A General Bayesian Model for Testlets: Theory and Applications

Abstract

The need for more realistic and richer forms of assessment in educational tests has led to the inclusion (in many tests) of polytomously scored items, multiple items based on a single stimulus (a “testlet”), and the increased use of a generalized mixture of binary and polytomous item formats. In this paper, the authors extend earlier work 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. The model they propose, a modified version of commonly employed item response models, is embedded within a fully Bayesian framework, and inferences under the model are obtained using Markov chain Monte Carlo techniques. The authors demonstrate its use within a designed series of simulations and by analyzing operational data from the North Carolina Test of Computer Skills and the Educational Testing Service’s Test of Spoken English. Their 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 Test of Spoken English does not.