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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.

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Psychometric Meta-Analysis (Hunter-Schmidt Validity Generalization)

Dossier de méthode taxonomique · process-pipeline / organizational-behavior

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

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Same method familyCommon Method Bias Remediesmachine-suggested · Relational suggestion, not evidence.Used in the same domainEvent History Turnover Analysismachine-suggested · Relational suggestion, not evidence.Same method familyMultitrait-Multimethod Matrixmachine-suggested · Relational suggestion, not evidence.