A GENERAL BAYESIAN MODEL FOR TESTLETS: THEORY AND APPLICATIONS

Abstract

ABSTRACTThis paper extends earlier work (Bradlow, Wainer, & Wang, 1999; Wainer, Bradlow, & Du, 2000) on the modeling of testlet‐based response data to include the situation in which a test is composed, partially or completely, of polytomously scored items and/or testlets. A modified version of commonly employed item response models, embedded within a fully Bayesian framework, is proposed, and inferences under the model are obtained using Markov chain Monte Carlo (MCMC) techniques. Its use is demonstrated within a designed series of simulations and by analyzing operational data from the North Carolina Test of Computer Skills and Educational Testing Service's Test of Spoken English (TSE). Our empirical findings suggest that the North Carolina Test of Computer Skills exhibits significant testlet effects, indicating significant dependence of item scores obtained from common stimuli, whereas the TSE exam does not.