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A mixture Rasch facets model for rater's illusory halo effects.

Kuan-Yu Jin1, Ming Ming Chiu2

  • 1Hong Kong Examinations and Assessment Authority, 7/F, Dah Sing Financial Centre, 248 Queen's Road East, Wan Chai, Hong Kong, Hong Kong. kyjin@hkeaa.edu.hk.

Behavior Research Methods
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Summary

This study introduces a new model to distinguish true halo effects from illusory halo effects in rater assessments. The mixture Rasch facets model for halo effects (MRFM-H) accurately identifies biased raters and improves rating analysis.

Keywords:
FacetsHalo effectsItem response theoryLatent classRater

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Rater impressions can create uniform ratings across criteria (halo effect).
  • Current methods cannot differentiate true halo (homogeneous criteria) from illusory halo (rater bias).

Purpose of the Study:

  • Introduce and test a mixture Rasch facets model for halo effects (MRFM-H).
  • Distinguish true halo from illusory halo effects.
  • Classify raters as normal or halo-prone.

Main Methods:

  • Developed and simulated the mixture Rasch facets model for halo effects (MRFM-H).
  • Tested MRFM-H on three empirical datasets.
  • Compared MRFM-H with a simpler model that ignored halo effects.

Main Results:

  • MRFM-H accurately identified halo raters in simulations with sufficient data.
  • Increased number of rating criteria enhanced classification accuracy.
  • Experienced raters showed illusory halo effects, less likely with more criteria.

Conclusions:

  • MRFM-H effectively differentiates true and illusory halo effects.
  • The model aids in classifying raters and understanding rating biases.
  • Findings have implications for improving the reliability of assessments.