Analyzing qualitative data is hard. Even harder is presenting the data to a team of stakeholders in a VERY tech company. As I was guiding a team of researchers through a qualitative study, I put together my thoughts on what I actually do when reporting out qual data analysis.
Below are the 4 steps that I follow:
First thing to do is to spend some time making a profile of each participant from the study. For the presentation or report, you can include a summary page and highlight the important things of the population (gender, age, …) as well as de key differences amongst them. The reason for doing this, is that since the number of participants is low and the research is a qualitative in-depth one, the answers are not meant to be generalized. You want to create context and allow your audience to understand who your participants were and sharing as much of them as you can will make this connection stronger (pictures, quotes, etc. are highly encouraged). Also here you want to summarize the study (set up, method, timeline) and explain the pro’s and limitations. Including pictures of the set up is always a plus. Give your audience a glimpse of what you did, put them in the place, after all, this is how all good stories start.
The second thing you want to do is highlight the key learnings. The Executive Summary: what are the top 2-3 things from each area of your research that you think are the most important for them to know. Similar to “if you only get 1 thing from this presentation, let it be this”. Build on each of these learnings with direct quotes, pictures or videos to make them more powerful.
After that, you want to have a summary of all the responses per area as well as overall. The summary can be done in the style of a UX dashboard or score card. If you divided the questions per topic, you can aggregate similar responses and take out the outliers. And then make sure to explain the outliers separately. So for example, if in one section most people felt similarly (and it is a positive response), you can label that “green” but if one person had a radically different answer you can list that separately stating that under this circumstance this response was different and explain why do you think that is (the participant is a hard core programmer, for example). Remember to label the colors to make sure the audience quickly gets the results. Nothing is worst that making them guess at what you are trying to show them. If you have many data points, highlight the most important ones that add to your story (see point below) and leave the rest for them to explore on their own by providing a copy of this dashboard. You don’t want to bore people with details and have them remember that, instead of the point above! Note that sometimes it happens that there is no consensus at all in an answer. This is quite typical of in depth interviews. Explaining why that is is equally as important, so don’t ignore those answers.
Finally, the most important one is to make the data actionable for the team. What are your recommendations for them to proceed with this data? Are there fundamental things to change? Do they need to change course? Do they need to re-think certain things or weigh things differently? And, as importantly, what would be the next step of research? What were things not included in this research that you think are important to cover in the next phase? Any suggestions on how to go about doing that? This could be your chance to secure a second study, and also to put the limitations of your study in the right light. Be prepared to highlight the things you weren’t able to do but the team needs, doing so preemptively will ease the Q&A session at the end.
A “nice to have” is what went right/wrong with the study and what you would change if you had more time/money/resources. Sometimes this might not be as interesting for the team you are presenting, but I highly encourage to document this, either for yourself or for others that might benefit from not going down that path.
This summarizes how I go about presenting the results of qualitative experience research. Looking forward to hearing from others practitioners in the field!