A reliability-generalization study of journal peer reviews: a multilevel meta-analysis of inter-rater reliability and its determinants

PLoS One. 2010 Dec 14;5(12):e14331. doi: 10.1371/journal.pone.0014331.


Background: This paper presents the first meta-analysis for the inter-rater reliability (IRR) of journal peer reviews. IRR is defined as the extent to which two or more independent reviews of the same scientific document agree.

Methodology/principal findings: Altogether, 70 reliability coefficients (Cohen's Kappa, intra-class correlation [ICC], and Pearson product-moment correlation [r]) from 48 studies were taken into account in the meta-analysis. The studies were based on a total of 19,443 manuscripts; on average, each study had a sample size of 311 manuscripts (minimum: 28, maximum: 1983). The results of the meta-analysis confirmed the findings of the narrative literature reviews published to date: The level of IRR (mean ICC/r2=.34, mean Cohen's Kappa=.17) was low. To explain the study-to-study variation of the IRR coefficients, meta-regression analyses were calculated using seven covariates. Two covariates that emerged in the meta-regression analyses as statistically significant to gain an approximate homogeneity of the intra-class correlations indicated that, firstly, the more manuscripts that a study is based on, the smaller the reported IRR coefficients are. Secondly, if the information of the rating system for reviewers was reported in a study, then this was associated with a smaller IRR coefficient than if the information was not conveyed.

Conclusions/significance: Studies that report a high level of IRR are to be considered less credible than those with a low level of IRR. According to our meta-analysis the IRR of peer assessments is quite limited and needs improvement (e.g., reader system).

Publication types

  • Meta-Analysis

MeSH terms

  • Bayes Theorem
  • Humans
  • Models, Statistical
  • Observer Variation
  • Peer Review*
  • Periodicals as Topic
  • Publications
  • Regression Analysis
  • Reproducibility of Results
  • Research Design