SEA)

36. Text in Manuscripts From: Gorbach PM, Galea J, Amani B, Shin A, Celum C, Kerndt P, Golden M. Don’t ask, Don’t Tell: Patterns of HIV Disclosure Among HIV Positive Men Who Have Sex with Men (MSM) with Recent STI Practicing High Risk Behavior In Los Angeles and Seattle. Sexually Transmitted Infections, 2004 December, 80(6):512-517.

37. Quotes in Manuscripts: Tables

38. Data Analysis: Review of Steps 1. Collect data (conduct interviews/focus groups) 2. Manage data - transcribe interviews 3. Choose theoretical approach: grounded or applied 4. Determine analytic approach - case or cross-case 5. Code transcripts 6. Create matrices, displays - data reduction 7. Conduct theme analysis - identify themes 8. Draw conclusions - present themes

39. Improving Qualitative Data Quality • Data collection - consider strengths & weakness of indepth interviews vs focus groups (convenience, social desirability, efficiency) • Sampling – can move beyond convenience or “purposive” toward more representative • Analytic approach – use software for data management & text analysis, develop matrices during analysis, identify theme through content analysis, compare themes by sub-groups

40. Components of Data Analysis Interactive Model of Analysis: Miles and Huberman Data collection Data display Data reduction Conclusions: drawing/verifying