A social network approach to peer assessment: Improving predictive validity

Gil Luria, Yuval Kalish

Research output: Contribution to journalArticlepeer-review


Our premise is that the simple measure used in peer assessment (i.e., number of peer nominations) does not capture the complexity of social information processing and therefore has limited predictive validity. Based on indicators derived from social network analysis and social information processing theories, we suggest new measures (nominations-by-nominees and nominations-not-returned) to enhance the predictive validity of peer assessment. We then compare the validity of existing measures with ours, using a longitudinal sample of 249 soldiers, divided into 18 groups. The soldiers first assessed each other on friendly behavior and instrumental contribution to the team. More than six months later, the commanders of the 132 soldiers in the unit under review provided evaluations of their performance in regard to stress, engagement, and leadership. We found that our new, complex measures predicted performance above and beyond the traditional measure. Theoretical and applied implications are discussed.

Original languageEnglish
Pages (from-to)537-560
Number of pages24
JournalHuman Resource Management
Issue number4
StatePublished - Jul 2013


  • Egocentric network density
  • Indegree centrality
  • Longitudinal data
  • Nonreciprocity
  • Peer assessment
  • Performance
  • Social information processing
  • Social network analysis

ASJC Scopus subject areas

  • Applied Psychology
  • Strategy and Management
  • Organizational Behavior and Human Resource Management
  • Management of Technology and Innovation


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