Using SAS PROC MCMC for Item Response Theory Models

Interest in using Bayesian methods for estimating item response theory models has grown at a remarkable rate in recent years. This attentiveness to Bayesian estimation has also inspired a growth in available software such as WinBUGS, R packages, BMIRT, MPLUS, and SAS PROC MCMC. This article intends...

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Bibliographic Details
Published inEducational and psychological measurement Vol. 75; no. 4; pp. 585 - 609
Main Authors Ames, Allison J., Samonte, Kelli
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.08.2015
SAGE PUBLICATIONS, INC
Subjects
Bayesian analysis
Bayesian Statistics
Computation
Computer Software
Estimating techniques
Item Response Theory
Monte Carlo Methods
Quantitative psychology
Syntax
software
Markov chain Monte Carlo
item response theory
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Summary:Interest in using Bayesian methods for estimating item response theory models has grown at a remarkable rate in recent years. This attentiveness to Bayesian estimation has also inspired a growth in available software such as WinBUGS, R packages, BMIRT, MPLUS, and SAS PROC MCMC. This article intends to provide an accessible overview of Bayesian methods in the context of item response theory to serve as a useful guide for practitioners in estimating and interpreting item response theory (IRT) models. Included is a description of the estimation procedure used by SAS PROC MCMC. Syntax is provided for estimation of both dichotomous and polytomous IRT models, as well as a discussion on how to extend the syntax to accommodate more complex IRT models.
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ISSN:0013-1644
1552-3888
DOI:10.1177/0013164414551411