Parallel Markov chain Monte Carlo for Bayesian dynamic item response models in educational testing

Abstract

Bayesian dynamic item response models have been successfully used for educational testing data; these models are especially useful for individually varying and irregularly spaced longitudinal testing data. However, because of the complexity of the models and the large size of the data sets, computation time is excessive for carrying out full data analyses in practice. Here, we introduce a parallel Markov chain Monte Carlo method to speed the implementation of these Bayesian models. Using both simulation data and real educational testing data for reading ability, we demonstrate that computation time is greatly reduced for our parallel computing method versus full data analyses. The estimated error of our method is shown to be small, using common distance metrics. Our parallel computing approach can be used for other models in the Educational and Psychometric fields, including Bayesian item response theory models. Copyright © 2017 John Wiley & Sons, Ltd.