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Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods
Author(s) -
Kim JeeSeon,
Bolt Daniel M.
Publication year - 2007
Publication title -
educational measurement: issues and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.158
H-Index - 52
eISSN - 1745-3992
pISSN - 0731-1745
DOI - 10.1111/j.1745-3992.2007.00107.x
Subject(s) - markov chain monte carlo , computer science , inference , gibbs sampling , bayesian probability , bayesian inference , context (archaeology) , monte carlo method , markov chain , machine learning , data mining , artificial intelligence , statistics , mathematics , paleontology , biology
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain convergence. Model comparison and fit issues in the context of MCMC are also considered. Finally, an illustration is provided in which a two‐parameter logistic (2PL) model is fit to item response data from a university mathematics placement test through MCMC using the WINBUGS 1.4 software. While MCMC procedures are often complex and can be easily misused, it is suggested that they offer an attractive methodology for experimentation with new and potentially complex IRT models, as are frequently needed in real‐world applications in educational measurement.