Bayesian modelling of differential item functioning: type I error and power rates in the presence of non-normal ability distributions, impact, and anchor set contamination Online publication date: Tue, 29-Apr-2014
by W. Holmes Finch; Brian F. French
International Journal of Quantitative Research in Education (IJQRE), Vol. 1, No. 4, 2013
Abstract: Differential item functioning (DIF) refers to the case where performance on an item differs between groups of examinees when the underlying latent trait being assessed is held constant. The DIF literature supports the conclusion that standard detection methods are effective across many conditions; however, there are other situations in which these methods lack accuracy. There has been increased interest in the use of Bayesian estimation across a wide area of statistical practice, including in educational measurement. Bayesian methods may be able to assist with the lack of accuracy. The goal of this simulation study was to examine the performance of Bayesian models with informative priors for detecting DIF under a variety of conditions. Results illustrate a positive performance of two such methods, Bayesian versions of logistic regression, and the MIMIC model. Type I error rates for these approaches were controlled when the distribution of θ differed between groups, while power rates were comparable across methods and conditions. This paper concludes with a discussion of the implications for practice, and directions for future research.
Online publication date: Tue, 29-Apr-2014
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