Differential item functioning (DIF) analysis is an important step in the data analysis of large-scale testing programs. Nowadays, many such programs endorse matrix sampling designs to reduce the load on examinees, such as the balanced incomplete block (BIB) design. These designs pose challenges to the traditional DIF analysis methods. For example,…

There has been growing use of ideal point models to develop scales measuring important psychological constructs. For meaningful comparisons across groups, it is important to identify items on such scales that exhibit differential item functioning (DIF). In this study, the authors examined several methods for assessing DIF on polytomous items…

Mixtures of item response theory (IRT) models have been proposed as a technique to explore response patterns in test data related to cognitive strategies, instructional sensitivity, and differential item functioning (DIF). Estimation proves challenging due to difficulties in identification and questions of effect size needed to recover underlying…

This article highlights issues associated with the use of the differential functioning of items and tests (DFIT) methodology for assessing measurement invariance (or differential functioning) with Likert-type data. Monte Carlo analyses indicate relatively low sensitivity of the DFIT methodology for identifying differential item functioning (DIF)…