# Presentation: 197979

## 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

- 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 “R’s” versus 4 “P’s”

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

## Tasks by Research Activity

| Research Task | Research Activity |
| --- | --- |
| How do I select research subjects? | Data Collection |
| How do I work with subjects to get data? |  |
| How do I avoid arbitrary findings? | Data Analysis |
| How do I convince others of my findings? |  |

## Quantitative and Qualitative Research Activities

| Research Activity | Quantitative Approaches | Qualitative Approaches |
| --- | --- | --- |
| 

Data Collection |  Representativeness: Random samples of pre-determined groups
Reactivity: Fixed data collection instruments | Purposefulness: Sites and subjects sampled according to needs
Participation: Flexible data collection strategies |
| 

Data Analysis | -Reliability: Hypothesis testing via statistical inference
-Replicability: Standard reporting formats (tables, etc.) | -Process: Iterative coding and memoing to refine results
-Particularity: Narrative reports of findings in context |

## Quantitative and Qualitative Research Activities

| Research Activity | Quantitative Approaches | Qualitative Approaches |
| --- | --- | --- |
| 

Data Collection | Representativeness: Random samples of pre-determined groups
Reactivity: Fixed data collection instruments | Purposefulness: Sites and subjects sampled according to needs
Participation: Flexible data collection strategies |
| 

Data Analysis | -Reliability: Hypothesis testing via statistical inference
-Replicability: Standard reporting formats (tables, etc.) | -Process: Iterative coding and memoing to refine results
-Particularity: Narrative reports of findings in context |

## “R’s” or “P’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 “exceptional” journals
- Supplement quantitative approach
- Mimic quantitative approach
- Answer question quantitative approach can’t