ABSTRACT This 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.
A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor mult...
A lognormal model for the response times of a person on a set of test items is investigated. The model has a parameter structure analogous to the two-parameter logistic response...
Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because pr...
Summary. In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the i...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in clo...
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