Ames Allison J, Samonte Kelli
University of North Carolina at Greensboro, Greensboro, NC, USA.
Educ Psychol Meas. 2015 Aug;75(4):585-609. doi: 10.1177/0013164414551411. Epub 2014 Sep 25.
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.
近年来，使用贝叶斯方法估计项目反应理论模型的兴趣以惊人的速度增长。对贝叶斯估计的这种关注也激发了可用软件的增长，如WinBUGS、R包、BMIRT、MPLUS和SAS PROC MCMC。本文旨在在项目反应理论的背景下提供贝叶斯方法的易懂概述，为从业者估计和解释项目反应理论（IRT）模型提供有用的指导。其中包括对SAS PROC MCMC使用的估计程序的描述。提供了用于估计二分和多分IRT模型的语法，以及关于如何扩展语法以适应更复杂IRT模型的讨论。
