Detection of Differential Item Functioning with difNLR function
In this work we present a new method for detection of Differential Item Functioning (DIF) based on Non-Linear Regression. Detection of DIF has been considered one of the most important topics in measurement and is implemented within packages difR, lordif and others. Procedures based on Logistic Regression are one of the most popular in the study field, however, they do not take into account possibility of guessing or probability of carelessness, which are expectable in multiple-choice tests or in patient reported outcome measures. Methods based on Item Response Theory (IRT) models can count for guessing or for carelessness/inattention, but these latent models may be harder to explain to general audience. We present an extension of Logistic Regression procedure by including probability of guessing and probability of carelessness. This general method based on Non-Linear Regression (NLR) model is used for estimation of Item Response Function and for detection of uniform and non-uniform DIF in dichotomous items. Simulation study suggests that NLR method outperforms or is comparable with the LR-based or IRT-based methods. The new difNLR function provides a nice graphical output and is presented as part of Shiny application ShinyItemAnalysis, which is available online.