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Measuring individual differences in change with multidimensional Rasch models

W C Wang1, M Wilson, R J Adams

  • 1Department of Psychology, National Chung-Cheng University, Chia-Yi, Taiwan.

Journal of Outcome Measurement
|August 26, 1998
PubMed
Summary
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This study introduces the multidimensional random coefficient multinomial logit (MRCML) model for change measurement. Simulation studies confirm the MRCML model

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Existing item response models for change measurement include those by Fischer, Andersen, and Embretson.
  • These models provide frameworks for assessing changes in measured traits over time.

Purpose of the Study:

  • To propose and evaluate a new multidimensional Rasch model: the multidimensional random coefficient multinomial logit (MRCML) model.
  • To compare the MRCML model with existing change measurement models.
  • To investigate the application of the MRCML model to polytomous items and varying item difficulties.

Main Methods:

  • Review and comparison of existing item response models for change measurement.
  • Introduction of the multidimensional random coefficient multinomial logit (MRCML) model.

Related Experiment Videos

  • Development of five specific MRCML models based on variations in item difficulties across occasions and items.
  • Conducting simulation studies to assess parameter recovery under different testing conditions.
  • Analysis of a real dataset to demonstrate practical applications.

    • Main Results:

    • The proposed MRCML model is suitable for polytomous items and analyzing variations in item difficulties.
    • Simulation studies demonstrated excellent recovery of all model parameters across various testing situations.
    • The MRCML model effectively captured individual differences in change using real-world data.

    Conclusions:

    • The MRCML model offers a flexible and robust approach to change measurement in psychometrics.
    • The model's ability to handle polytomous items and item difficulty variations enhances its applicability.
    • The MRCML model shows strong potential for analyzing individual differences in change.