MCMC estimation and some fit analysis of multidimensional IRT models

Anton Beguin; Cornelis A.W. Glas

doi:10.1007/BF02296195

MCMC estimation and some fit analysis of multidimensional IRT models

Anton Beguin, Cornelis A.W. Glas

Faculty of Behavioural, Management and Social Sciences

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252 Citations (Scopus)

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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 language	English
Pages (from-to)	471-488
Number of pages	17
Journal	Psychometrika
Volume	66
Issue number	4
DOIs	https://doi.org/10.1007/BF02296195
Publication status	Published - 2001

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

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10.1007/BF02296195

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title = "MCMC estimation and some fit analysis of multidimensional IRT models",

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.",

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AU - Glas, Cornelis A.W.

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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

SN - 0033-3123

VL - 66

SP - 471

EP - 488

JO - Psychometrika

JF - Psychometrika

IS - 4

ER -

MCMC estimation and some fit analysis of multidimensional IRT models

Abstract

Keywords

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