| Literature DB >> 29795834 |
Allison J Ames1, Kelli Samonte1.
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.Keywords: Markov chain Monte Carlo; item response theory; software
Year: 2014 PMID: 29795834 PMCID: PMC5965616 DOI: 10.1177/0013164414551411
Source DB: PubMed Journal: Educ Psychol Meas ISSN: 0013-1644 Impact factor: 2.821