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Related Experiment Videos

Measuring change across multiple occasions using the Rasch Rating Scale Model.

E W Wolfe1, C W Chiu

  • 1Department of Psychiatry, Michigan State University, East Lansing 48824, USA. wolfee@msu.edu

Journal of Outcome Measurement
|November 26, 1999
PubMed
Summary
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This study extends Wright's algorithm to disentangle measurement changes across three occasions. The corrected approach improves diagnostic accuracy for latent trait models, reducing data misfit.

Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Latent trait models are used to measure change over time.
  • Disentangling changes in measurement facets is challenging.
  • Previous work applied Wright's algorithm to two occasions.

Purpose of the Study:

  • Extend Wright's algorithm to analyze changes across three occasions.
  • Demonstrate the algorithm's utility in diagnosing measurement change.
  • Improve the interpretation of change within measurement contexts.

Main Methods:

  • Applied Wright's correction algorithm to a Rasch rating scale analysis.
  • Analyzed multi-occasion data across three time points.
  • Compared results from standard calibration versus corrected analysis.

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Main Results:

  • Standard Rasch rating scale analysis yielded confusing results.
  • Wright's correction revealed expected changes in measures.
  • The algorithm reduced misfit to the Rasch Rating Scale Model.

Conclusions:

  • Wright's algorithm effectively disentangles measurement changes across three occasions.
  • The procedure enhances the interpretability of longitudinal data.
  • This method improves the diagnostic capabilities of latent trait models.