I’m taking some time off work this week to be with my two girls in their final week of summer. But in that time I started playing around with colors in R a bit and wanted to share some of what I’ve learned, specifically in relation to bivariate color palettes. There is an existing R package, {biscale}, which is probably the simplest way to approach this, but I wanted to dig in a bit more and explore on my own.

This is, basically, the third post in a series of posts about estimating and, now, exploring achievement gap variation between schools. The first post described the method, while the second applied the method to estimate achievement gaps for all schools reporting data on students coded as Hispanic and White in California. That post included some preliminary explorations of the data, but this post will take that further by looking at the variance geographically.

In the last post I talked about one method to estimate distributional differences from ordinal data, such as those reported by statewide accountability systems. In this post, we’ll put this method to work for the state of California. I’ll show how we can estimate school-level Hispanic/White achievement gaps for every school in the state that reports data on both groups. In California, this means the school must have at least 30 students in each group, for the corresponding grade.