This week, we are sharing the writing of participants in the 2023 Community Engagement Programme! In this blogpost, you can hear more about Nikola’s experience, specifically on encountering challenges with data collection. 

We attend reputable schools like LSE to research or study in one capacity or another. It is the amalgamation of resources, people, and experiences which make the university what it is. However, what happens when you struggle to find enough of what you are looking for? My Community Engagement Programme team and I fell right into this puzzle. Assigned to work with an NGO over its retention strategies, we needed data on precisely the people who were both choosing to leave or committing to stay. We compiled a killer survey, walked through some hypotheses on the results, prepared for some 1:1 interview, and then passed the Qualtrics Survey on to our program supervisor to send out to participants via email. Three weeks passed, and we got less than 20 responses. BOOM? More like a thunk. Now what?

Our project had a fast turnaround, and along with limits as to the base with which we could reach out to, our numbers were not only low, but were likely to remain at that rate. When you are looking for answers to big problems, you kind of need big, or at least, mildly equitable, numbers to make your research and subsequent conclusions formidable. If you don’t have that, I thought, it might be better to throw in the towel.

Rather fortunately, the rest of my group was much less defeatist. These Saints (who, it must be noted, worked so hard) extrapolated every last ounce of information that we could from the data we did get. Not only that, but we doubled down on our case-study observations, making sure to squeeze every bit of personalized information we could from our incredibly generous interviewees. Pull all of those bits together and you end up with result that turned out to be equally formidable.

Squelching every last drop of evidence gave me a new respect for smaller-scale studies, especially when data is sparse, absent, or just generally limited due to the subject. Instead of relying on the ease of horizontal data weaving for quantitative means, you must lean into your qualitative side to discover vertical evidence seemingly below the surface. Each case study is a wealth of knowledge; all that is needed is to tap into it, correspond it to your other results and qualify it within the larger context. Not all of our interviewees share the same points, but they gave valid perspectives that provided a degree of takeaway and nuance just as fulfilling as any advanced survey metric. Sure, you could argue the qualitative data is itself more questionable and deserving of skepticism. But this writer is American, coming from a place where all manners of quantitative research are already under an era of skepticism. At the very least, it proves vertical research-style analysis in high demand.

So we learned something after all – about ourselves and extrapolating deeper. And even better, (we believe) our client learned more about their retention strategies because of it. It wasn’t a perfect piece but not only did we make do with what we had, but we found a way to excel because of it. It’s not Shakespearean but it will do.

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