In previous weeks, I talked about collecting stories in a systematic way so that they can later be analyzed - this could be interview or focus group transcripts and notes; answers to open-ended questions in surveys; documented staff observations, etc.
There are two broad approaches you can take: 1) Focus on your key evaluation questions - what is it you’re trying to learn from these stories? Discard the info that isn’t relevant; or 2) Explore all the data, allowing for new themes and issues to arise.
Regardless of the way the stories were collected, think about how you can involve your stakeholders in this analysis, including your program participants. You can check in with them to make sure you’ve interpreted correctly what they’ve said, and then engage with them to help figure out recommendations based on your findings.
General steps to analyzing qualitative data:
- Organize the data in a way that makes sense and put it in a format that will allow you to analyze (you may need to transcribe notes, put information in an excel file, etc.)
- Read over the full text. Assign keywords (or codes) to the responses to help you identify the themes that are emerging.
- Identify emerging themes - filter text by identified keywords, and look all the data related to one theme over again to try to identify patterns or connections. Are the views expressed consistent, or are there diverging views? What interesting stories or themes are emerging? Consider if you need to collect more data to address unanswered questions.
- Display the data - this could be visualizing the data by showing the frequency of each theme, or it could be by drawing a map or model of connections or links identified between themes.
- Interpret the data - consider what the key takeaways are. What does it all mean, and if possible, consider involving stakeholders in this exercise. Pay attention to the issues/themes that stand out, but also keep in mind different or opposing views and different experiences.
- Summarize findings and share them. This could include a summary of key themes, ranking the importance of the themes, describe the range of views on issues, and include a few key quotes to illustrate your points and bring the data to life. Quotes can also be used to show the wide range of responses, including opposing views.
Want to learn more? For a brief overview, check out OTF’s Evaluation E-learning course, or for a more in depth guide, check out this Qualitative Data Analysis: A Methods Sourcebook Miles et al. 2013 Qualitative Data Analysis - Chapter 4.pdf (1.4 MB)