Benchmark validation of statistical models: Application to mediation analysis of imagery and memory

David Mackinnon, Matthew J. Valente, Ingrid C. Wurpts

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation.

Original languageEnglish (US)
Pages (from-to)654-671
Number of pages18
JournalPsychological Methods
Issue number4
StatePublished - Dec 2018


  • Mediation
  • Methods validation
  • Replication

ASJC Scopus subject areas

  • Psychology (miscellaneous)


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