A non-parametric Bayesian diagnostic for detecting differential item functioning in IRT models

Differential item functioning (DIF) in tests and multi-item surveys occurs when a lack of conditional independence exists between the response to one or more items and membership to a particular group, given equal levels of proficiency. We develop an approach to detecting DIF in the context of item response theory (IRT) models based on computing a diagnostic which is the posterior mean of a p-value. IRT models are fit in a Bayesian framework, and simulated proficiency parameters from the posterior distribution are retained. Monte Carlo estimates of the p-value diagnostic are then computed by comparing the fit of nonparametric regressions of item responses on simulated proficiency parameters and group membership. Some properties of our approach are examined through a simulation experiment. We apply our method to the analysis of responses from two separate studies to the BASIS-24, a widely used self-report mental health assessment instrument, to examine DIF between the English and Spanish-translated version of the survey. © Springer Science+Business Media, LLC 2009.