ERIC Number: EJ779663
Record Type: Journal
Publication Date: 2007
Reference Count: 41
An NCME Instructional Module on Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods
Kim, Jee-Seon; Bolt, Daniel M.
Educational Measurement: Issues and Practice, v26 n4 p38-51 Win 2007
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.
Descriptors: Placement, Monte Carlo Methods, Markov Processes, Measurement, Sampling, Item Response Theory, Computation, Inferences, Mathematics, Mathematics Tests, College Mathematics, Computer Software, Evaluation Methods
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Authoring Institution: N/A