Louisiana’s “School Performance Score” Doesn’t Measure School Performance
Louisiana’s “School Performance Score” (SPS) is the state’s primary accountability measure, and it determines whether schools are subject to high-stakes decisions, most notably state takeover. For elementary and middle schools, 90 percent of the SPS is based on testing outcomes. For secondary schools, it is 70 percent (and 30 percent graduation rates).*
The SPS is largely calculated using absolute performance measures – specifically, the proportion of students falling into the state’s cutpoint-based categories (e.g., advanced, mastery, basic, etc.). This means that it is mostly measuring student performance, rather than school performance. That is, insofar as the SPS only tells you how high students score on the test, rather than how much they have improved, schools serving more advantaged populations will tend to do better (since their students tend to perform well when they entered the school) while those in impoverished neighborhoods will tend to do worse (even those whose students have made the largest testing gains).
One rough way to assess this bias is to check the association between SPS and student characteristics, such as poverty. So let’s take a quick look.
The scatterplot below presents the relationship between schools’ SPS in 2011 and the percent of their students receiving subsidized lunch, an imperfect but for our purposes adequate proxy for student poverty.
There is a moderate-to-strong relationship between poverty and SPS (the correlation coefficient is 0.64). Virtually
The SPS is largely calculated using absolute performance measures – specifically, the proportion of students falling into the state’s cutpoint-based categories (e.g., advanced, mastery, basic, etc.). This means that it is mostly measuring student performance, rather than school performance. That is, insofar as the SPS only tells you how high students score on the test, rather than how much they have improved, schools serving more advantaged populations will tend to do better (since their students tend to perform well when they entered the school) while those in impoverished neighborhoods will tend to do worse (even those whose students have made the largest testing gains).
One rough way to assess this bias is to check the association between SPS and student characteristics, such as poverty. So let’s take a quick look.
The scatterplot below presents the relationship between schools’ SPS in 2011 and the percent of their students receiving subsidized lunch, an imperfect but for our purposes adequate proxy for student poverty.
