difNLR: Generalized Logistic Regression Models for DIF and DDF Detection

Adéla Hladká · Patrícia Martinková

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Psychometric Methodologies and Testing Educational Assessment and Pedagogy Statistical Methods and Bayesian Inference

Abstract

Differential item functioning (DIF) and differential distractor functioning (DDF) are important 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 providing 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.

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References

1961Probabilistic Models for Some Intelligence and Attainment Tests 2012Applications of Item Response Theory To Practical Testing Problems 1978A Rating Formulation for Ordered Response Categories 2012mirt : A Multidimensional Item Response Theory Package for the R Environment 1969Estimation of Latent Ability Using a Response Pattern of Graded Scores 1982Probabilistic Models for some Intelligence and Attainment Tests.1990Detecting Differential Item Functioning Using Logistic Regression Procedures 2010A general framework and an R package for the detection of dichotomous differential item functioning

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