title: Bayesian item fit analysis for unidimensional item response theory models.
abstract: Assessing item fit for unidimensional item response theory models for dichotomous items has always been an issue of enormous interest, but there exists no unanimously agreed item fit diagnostic for these models, and hence there is room for further investigation of the area. This paper employs the posterior predictive model-checking method, a popular Bayesian model-checking tool, to examine item fit for the above-mentioned models. An item fit plot, comparing the observed and predicted proportion-correct scores of examinees with different raw scores, is suggested. This paper also suggests how to obtain posterior predictive p-values (which are natural Bayesian p-values) for the item fit statistics of Orlando and Thissen that summarize numerically the information in the above-mentioned item fit plots. A number of simulation studies and a real data application demonstrate the effectiveness of the suggested item fit diagnostics. The suggested techniques seem to have adequate power and reasonable Type I error rate, and psychometricians will find them promising.
authors.affiliation: Educational Testing Service, Princeton, NJ 08541, USA. ssinharay@ets.org
authors.forename: Sandip
authors.initials: S
authors.lastname: Sinharay
mesh_terms: D000465:Algorithms; D001077:Aptitude Tests / Q000706:statistics & numerical data; D001499:Bayes Theorem; D015982:Bias; D003625:Data Collection / Q000706:statistics & numerical data; D005163:Factor Analysis, Statistical; D006801:Humans; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D011581:Psychological Tests / Q000706:statistics & numerical data; D011594:Psychometrics / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012984:Software