A Bayesian approach to ranking and rater evaluation: An application to grant reviews

Jing Cao, S. Lynne Stokes, Song Zhang

Research output: Contribution to journalReview articlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)194-214
Number of pages21
JournalJournal of Educational and Behavioral Statistics
Issue number2
StatePublished - Jun 2010


  • Bayesian hierarchical model
  • Multirater ordinal data
  • Number of raters
  • Ranking estimator
  • Rater performance

ASJC Scopus subject areas

  • Education
  • Social Sciences (miscellaneous)


Dive into the research topics of 'A Bayesian approach to ranking and rater evaluation: An application to grant reviews'. Together they form a unique fingerprint.

Cite this