Value-Added and Student Growth Percentile Models: What Drives Differences in Estimated Classroom Effects?

Abstract

ABSTRACT This study shows value-added models (VAM) and student growth percentile (SGP) models fundamentally disagree regarding estimated teacher effectiveness when the classroom distribution of test scores conditional on prior achievement is skewed (i.e., when a teacher serves a disproportionate number of high- or low-growth students). While conceptually similar, the two models differ in estimation method which can lead to sizable differences in estimated teacher effects. Moreover, the magnitude of conditional skewness needed to drive VAM and SGP models apart often by three and up to 6 deciles is within the ranges observed in actual data. The same teacher may appear weak using one model and strong with the other. Using a simulation, I evaluate the relationship under controllable conditions. I then verify that the results persist in observed student–teacher data from North Carolina.