Literature DB >> 29795834

Using SAS PROC MCMC for Item Response Theory Models.

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


  14 in total

1.  A model for psychiatric questionnaires with embarrassing items.

Authors:  Mariana Cúri; Julio M Singer; Dalton F Andrade
Journal:  Stat Methods Med Res       Date:  2010-06-04       Impact factor: 3.021

2.  Formulation and Application of the Hierarchical Generalized Random-Situation Random-Weight MIRID.

Authors:  Lai-Fa Hung
Journal:  Multivariate Behav Res       Date:  2011-07-29       Impact factor: 5.923

3.  The Multigroup Multilevel Categorical Latent Growth Curve Models.

Authors:  Lai-Fa Hung
Journal:  Multivariate Behav Res       Date:  2010-03-31       Impact factor: 5.923

4.  Hip psychometrics.

Authors:  Peter Baldwin; Joseph Bernstein; Howard Wainer
Journal:  Stat Med       Date:  2009-07-30       Impact factor: 2.373

5.  The Monte Carlo method.

Authors:  N METROPOLIS; S ULAM
Journal:  J Am Stat Assoc       Date:  1949-09       Impact factor: 5.033

6.  Using R and WinBUGS to fit a generalized partial credit model for developing and evaluating patient-reported outcomes assessments.

Authors:  Yuelin Li; Ray Baser
Journal:  Stat Med       Date:  2012-02-23       Impact factor: 2.373

7.  Improving psychometric assessment of the Beck Depression Inventory using multidimensional item response theory.

Authors:  Tiago M Fragoso; Mariana Cúri
Journal:  Biom J       Date:  2013-03-22       Impact factor: 2.207

8.  Some exact tests for manifest properties of latent trait models.

Authors:  Jan G De Gooijer; Ao Yuan
Journal:  Comput Stat Data Anal       Date:  2011-01-01       Impact factor: 1.681

9.  Using a Multivariate Multilevel Polytomous Item Response Theory Model to Study Parallel Processes of Change: The Dynamic Association Between Adolescents' Social Isolation and Engagement With Delinquent Peers in the National Youth Survey.

Authors:  Chueh-An Hsieh; Alexander A von Eye; Kimberly S Maier
Journal:  Multivariate Behav Res       Date:  2010-05-28       Impact factor: 5.923

10.  Estimating the prevalence of sensitive behaviour and cheating with a dual design for direct questioning and randomized response.

Authors:  Ardo van den Hout; Ulf Böckenholt; Peter G M van der Heijden
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2010-08       Impact factor: 1.864

View more
  2 in total

1.  Fitting Residual Error Structures for Growth Models in SAS PROC MCMC.

Authors:  Daniel McNeish
Journal:  Educ Psychol Meas       Date:  2016-06-01       Impact factor: 2.821

2.  Using the Stan Program for Bayesian Item Response Theory.

Authors:  Yong Luo; Hong Jiao
Journal:  Educ Psychol Meas       Date:  2017-02-01       Impact factor: 2.821

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.