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An introduction to multidimensional measurement using Rasch models.

Derek C Briggs1, Mark Wilson

  • 1School of Education, University of California, Berkeley, POME, 3659 Tolman Hall, Berkeley, CA 94720, USA. derekb@uclink4.berkeley.edu

Journal of Applied Measurement
|April 18, 2003
PubMed
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Multidimensional measurement models, like the Multidimensional Random Coefficient Multinomial Logit (MRCML) model, offer superior data fit and more reliable student achievement estimates compared to unidimensional approaches in science education.

Area of Science:

  • Educational Measurement
  • Psychometrics
  • Science Education

Background:

  • Measurement in education often assumes unidimensionality, which may not reflect complex constructs.
  • Multidimensional models offer a more appropriate and advantageous approach when unidimensionality fails.

Purpose of the Study:

  • To illustrate the utility of a multidimensional measurement approach using the MRCML model.
  • To compare the fit and reliability of multidimensional versus unidimensional models for student science achievement.

Main Methods:

  • Utilized the Multidimensional Random Coefficient Multinomial Logit (MRCML) model, an extension of the Rasch model.
  • Applied ConQuest software to estimate structural parameters for both multidimensional and unidimensional models.
  • Compared model fit and student achievement estimates using data from 541 middle school students.

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Main Results:

  • The multidimensional approach demonstrated superior fit to the empirical data.
  • Multidimensional models provided more reliable estimates of student achievement than unidimensional models.
  • The multidimensional approach yielded richer insights into student achievement for teachers.

Conclusions:

  • Multidimensional measurement models are technically appropriate and substantively advantageous when constructs are not unidimensional.
  • The MRCML model offers a valuable tool for analyzing complex educational assessments.
  • Adopting multidimensional approaches can enhance the accuracy and interpretability of student achievement data in science education.