Qualitative Data Analysis

Using and Interpreting Qualitative Data:
How and What We Learn from Interviews, Focus Groups, and Participant Observation
Daniel Dohan, Ph.D.
Institute for Health Policy Studies & Dept. of Anthropology, History, & Social Medicine University of California, San Francisco

Research Background
‡ Poverty, culture, and healthcare ‡ Projects and methods
± The Price of Poverty: Solo p-o ± Cancer & culture: Solo p-o and iv¶s; team focus groups ± Welfare & substance abuse: Team iv¶s ± Poverty & stigma: Team p-o and iv¶s

Overview
‡ Qualitative and quantitative approaches ‡ Producing qualitative data ‡ Analyzing & publishing qualitative results

All Researchers Face Four Fundamental Tasks
1. 2. 3. 4. ‡ Selecting subjects to study Interacting with subjects to gather data Avoiding arbitrary findings Convincing others of what you found Quant & qual approach tasks differently
± Quantitative: four R¶s ± Qualitative: four P¶s

Different Approaches to Research: 4 ³R¶s´ versus 4 ³P¶s´
Research Task How do I select research subjects? How do I work with subjects to get data? How do I avoid arbitrary findings? How do I convince others of my findings? 4 R¶s (Quantitative) Representativeness (non-)Reactivity R Reliability Replicability 4 P¶s (Qualitative) Purposefulness Participation Process Particularity

Tasks by Research Activity
Research Task How do I select research subjects? How do I work with subjects to get data? How do I avoid arbitrary findings? How do I convince others of my findings? Research Activity

Data Collection

Data Analysis

Quantitative and Qualitative Research Activities
Research Activity Quantitative Approaches
- Representativeness: Random samples of predetermined groups -Reactivity: Fixed data collection instruments -Reliability: Hypothesis testing via statistical inference -Replicability: Standard reporting formats (tables, etc.)

Qualitative Approaches
-Purposefulness: Sites and subjects sampled according to needs -Participation: Flexible data collection strategies -Process: Iterative coding and memoing to refine results -Particularity: Narrative reports of findings in context

Data Collection

Data Analysis

Quantitative and Qualitative Research Activities
Research Activity Quantitative Approaches
-Representativeness: Random samples of predetermined groups -Reactivity: Fixed data collection instruments -Reliability: Hypothesis testing via statistical inference -Replicability: Standard reporting formats (tables, etc.)

Qualitative Approaches
-Purposefulness: Sites and subjects sampled according to needs -Participation: Flexible data collection strategies -Process: Iterative coding and memoing to refine results -Particularity: Narrative reports of findings in context

Data Collection

Data Analysis

³R¶s´ or ³P¶s´? Depends on Your Question
‡ R¶s
± Population is well defined, accessible, and appreciates non-reactivity ± Available measures are appropriate and support hypothesis testing

‡ P¶s
± Population is unclear, inaccessible, or uncomfortable with research institutions ± Available measures are unavailable, problematic, or undesirable

Collecting Qualitative Data
‡ Talk to people
± Individual interviews, focus groups

‡ Interact with people
± Participant-observation (p-o)

‡ Read what people write
± Scholarly publications (literature reviews) ± Private archives (historical analyses) ± Popular publications (content analyses)

Qualitative Data Production: Interviews, Focus Groups, P-O
HI Interviews Focus Groups P-O LOW ‡ Control over production
± Specificity of data for research question

‡ Scalability of production
± Amount of data that can be collected

‡ Intrusiveness of production
± Range of addressable questions

Analytic Principles
‡ Analyze cases
± Retain holism, contingency, complexity ± Balance analysis and data

‡ Analyze iteratively
± Let new data inform ongoing analysis ± Revise analytical categories as needed ± Pursue new questions that emerge during write-up

Analytic Processes
‡ Coding data
± Mark, corral, and reduce data ± Start with codes a priori or allow to develop ± Codes evolve with time and experience

‡ Analyzing data and codes
± Mimic quantitative by counting, correlating ± Reduce data and focus analysis ± Proliferate codes to see layers of meaning

Computer Assistance
‡ Does not alter analysis process ‡ Usually not a shortcut or timesaver ‡ Programs fit different data & needs

Computer Software
‡ Atlas-ti: large datasets, unstructured coding, mimic paper code & sort ‡ NUDIST: large datasets, structured coding, mimic quant analysis ‡ NVivo: less data, unstructured coding, find patterns/relationships in codes ‡ Folio Views: huge datasets, focused coding, search & sort

Publishing
‡ Journals approach to qualitative findings
± CMP, SSM: collect/analyze data, send it in ± AJPH, HSR, JNCI: qualitative is exception

‡ Targeting the ³exceptional´ journals
± Supplement quantitative approach ± Mimic quantitative approach ± Answer question quantitative approach can¶t