MCMC estimation and some fit analysis of multidimensional IRT models

Anton Beguin, Cornelis A.W. Glas

Research output: Contribution to journalArticleAcademic

175 Citations (Scopus)

Abstract

A Bayesian procedure to estimate the three-parameter normal ogive model and a generalization of the procedure to a model with multidimensional ability parameters are presented. The procedure is a generalization of a procedure by Albert (1992) for estimating the two-parameter normal ogive model. The procedure supports analyzing data from multiple populations and incomplete designs. It is shown that restrictions can be imposed on the factor matrix for testing specific hypotheses about the ability structure. The technique is illustrated using simulated and real data.
Original languageEnglish
Pages (from-to)471-488
Number of pages17
JournalPsychometrika
Volume66
Issue number4
DOIs
Publication statusPublished - 2001

Fingerprint

Multidimensional Model
Markov Chain Monte Carlo
Testing
Two Parameters
Model
Restriction
Estimate
Population

Keywords

  • IR-60246
  • Bayes estimates - full-information factor analysis - Gibbs sampler - item response theory - Markov chain Monte Carlo - multidimensional item response theory - normal ogive model
  • METIS-203946

Cite this

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MCMC estimation and some fit analysis of multidimensional IRT models. / Beguin, Anton; Glas, Cornelis A.W.

In: Psychometrika, Vol. 66, No. 4, 2001, p. 471-488.

Research output: Contribution to journalArticleAcademic

TY - JOUR

T1 - MCMC estimation and some fit analysis of multidimensional IRT models

AU - Beguin, Anton

AU - Glas, Cornelis A.W.

PY - 2001

Y1 - 2001

N2 - A Bayesian procedure to estimate the three-parameter normal ogive model and a generalization of the procedure to a model with multidimensional ability parameters are presented. The procedure is a generalization of a procedure by Albert (1992) for estimating the two-parameter normal ogive model. The procedure supports analyzing data from multiple populations and incomplete designs. It is shown that restrictions can be imposed on the factor matrix for testing specific hypotheses about the ability structure. The technique is illustrated using simulated and real data.

AB - A Bayesian procedure to estimate the three-parameter normal ogive model and a generalization of the procedure to a model with multidimensional ability parameters are presented. The procedure is a generalization of a procedure by Albert (1992) for estimating the two-parameter normal ogive model. The procedure supports analyzing data from multiple populations and incomplete designs. It is shown that restrictions can be imposed on the factor matrix for testing specific hypotheses about the ability structure. The technique is illustrated using simulated and real data.

KW - IR-60246

KW - Bayes estimates - full-information factor analysis - Gibbs sampler - item response theory - Markov chain Monte Carlo - multidimensional item response theory - normal ogive model

KW - METIS-203946

U2 - 10.1007/BF02296195

DO - 10.1007/BF02296195

M3 - Article

VL - 66

SP - 471

EP - 488

JO - Psychometrika

JF - Psychometrika

SN - 0033-3123

IS - 4

ER -