Articlegreen2001

MCMC Estimation and Some Model-Fit Analysis of Multidimensional IRT Models

349 citations0/0 files storedPsychometrika

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Psychometric Methodologies and Testing Statistical Distribution Estimation and Applications Statistical Methods and Bayesian Inference

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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References

1969Statistical Theories of Mental Test Scores 2007Multiple Factor Analysis 1981Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm 1997Handbook of Modern Item Response Theory 1948Multiple factor analysis.1984Effects of Local Item Dependence on the Fit and Equating Performance of the Three-Parameter Logistic Model 1973A Goodness of Fit Test for the Rasch Model 1990BILOG 3 : item analysis and test scoring with binary logistic models

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2009High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm 2010Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis 2006Posterior Predictive Assessment of Item Response Theory Models 2008A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers 2006A Comparison of Alternative Models for Testlets 2003Estimation of Compensatory and Noncompensatory Multidimensional Item Response Models Using Markov Chain Monte Carlo

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