Bayesian Estimation in the Rasch Model

Abstract

Bayesian estimation procedures based on a hierarchical model for estimating parameters in the Rasch model are described. Through simulation studies it is shown that the Bayesian procedure is superior to the maximum likelihood procedure in that the estimates are (a) more accurate, at least in small samples; and (b) meaningful in that parameters corresponding to perfect item and ability responses can be estimated.