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Using SAS PROC MCMC for Item Response Theory Models.

Allison J Ames¹, Kelli Samonte¹

¹University of North Carolina at Greensboro, Greensboro, NC, USA.

Educational and Psychological Measurement

|May 26, 2018

Summary

This summary is machine-generated.

Bayesian methods offer a powerful approach for item response theory (IRT) models. This guide details Bayesian estimation using SAS PROC MCMC for various IRT models, aiding practitioners.

Keywords:

Markov chain Monte Carlo item response theory software

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Area of Science:

Psychometrics
Statistical Modeling

Background:

Growing interest in Bayesian methods for item response theory (IRT) models.
Increased availability of software for Bayesian estimation (e.g., WinBUGS, R, BMIRT, MPLUS, SAS PROC MCMC).

Purpose of the Study:

Provide an accessible overview of Bayesian methods for IRT model estimation.
Serve as a practical guide for researchers and practitioners.

Main Methods:

Description of the Bayesian estimation procedure within SAS PROC MCMC.
Syntax examples for dichotomous and polytomous IRT models.

Main Results:

Demonstration of SAS PROC MCMC for estimating IRT models.
Guidance on extending syntax for more complex IRT models.

Conclusions:

Bayesian estimation offers a flexible framework for IRT models.
SAS PROC MCMC provides a viable tool for implementing these methods.