A Monte Carlo Examination of the Sensitivity of the Differential Functioning of Items and Tests Framework for Tests of Measurement Invariance With Likert Data

Abstract

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) under some conditions of differential functioning with previously recommended significance values. The differential test functioning index was extremely insensitive to differential functioning under all study conditions. The authors recommend alternative noncompensatory DIF cutoff values used to evaluate the significance of DIF for different DIF effect sizes. Additionally, contrasts between polytomous and dichotomous data are drawn, and problems with determining measurement invariance at the scale, rather than item, level for Likert scale data are highlighted.