Bias and the effect of priors in Bayesian estimation of parameters of item response models

Abstract

The effectiveness of a Bayesian approach to the estimation problem in item response models has been sufficiently documented in recent years. Although research has indicated that Bayesian estimates, in general, are more accurate than joint maximum likelihood (JML) estimates, the effect of choice of priors on the Bayesian estimates is not well known. Moreover, the extent to which the Bayesian estimates are biased in comparison with JML estimates is not known. The effect of priors and the amount of bias in Bayesian estimates is examined in this paper through simulation studies. It is shown that different specifications of prior information have relatively modest effects on the Bayesian estimates. For small samples, it is shown that the Bayesian estimates are less biased than their JML counterparts. Index terms: accuracy, Bayesian estimates, bias, item response models, joint maximum likelihood estimates, priors.