Bayes Factor Covariance Testing in Item Response Models

Jean-Paul Fox (Corresponding Author), Joris Mulder, Sandip Sinharay

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)979-1006
JournalPsychometrika
Volume82
Issue number4
DOIs
Publication statusPublished - Dec 2017

Keywords

  • Bayesian inference
  • Bayes factor
  • Marginal IRT
  • Local independence
  • Random item parameter

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