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Parallel Markov chain Monte Carlo for Bayesian dynamic item response models in educational testing
Author(s) -
Wei Zheng,
Wang Xiaojing,
Conlon Erin Marie
Publication year - 2017
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.164
Subject(s) - markov chain monte carlo , computer science , variable order bayesian network , bayesian probability , computation , monte carlo method , item response theory , bayesian statistics , approximate bayesian computation , bayesian inference , algorithm , artificial intelligence , mathematics , statistics , psychometrics , inference
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.