I used to conduct qualitative-heavy research projects pretty much all day every day. Key informant interviews, bellwether interviews, document review, focus groups, you name it…

…so I know from personal experience that 99.9% of qualitative reports look like this:

qualitative_before

Which is fine, unless you want someone to, ya know, actually read your report.

Here are 6 ideas for presenting qualitative data in reports, slidedocs, presentations, handouts and more.

 

Word Clouds

Dataviz novices love to love word clouds while dataviz experts love to hate word clouds. I’m not talking about the we have no idea how to analyze text so let’s throw it in Wordle word clouds.

Word Clouds for One-Word Descriptions

In a lot of interview projects, colleagues and I have purposefully included a question in the interview protocol like, “What one word you use to describe….”

Examples: “Hey Mr. Policymaker, what one word would you use to describe public opinion towards poverty in the United States?” or “Hey Mrs. Principal, what one word would you use to describe the grant’s implementation during Year 1 in your charter school?”

The rest of the interview is obviously focused on loooooooong responses, and fuller descriptions, and plenty of examples. But for one or two moments during the conversation, it’s nice to pause and throw in a semi-qualitative short question.

Here’s an example from Students First:

 

Word Clouds for Before/After Comparisons

Word clouds are also great for before/after comparisons, like these tweets describing breakups.   Does your study involve pre/post tests with a few open-ended questions? Do you interview participants at multiple intervals during the study? You could adapt this technique for nearly any time series design.

 

Showcasing Open-Ended Survey Data Beside Closed-Ended Data

What’s better than quantitative data? Or better than qualitative data? Quantitative and qualitative data combined.

Use this technique when your survey has both closed-ended and open-ended questions. Tie the responses together in one chart to add much-needed context. In this example, a survey asked nonprofits to describe what it was like to work with an evaluator. Then, the survey asked them why their experiences were good or bad. Who cares if 33% of nonprofits had excellent experiences but we don’t have examples that describe why? Rather than simply listing out the open-ended responses in your appendix, showcase them beside your stacked bar chart.

qualitative_quotes

Read more about this technique in this post written by Johanna Morariu and I last year.

 

Photos Beside Participants’ Responses

For your non-anonymous reporting, how about inserting photos of the interviewees next to their ideas?

 

Icons Beside Descriptions and Responses

Icons are so easy to use that there’s really no excuse for not using them to break up long sections of text.

Another great use of icons comes from the Washington Post’s story about how animals are faring after the BP oil spill. Isn’t it remarkable how icons and bold subtitles help to break up a chunk of text?

qual_icons

 

Diagrams to Explain Concepts and Processes

The evaluator’s go-to diagram is the logic model. But you can create diagrams for other aspects of your research project, too, like this diagram explaining what type of protective gear is keeping doctors safe from Ebola:

 

Graphic Timelines

Regular text-based timelines + diagrams, photos, and other images = graphic timelines. Timelining is especially valuable in developmental evaluation where you’re tracking a program, initiative, or campaign as it unfolds.
Interactive Timelines
This timeline from the New York Times includes an assortment of qualitative and quantitative information: dates, milestones, # of rockets, and # of casualties. My screenshot doesn’t do it justice; head over to the New York Times website to explore the interactive version.

qual_timeline

Static Timelines
Maybe you’re not the New York Times, I get it. Anyone can design a static timeline from the comfort of their PowerPoint slide.

How are you visualizing data from text, interviews, and focus groups? Please link to your favorite resources below.