A Penalty Approach to Differential Item Functioning in Rasch Models

Gerhard Tutz · Gunther Schauberger

105 citations0/0 files storedPsychometrika

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Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Statistical Methods and Inference

Abstract

A new diagnostic tool for the identification of differential item functioning (DIF) is proposed. Classical approaches to DIF allow to consider only few subpopulations like ethnic groups when investigating if the solution of items depends on the membership to a subpopulation. We propose an explicit model for differential item functioning that includes a set of variables, containing metric as well as categorical components, as potential candidates for inducing DIF. The ability to include a set of covariates entails that the model contains a large number of parameters. Regularized estimators, in particular penalized maximum likelihood estimators, are used to solve the estimation problem and to identify the items that induce DIF. It is shown that the method is able to detect items with DIF. Simulations and two applications demonstrate the applicability of the method.

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References

1961Probabilistic Models for Some Intelligence and Attainment Tests 2012Applications of Item Response Theory To Practical Testing Problems 1982A Rasch Model for Partial Credit Scoring 1997Handbook of Modern Item Response Theory 1994Differential Item Functioning.1982Probabilistic Models for some Intelligence and Attainment Tests.1990Detecting Differential Item Functioning Using Logistic Regression Procedures 1997Graded Response Model

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2019Simplifying the Assessment of Measurement Invariance over Multiple Background Variables: Using Regularized Moderated Nonlinear Factor Analysis to Detect Differential Item Functioning 2020Improving the assessment of measurement invariance: Using regularization to select anchor items and identify differential item functioning.2019A Practical Guide to Variable Selection in Structural Equation Modeling by Using Regularized Multiple-Indicators, Multiple-Causes Models 2014Detection of Differential Item Functioning Using the Lasso Approach 2019Regularized Structural Equation Modeling to Detect Measurement Bias: Evaluation of Lasso, Adaptive Lasso, and Elastic Net

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