The R Journal: article published in 2020, volume 12:1

difNLR: Generalized Logistic Regression Models for DIF and DDF Detection PDF download
Adéla Hladká and Patrícia Martinková , The R Journal (2020) 12:1, pages 300-323.

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.

Received: .na.character; online 2020-09-10
CRAN packages: difR, DIFlasso, DIFboost, GDINA, mirt, lordif, psychotree, difNLR, ShinyItemAnalysis, ggplot2, stats, VGAM, nnet
CRAN Task Views implied by cited CRAN packages: Psychometrics, Econometrics, SocialSciences, Distributions, Environmetrics, ExtremeValue, Graphics, MachineLearning, MissingData, Multivariate, Phylogenetics, Survival, TeachingStatistics

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This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Adéla Hladká and Patrícia Martinková},
  title = {{difNLR: Generalized Logistic Regression Models for DIF and
          DDF Detection}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-014},
  url = {},
  pages = {300--323},
  volume = {12},
  number = {1}