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Summary
This summary is machine-generated.

This study introduces a new data-driven framework for testing measurement invariance in Rasch models. It automatically detects groups, improving power over traditional methods when group structures are unknown.

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differential item functioningdifferential step functionmeasurement invariancemodel-based recursive partitioningpartial credit modelrating scale modeltree

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Educational measurement

Background:

  • Measurement invariance is crucial for valid psychometric models across diverse groups.
  • Traditional global model tests, like the likelihood ratio (LR) test, require pre-defined reference and focal groups.
  • Existing methods are limited when group structures are unknown a priori.

Purpose of the Study:

  • To propose a novel data-driven framework for global model tests in polytomous Rasch models.
  • To eliminate the need for a priori group specification in measurement invariance testing.
  • To enhance the power and applicability of measurement invariance tests in practical settings.

Main Methods:

  • Development of a model-based recursive partitioning algorithm for automatic group detection.
  • Statistical background and theoretical framework for the new approach.
  • Simulation studies comparing the new framework with the traditional likelihood ratio test.

Main Results:

  • The new framework, like the LR test, is sensitive to differential item and step functioning.
  • Both methods maintain significance levels irrespective of true ability distribution differences.
  • The data-driven approach demonstrates superior power when group structures are not pre-specified.

Conclusions:

  • The proposed framework offers a more powerful and practical solution for measurement invariance testing in polytomous Rasch models.
  • Automatic, data-driven group detection overcomes limitations of traditional methods.
  • Freely available R software implementation facilitates its application in empirical research.