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Testing measurement invariance in a conditional likelihood framework by considering multiple covariates

Clemens Draxler1, Andreas Kurz2

  • 1UMIT TIROL - Private University for Health Sciences and Technology, Eduard-Wallnöfer-Zentrum 1, 6060, Hall in Tirol, Austria. clemens.draxler@umit-tirol.at.

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Summary
This summary is machine-generated.

This study introduces a novel method for testing measurement invariance in Rasch models, crucial for reliable psychometric assessments. The approach enhances the validity of research findings across diverse groups and conditions.

Keywords:
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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Behavioral Research

Background:

  • Measurement invariance is critical for valid comparisons in psychometrics.
  • Rasch models are widely used but require stringent invariance assumptions.
  • Existing methods for testing invariance can be limited.

Purpose of the Study:

  • To develop and validate a new procedure for assessing measurement invariance in Rasch models.
  • To simultaneously estimate and test the effects of multiple covariates on item parameters.
  • To provide robust statistical tests for various sample sizes and data types.

Main Methods:

  • A mixed-effects or random intercept model for binary data was employed.
  • A conditional likelihood approach was used for simultaneous estimation and testing.
  • Four asymptotic tests and one parameter-free test were derived.
  • Generalizations for polytomous data were outlined.

Main Results:

  • The proposed method effectively estimates and tests covariate effects on item parameters.
  • The derived statistical tests offer reliable assessment of invariance.
  • The approach is applicable to longitudinal designs and complex data structures.
  • Illustrations on real and hypothetical data demonstrate practical utility.

Conclusions:

  • The developed methodology provides a powerful tool for ensuring measurement invariance in Rasch models.
  • This enhances the reliability and validity of psychometric research, particularly in behavioral and clinical studies.
  • The approach offers flexibility for binary and categorical data, including longitudinal analyses.