Sallie Wormer

Miro is a great tool to identify themes from qualitative responses in surveys and thick data. Here we were examining a sample set of the survey open-ended responses and looking at multiple dimensions identified in the interview portion of this mixed methods study (problem type, journey point, etc.) Ultimately, we uncovered four primary “buckets” that drove need, criticality and behavior when the responses were tagged and quantified.

Another coding and analysis example, using colors to identify interviewees (vs. themes) and affinity mapping. By using Miro this way, the team can quickly find areas of interest to get moving before the final deliverable is ready. In smaller n studies (aka less than the color limit in Miro), color coding participants helps avoid what I call the “loud mouth” bias many UXRs are susceptible to, where one passionate and eloquent participant drives all the quotes and themes. This way, it’s quick and easy to see what emerges across multiple participants vs. a single voice.

Carlow’s IRB was manually processed with various paper forms (!) and multiple approval steps, creating bottlenecks at the secretary’s desk who ‘owned’ it. Often this resulted in duplicate work as various forms and approvals got inevitably lost ‘in the mail’ or on someone’s desk or were sent to the wrong person. Automating this process required working with faculty to codify the user types, the research types, and the conditional approval points which were dependent on the user-type and type of research to be done. The first part of the project required mapping out all conditions, rebuilding the request forms into a shared structured data set, and then coding the process in SharePoint via InfoPath. The documentation (shown here) was as much sanity checking for myself during building and QA as it was for future admins who might need to adjust anything, should the IRB alter its process.