A common worry about peer review is that weaker performers make weaker reviewers. Wu and Schunn tested this directly and found the assumption doesn't hold.
Wu, Y., Schunn, C. D. (2022). "Assessor writing performance on peer feedback: Exploring the relation between assessor writing performance, problem identification accuracy, and helpfulness of peer feedback." Journal of Educational Psychology, 115(1), 118–142.
Instructors and managers routinely worry that a lower-skilled reviewer can't be trusted to evaluate someone else's work well. Wu and Schunn tested this assumption directly: does an assessor's own writing performance predict their accuracy at identifying problems in a peer's writing, or the helpfulness of their comments?
The study examined the relationship between assessors' own writing performance and two outcomes of their reviewing: how accurately they identified real problems in a peer's document, and how helpful their resulting comments were rated.
Lower-performing assessors were just as able to detect problems in a peer's document as higher performers. They also gave equally helpful and useful comments overall — the one exception being on very specific problems that the lower-performing assessor also struggled with in their own writing.
The instinct to exclude your weakest performers from a peer-review rotation, on the assumption they have nothing useful to offer as reviewers, is not supported by this research. A struggling employee can still accurately flag a real problem in a colleague's work and explain it helpfully — with the narrow exception of the exact skill gap they personally share. That's a reason to diversify reviewer pools across skill levels, not restrict them to top performers.
Chris Schunn co-authored this study testing whether reviewer skill level limits the value of the feedback they give. He is a Professor of Psychology, Learning Sciences and Policy, and Intelligent Systems at the University of Pittsburgh and a Senior Scientist at the University's Learning Research and Development Center (LRDC), where he has directed research projects backed by more than $80M in federal grants. As Peerceptiv's Chief Learning Scientist, his research directly shapes how the platform structures reviewer prompts, rubrics, and feedback workflows.
