I know, I know, it’s been ages and I promised to write up my experiment. Since I miss the eclipse here in Britain (and I missed it back when we got a total eclipse too, because of cloud cover!), I’m going to finally keep up that promise. You can find part one of the experiment here. The hypothesis: that my mood improves when I read more!
|Day||Number of pages read||Mood (%)|
Which gives us a chart that looks something like this — the top line shows you the number of pages I read on a given day, while the lower chart shows you my mood on the corresponding day.
Eyeballing that, you can see a pretty obvious correlation between the two lines, though there are clearly other factors. But we can be more precise.
I still think a Spearman’s rank correlation is the best way to analyse these data. In a Spearman’s rank correlation test, you rank each column in order of the size of the number — so for example, day one’s page number ranking is 25, while the mood ranking on day one is 10. You then calculate the difference between them — 15 — and then square it — 225. You do that for all the ranks, and then you put the numbers in the following equation…
If that looks like Greek to you, don’t worry. rs is what we’re trying to calculate. In English, the top bit means six times the total of the difference squared, so for our purposes that’s 10719. That’s then divided by n, the number of variables, multiplied by the number of variables squared minus one — which is 26970. 10719/26970 = 0.39744160178. That needs to be subtracted from one to give rs. Our answer is 0.60255839822 — let’s call it 0.6, since we know from the way we’ve set up this experiment that it’s not very precise. That means that there’s a fairly strong positive correlation between the numbers: 0 would be no correlation, and a negative number would mean a negative correlation.
Nobody’s surprised: there is a correlation between the days I read a lot and the days I’m in a good mood. Whew. Glad we got that out of the way!
My next post will be all about discussing these results — what they really mean, and what I might do in the future. The discussion part of an experiment is important, even if the results look pretty self-evident!