Posterior Predictive Assessment of Item Response Theory Models

Abstract

Model checking in item response theory (IRT) is an underdeveloped area. There is no universally accepted tool for checking IRT models. The posterior predictive model-checking method is a popular Bayesian model-checking tool because it has intuitive appeal, is simple to apply, has a strong theoretical basis, and can provide graphical or numerical evidence about model misfit. An important issue with the application of the posterior predictive model-checking method is the choice of a discrepancy measure (which plays a role like that of a test statistic in traditional hypothesis tests). This article examines the performance of a number of discrepancy measures for assessing different aspects of fit of the common IRT models and makes specific recommendations about what measures are most useful in assessing model fit. Graphical summaries of model-checking results are demonstrated to provide useful insights about model fit.