Introduction to Bayesian item response modelling
by Jim Albert
International Journal of Quantitative Research in Education (IJQRE), Vol. 2, No. 3/4, 2015

Abstract: This article provides a brief survey of developments in item response modelling from a Bayesian perspective. There is a description of the influential literature in the application of Markov chain Monte Carlo algorithms to fit IRT models. To give insight into current Bayesian work, we give overviews of recent papers on Bayesian multilevel modelling, IRT modelling using flexible asymmetric link functions, and detection of multidimensional structure using posterior predictive model checking. To show the recent advances in Bayesian software, we illustrate the use of an R package to fit a two-parameter IRT model by MCMC methods.

Online publication date: Wed, 16-Sep-2015

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