STOCHASTIC APPROXIMATION METHODS FOR LATENT REGRESSION ITEM RESPONSE MODELS

Abstract

This paper presents an application of a stochastic approximation EM-algorithm using a Metropolis-Hastings sampler to estimate the parameters of an item response latent regression model. Latent regression models are extensions of item response theory (IRT) to a 2-level latent variable model in which covariates serve as predictors of the conditional distribution of ability. Applications for estimating latent regression models for data from the 2000 National Assessment of Educational Progress (NAEP) grade 4 math assessment and the 2002 grade 8 reading assessment are presented and results of the proposed method are compared to results obtained using current operational procedures.