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This study introduces a new statistical method for detecting differential item functioning (DIF) in the Rasch model. The approach identifies interpretable, previously unknown groups with DIF based on observed characteristics.

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Educational measurement

Background:

  • Differential item functioning (DIF) detection is crucial in psychometrics.
  • Existing Rasch model DIF methods often require pre-specified groups (e.g., male/female).
  • Latent class approaches identify unknown groups but lack direct covariate interpretation.

Purpose of the Study:

  • To propose a novel, interpretable method for DIF detection in the Rasch model.
  • To bridge the gap between pre-specified group comparisons and uninterpretable latent classes.
  • To facilitate hypothesis generation regarding the psychological sources of DIF.

Main Methods:

  • Development of a new DIF detection method using model-based recursive partitioning.
  • Integration of observed covariates to define DIF groups.
  • Application to simulated data and empirical data from a general knowledge quiz.

Main Results:

  • The proposed method successfully detects DIF in previously unknown, interpretable groups.
  • Simulation studies validate the statistical properties and performance of the method.
  • Empirical application demonstrates practical utility in real-world data.

Conclusions:

  • Model-based recursive partitioning offers a powerful and interpretable approach to DIF detection.
  • This method enhances understanding of item bias by linking DIF to observable person characteristics.
  • Freely available R software facilitates the adoption of this novel DIF detection technique.