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Reporting Qualitative Data

Gaining Control of the Fuzzy Non-Numeric

3 minute read

When dealing with moderate to large sets of qualitative data, affinity analysis can help identify and prioritize common issues that users experience.

We’ve spent a lot of time discussing quantitative data this week: Collecting, measuring, and analyzing it. This got me thinking about its more nebulous twin — qualitative data — and how it’s a whole different beast.

Quantitative data is great. It’s mathy. It gives you definitive answers and can be conveyed in colorful charts and tables. Quantitative data is the bookish nerd of the UX analysis world.

Qualitative data, on the other hand, is the brash wannabe rock star. It defies definition. It’s nebulous, artsy… fuzzy.

So how do you go about conveying qualitative results to stakeholders, developers and designers during a project? Whereas quantitative reports carry a degree of certainty that lend themselves to decision-making, qualitative analysis is more subtle.

Affinity Analysis

One tool you can use to wrangle a nebulous qualitative data study is affinity analysis (aka: affinity diagramming). You’re basically taking qualitative observations and grouping them to find patterns. This is where sticky notes come in handy (and why UXers love them so much).

Get into a room with the team. Divide the studies up amongst the group. Identify all of the issues (or observations, comments, expressed desires, etc.) and write each on a sticky note. When all is said and done, have the team put the stickies on a wall or table. Start sorting them into groups of similar stickies.

Before too long, you’ll notice clusters forming among the multi-colored paper forest. Look at each cluster and summarize it in a phrase or sentence. These clusters are going to be your “issues” or “observations” (depending upon the study).

Now you’ve got something that’s starting to look a little more quantitative. Something you can even put into a chart or table. It’s like putting those nerdy hipster glasses on John Mayer. He’s still the rebel artsy dude, but he’s getting his math on.

An Example

As an example, I’ve taken some qualitative data from a study we did a few months ago, interviewing participants about their experience searching for an engineering job online. We interviewed seven participants (labeled P1-P7) and recorded the process. We then went back and did an affinity analysis to find common observations among all of the participants, coming up with six distinct clusters of stickies in the process.

By counting the number of participants who shared similar observations, we were able to calculate an “affinity score” for each — basically a percentage:

Affinity Score = Candidates Reporting ÷ Total Number of Candidates

And, boom: You’ve got data you can put in a table or chart to clearly identify the most relevant issues or observations for your team and stakeholders.

Key Interview Observations

Simple, But Effective

It’s not rocket-surgery, but this method of identifying commonalities in qualitative analysis is extremely simple and effective. I encourage you to give it a shot next time you’re faced with a bunch of fuzzy data that needs to be tamed.