Validity generalization and situational specificity: A second look at the 75% rule and Fisher's z transformation
Abstract
In this article we analyzed the James, Demaree, and Mulaik (1986) critique of validity generalization. We demonstrated that the James et al. article (a) is not relevant to the real-world use of validity generalization in organizations, (b) has overlooked the bulk of the evidence against the situational specificity hypothesis and, therefore, the substantive conclusion that the situational specificity hypothesis is alive and well cannot be supported, and (c) has confused the processes of hypothesis testing and parameter estimation in validity generalization and has made incorrect statements about the assumptions underlying both. In addition, (d) James et al.'s critique of the 75% rule is a statistical power argument and, as such, does not add to earlier statistical power studies; (e) the procedures for use of confidence intervals that they advocate are erroneous; (f) there is no double correction of artifacts in validity generalization, as they contend; (g) the bias in the correlation (r) and the sampling error formula for r that they discuss is well-known, trivial in magnitude, and has no empirical significance; and (h) the use of the Fisher's z transformation of r in validity generalization studies and other meta-analyses (which they advocate) creates an unnecessary inflationary bias in estimates of true validities and provides no benefits. In light of these facts, we conclude that the James et al. substantive conclusions and methodological recommendations are seriously flawe…
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Only one of the bare-bones sampling error estimates exceeds the 75% rule-of-thumb threshold.
“Only one of these estimates exceeds the 75% rule-ofthumb threshold (Schmidt et al., 1988) indicating that variability amongst the vector correlations likely reflects meaningful residual variance and is therefore more than just sampling error, consistent with the action of potential age moderation.”
From A Meta-Analysis of Spearman’s Hypothesis in the United States (2026) · cites this paper as Schmidt et al., 1988
Cites
Empirical bayes meta-analysis.
Raudenbush, S. W., & Bryk, A. S. (1985) · Journal of Educational Statistics · doi:10.3102/10769986010002075 · Cited by 299 · see citing works
Development and test of a new model for validity generalization.
Callender, J. C., & Osburn, H. G. (1980) · Journal of Applied Psychology · doi:10.1037/0021-9010.65.5.543 · Cited by 112 · see citing works
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Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005) · Personality and Social Psychology Bulletin · doi:10.1177/0146167205275613 · Cited by 1,642 · see citing works
Hunter, J. E., & Schmidt, F. L. (2000) · International Journal of Selection and Assessment · doi:10.1111/1468-2389.00156 · Cited by 833 · see citing works
Self-citationA Meta-Analysis of Spearman’s Hypothesis in the United States
Woodley of Menie, M., Peñaherrera-Aguirre, M., & Sarraf, M. (2026) · Intelligence & Cognitive Abilities · doi:10.65550/001c.171909
- Authors
- Schmidt, F. L., Hunter, J. E., & Raju, N. S.
- Affiliations
- University of Iowa (US)
- ORCID
- Hunter, John E.
- Venue
- Journal of Applied Psychology
- Published
- 1988-11
- Volume
- 73
- Issue
- 4
- Pages
- 665-672
- ISSN
- 0021-9010 (ISSN-L)
- 1939-1854
- OpenAlex
- W2006924846
- Cited by
- 74
- References
- 2
- Citations by year
- 2026: 1 · 2023: 1 · 2015: 1 · 2014: 3 · 2013: 1 · 2012: 3
- FWCI
- 7.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Applied Psychology (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
- Keywords
- validity generalization, Fisher's z-transformation, statistical power, confidence intervals, artifact removal, meta-analysis
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