Estimating True Student Growth Percentile Distributions Using Latent Regression Multidimensional IRT Models

Abstract

Student Growth Percentiles (SGPs) increasingly are being used in the United States for inferences about student achievement growth and educator effectiveness. Emerging research has indicated that SGPs estimated from observed test scores have large measurement errors. As such, little is known about “true” SGPs, which are defined in terms of nonlinear functions of latent achievement attributes for individual students and their distributions across students. We develop a novel framework using latent regression multidimensional item response theory models to study distributional properties of true SGPs. We apply these methods to several cohorts of longitudinal item response data from more than 330,000 students in a large urban metropolitan area to provide new empirical information about true SGPs. We find that true SGPs are correlated 0.3 to 0.5 across mathematics and English language arts, and that they have nontrivial relationships with individual student characteristics, particularly student race/ethnicity and absenteeism. We evaluate the potential of using these relationships to improve the accuracy of SGPs estimated from observed test scores, finding that accuracy gains even under optimal circumstances are modest. We also consider the properties of SGPs averaged to the teacher level, widely used for teacher evaluations. We find that average true SGPs for individual teachers vary substantially as a function of the characteristics of the students they teach. We discuss implications of our findings for the estimation and interpretation of SGPs at both the individual and aggregate levels.