Psychometric Meta-Analysis
Psychometric meta-analysis is the Hunter-Schmidt approach to cumulating research findings while correcting for the statistical artifacts that distort individual studies. Frank Schmidt and John Hunter developed it to solve the problem of validity generalization: across many studies the observed validity of a selection test varied widely, leading people to conclude that validity was situationally specific, when in fact most of the variation was an illusion produced by small samples, unreliable measures, and restricted ranges. Their 1977 Journal of Applied Psychology paper showed that once these artifacts are removed, the apparent variability shrinks and a stable true validity emerges that generalizes across settings. The full method, codified in their book Methods of Meta-Analysis, pools effect sizes, subtracts the variance due to sampling error, and corrects the mean and remaining variance for measurement unreliability and range restriction. It estimates not only the average true effect but how much it really varies and whether it generalizes.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Hunter, J. E., & Schmidt, F. L. (2004). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (2nd ed.). Sage Publications. · ISBN 9781412904797
- Schmidt, F. L., & Hunter, J. E. (1977). Development of a general solution to the problem of validity generalization. Journal of Applied Psychology, 62(5), 529-540. · DOI 10.1037/0021-9010.62.5.529
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.