A single country distorted my European mood trend, so I tried reweighting the data
I’m running a small side project where people anonymously report their current mood from 1–10.
I was looking at Europe using a rolling average of 100 responses, and noticed a very sharp jump at the end of April.
At first, it looked like European mood had suddenly increased by about 1 point.
But when I looked at the composition of those rolling windows, I found the problem: at one point, Spain accounted for up to 83% of the 100 European responses.
So what looked like a European trend was largely a change in the geographic composition of my sample.
Before correction:
The late-April rolling mean jumps by about +1.09 points.
I then tried a simple reweighting approach: Spain is still included, but it can contribute at most 20% of the weight in each rolling window of 100 European responses.
After correction:
The jump is reduced to about +0.89 points.
So Spain explains part of the apparent jump - but interestingly, not all of it.
This made me realize that fixing the rolling sample size doesn't necessarily fix the sample composition.
The data are anonymous and self-selected, so I'm not treating this as a representative estimate of European mood. I'm mostly interested in how best to visualize and analyze this kind of uneven crowdsourced dataset.
How would you handle this?
Cap each country's contribution? Weight countries equally? Use population weights? Or avoid producing a single “Europe” curve altogether?