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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| Published in | Educational and psychological measurement Vol. 75; no. 4; pp. 585 - 609 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Los Angeles, CA
SAGE Publications
01.08.2015
SAGE PUBLICATIONS, INC |
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| Abstract | 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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| AbstractList | 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. |
| Author | Samonte, Kelli Ames, Allison J. |
| AuthorAffiliation | 1 University of North Carolina at Greensboro, Greensboro, NC, USA |
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| Author_xml | – sequence: 1 givenname: Allison J. surname: Ames fullname: Ames, Allison J. email: ajames@uncg.edu – sequence: 2 givenname: Kelli surname: Samonte fullname: Samonte, Kelli |
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| Snippet | Interest in using Bayesian methods for estimating item response theory models has grown at a remarkable rate in recent years. This attentiveness to Bayesian... |
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| SubjectTerms | Bayesian analysis Bayesian Statistics Computation Computer Software Estimating techniques Item Response Theory Monte Carlo Methods Quantitative psychology Syntax |
| Title | Using SAS PROC MCMC for Item Response Theory Models |
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