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An R toolbox for score-based measurement invariance tests in IRT models.

Lennart Schneider1,2, Carolin Strobl3, Achim Zeileis4

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Summary
This summary is machine-generated.

Score-based tests for differential item functioning (DIF) are now accessible via R packages. This enables DIF detection across various item response theory (IRT) models and person covariates, enhancing measurement invariance analysis.

Keywords:
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Area of Science:

  • Psychometrics and Educational Measurement
  • Statistical Computing and Data Analysis

Background:

  • Differential Item Functioning (DIF) detection is crucial for measurement invariance in psychometrics and educational testing.
  • Existing methods for DIF detection are limited in scope regarding person covariates and item response theory (IRT) models.

Approach:

  • Introduces a new family of score-based tests for detecting DIF along arbitrary person covariates within various IRT models.
  • Demonstrates the practical application of these tests using the R system for statistical computing, integrating mirt, psychotools, and strucchange packages.
  • Extends the application to IRT models not previously investigated with these tests, including the generalized partial credit model.

Key Points:

  • Provides a comprehensive review of the theoretical underpinnings of score-based tests for measurement invariance.
  • Details the software implementation within R, facilitating broader user access to advanced DIF detection techniques.
  • Illustrates the utility and interpretation of the software through two empirical datasets.

Conclusions:

  • The developed R implementation makes advanced DIF detection methods accessible to a wider audience of researchers and practitioners.
  • This approach enhances the ability to rigorously assess measurement invariance across diverse populations and complex IRT models.