difNLR: Generalized Logistic Regression Models for DIF and DDF Detection

Authors

Adéla Hladká, Institute of Computer Science of the Czech Academy of Sciences, Charles University
Patrícia Martinková, Institute of Computer Science of the Czech Academy of Sciences, Charles University

Date of this Version

6-2020

Document Type

Article

Citation

The R Journal (June 2020) 12(1); Editor: Michael J. Kane

Comments

Copyright 2020, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

Differential item functioning (DIF) and differential distractor functioning (DDF) are impor tant topics in psychometrics, pointing to potential unfairness in items with respect to minorities or different social groups. Various methods have been proposed to detect these issues. The difNLR R package extends DIF methods currently provided in other packages by offering approaches based on generalized logistic regression models that account for possible guessing or inattention, and by pro viding methods to detect DIF and DDF among ordinal and nominal data. In the current paper, we describe implementation of the main functions of the difNLR package, from data generation, through the model fitting and hypothesis testing, to graphical representation of the results. Finally, we provide a real data example to bring the concepts together