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A Bayesian Approach to Ranking and Rater Evaluation

Author

Listed:
  • Jing Cao
  • S. Lynne Stokes
  • Song Zhang

    (University of Texas Southwestern Medical Center)

Abstract

We develop a Bayesian hierarchical model for the analysis of ordinal data from multirater ranking studies. The model for a rater’s score includes four latent factors: one is a latent item trait determining the true order of items and the other three are the rater’s performance characteristics, including bias, discrimination, and measurement error in the ratings. The proposed approach aims at three goals. First, three Bayesian estimators are introduced to estimate the ranks of items. They all show a substantial improvement over the widely used score sums by using the information on the variable skill of the raters. Second, rater performance can be compared based on rater bias, discrimination, and measurement error. Third, a simulation-based decision-theoretic approach is described to determine the number of raters to employ. A simulation study and an analysis based on a grant review data set are presented.

Suggested Citation

  • Jing Cao & S. Lynne Stokes & Song Zhang, 2010. "A Bayesian Approach to Ranking and Rater Evaluation," Journal of Educational and Behavioral Statistics, , vol. 35(2), pages 194-214, April.
    • Handle: RePEc:sae:jedbes:v:35:y:2010:i:2:p:194-214
    DOI: 10.3102/1076998609353116
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    References listed on IDEAS

    as
    1. Wei Shen & Thomas A. Louis, 1998. "Triple‐goal estimates in two‐stage hierarchical models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 455-471.
    2. David Thissen & Lynne Steinberg, 1986. "A taxonomy of item response models," Psychometrika, Springer;The Psychometric Society, vol. 51(4), pages 567-577, December.
    3. David Andrich, 1978. "A rating formulation for ordered response categories," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 561-573, December.
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