Mapping covariates, Part III

In my spare time (ha-ha) I refined the software I blogged about in the last two posts in order to automate almost everything about sorting out what's connected to what in a data set.  Now I can create a folder with a data file, an index of variables, and an options list, drop that folder on a script on the desktop, and a few seconds later have a graph like the one below. This makes it easy to tweak parameters to find a nice picture. One that tells $2^{10}$ words, give or take.

For the graph below, I dug out the results of a semester of course evaluation using the new form I got implemented a year ago. I wrote previously about the odd fact that the summative evaluation of the course in Q12 and Q13 didn't seem to relate much to the learning outcomes. The closest other item in this topology is how enjoyable the students reported the course being.

This graph shows covariances instead of correlations. The latter have the nice property of being normalized to [-1,1], but suffer from the fact that nearly constant items all correlate highly with each other. The program can run either way. The graph below shows the top 30 correlates. The means are shown too.