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 S

SUBMITTER: Lockwood JR 

PROVIDER: S-EPMC5965647 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature