The regDIF R Package: Evaluating Complex Sources of Measurement Bias Using Regularized Differential Item Functioning

Abstract

Measurement bias (MB), or differences in the measurement properties of a latent variable, is often evaluated for a single categorical background variable (e.g., gender). However, recent statistical advances now allow MB to be simultaneously evaluated across multiple continuous and categorical background variables (e.g., gender, age, culture). Regularization has also shown promising results for selecting true differential item functioning (DIF) effects in high-dimensional measurement models. Despite this progress, current software tools make it difficult for applied researchers to implement regularized DIF (Reg-DIF). The regDIF R package is thus introduced and shown to be a relatively fast and flexible implementation of the Reg-DIF method. Namely, regDIF allows for simultaneous modeling of multiple background variables, a variety of different item response functions, and multiple types of penalty methods, among other possibilities. This article demonstrates these features using simulated and real data and provides example code for researchers to use in their own work.