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Bayes Factor Covariance Testing in Item Response Models

Published online by Cambridge University Press:  01 January 2025

Jean-Paul Fox,
Joris Mulder and
Sandip Sinharay
Jean-Paul Fox*
Affiliation:
University of Twente
Joris Mulder
Affiliation:
Tilburg University
Sandip Sinharay
Affiliation:
Educational Testing Service
*
Correspondence should be made to Jean-Paul Fox, Department of Research Methodology, Measurement and Data Analysis, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands. Email: j.p.fox@utwente.nl
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Abstract

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies.

Keywords

Bayesian inferenceBayes factormarginal IRTlocal independencerandom item parameter

Information

Type
Original Paper
Information
Psychometrika , Volume 82 , Issue 4 , December 2017, pp. 979 - 1006
Copyright
Copyright © 2017 The Psychometric Society

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