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

被引：228

作者：

Béguin, AA ^{[1
]}

Glas, CAW ^{[1
]}

机构：

[1] Univ Twente, Enschede, Netherlands

来源：

PSYCHOMETRIKA | 2001年 / 66卷 / 04期

关键词：

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

D O I：

10.1007/BF02296195

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页码：541 / 561

页数：21

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