Abstrakt
Detection of Differential Item Functioning (DIF) has been considered one of the most important topics in measurement. Procedure based on Logistic Regression is one of the most popular tools in study field, however, it does not take into account possibility of guessing, which is expectable especially in multiple-choice tests.
In this work, we present an extension of Logistic regression procedure by including probability of guessing. This general method based on Non-Linear Regression (NLR) model is used for detection of uniform and non-uniform DIF in dichotomous items.
NLR technique for DIF detection is compared to Logistic Regression procedure and methods based on three parameter Item Response Theory model (Lord's and Raju's statistics) in simulation study based on Graduate Management Admission Test. NLR method outperforms Logistic Regression procedure in power for case of uniform DIF detection and moreover by providing estimate of pseudo-guessing parameter.
Proposed method also shows superiority in power at rejection rate lower than nominal value when compared to Lord's and Raju's methods. The proposed NLR method is accompanied by an R package difNLR and is implemented in an online Shiny application ShinyItemAnalysis.