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Related Experiment Videos

Demonstration of software programs for estimating multilevel measurement model parameters.

J Kyle Roberts1, Rich Herrington

  • 1Baylor College of Medicine, One Baylor Plaza, BCM 155, Houston, TX 77030, USA. jroberts@bcm.tmc.edu

Journal of Applied Measurement
|June 9, 2005
PubMed
Summary

This study demonstrates using five hierarchical linear modeling software programs (SAS, MLWIN, S-PLUS, R, HLM) for the Rasch measurement model. It provides data setup, running instructions, output interpretation, and local independence testing guidance.

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistical Modeling

Background:

  • The Rasch measurement model is a fundamental tool in psychometrics.
  • Hierarchical linear modeling (HLM) offers advanced analytical capabilities for complex data structures.
  • Implementing these models across different software requires clear guidance.

Purpose of the Study:

  • To provide a comprehensive overview of Rasch model parameterizations.
  • To demonstrate the application of five HLM software packages (SAS, MLWIN, S-PLUS, R, HLM) using a heuristic dataset.
  • To offer practical guidance for researchers on data setup, program execution, and output interpretation.

Main Methods:

  • Overview of Rasch model parameterizations.
  • Demonstration of HLM software (SAS, MLWIN, S-PLUS, R, HLM) with a heuristic dataset.

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  • Detailed instructions for data preparation, program execution, and output interpretation for each software.

    • Main Results:

    • Practical, step-by-step guidance for applying the Rasch model using five distinct HLM software packages.
    • Clear interpretation guidelines for the output generated by each program.
    • Discussion on testing the local independence assumption within these frameworks.

    Conclusions:

    • The study facilitates the practical application of the Rasch measurement model through various HLM software.
    • Researchers can leverage the provided data and code for reproducible analysis.
    • Effective implementation and interpretation of Rasch models in HLM are achievable with clear methodological guidance.