Qualitative Data Analysis

As you may recall from the Data Collection section of The Power of Proof, qualitative collection techniques include observations, interviews, focus groups, and questionnaires. The data collected from these techniques come in the form of notes, verbal answers, transcripts, and written responses. They generally include a respondent’s thoughts, feelings, and perspectives.

This  type  of data is primarily expressed in    terms   of   themes,   ideas,  events, personalities, histories, etc. Data are gathered through methods of observation, interviewing and document analysis. These results cannot be measured exactly, but must be interpreted and organized into themes or categories. The primary purpose of qualitative data is to provide information to the people involved in the project. This standard of usefulness is an important one to keep in mind when analyzing qualitative data.¹

Here are a few basic steps for analyzing qualitative data²

1. Have several people read the transcripts, field notes, or documents through to get an overall sense of the data.
    
   • Note the common themes that arise related to each evaluation question.
   • Note whether any important themes arise that are not related to the evaluation questions.
      
2. Reread the material, looking for details and patterns related to each common theme. Begin by asking these questions:
    
   • What patterns and common themes emerge in responses to specific questions or items? How do these patterns (or lack thereof) help to illuminate the broader evaluation question(s)?
   • Are there any deviations from these patterns? If yes, are there any factors that might explain these atypical responses?
   • What interesting stories emerge from the responses? How can these stories help to illuminate the broader evaluation question(s)?
   • Do any of these patterns or findings suggest that additional data may need to be collected? Do any of the evaluation questions need to be revised?
   • Do the patterns that emerge corroborate the findings of any additional qualitative analyses (e.g., document review) that have been conducted? If not, what might explain these discrepancies?

3. Organize responses to an evaluation question into similar categories (e.g., concerns, suggestions, strengths, weaknesses, similar experiences, program inputs, recommendations, outputs, outcome indicators, etc.)
    
4. Label the categories or themes, (e.g., concerns, suggestions, etc.)
    
5. Assess whether there appear to be any patterns, or associations and causal relationships in the themes, e.g., all people who attended programs in the evening had similar concerns, most people came from the same geographic area, most people were in the same salary range, people who were enthusiastic tended to have participated in the role play, etc.
    
6. Keep all commentary for several years after completion, in case it is needed for future reference.

Back to Analyze the Data section

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1. Source: Health Canada. (1996). Analysing and interpreting data. In: Guide to project evaluation: A participatory approach. Ottawa: Minister of Health & Welfare Canada.

2. Source: : McNamara, C. (1999). Analyzing, interpreting and reporting basic research results. Retrieved July 21, 2004 from The Management Assistance Program for Nonprofits web site.