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Cited by 908

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Academic Performance, Career Potential, Creativity, and Job Performance: Can One Construct Predict Them All?

Kuncel, Nathan R., Hezlett, Sarah A., Ones, Deniz S.·2004·Journal of Personality and Social Psychology

Abstract

This meta-analysis addresses the question of whether 1 general cognitive ability measure developed for predicting academic performance is valid for predicting performance in both educational and work domains. The validity of the Miller Analogies Test (MAT; W. S. Miller, 1960) for predicting 18 academic and work-related criteria was examined. MAT correlations with other cognitive tests (e.g., Raven's Matrices [J. C. Raven, 1965]; Graduate Record Examinations) also were meta-analyzed. The results indicate that the abilities measured by the MAT are shared with other cognitive ability instruments and that these abilities are generalizably valid predictors of academic and vocational criteria, as well as evaluations of career potential and creativity. These findings contradict the notion that intelligence at work is wholly different from intelligence at school, extending the voluminous literature that supports the broad importance of general cognitive ability (g).

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Metadata

Authors
Kuncel, N. R., Hezlett, S. A., & Ones, D. S.
Venue
Journal of Personality and Social Psychology
Published
2004
Volume
86
Issue
1
Pages
148-161
ISSN
  • 0022-3514 (ISSN-L)
  • 1939-1315
OpenAlex
W2127837421
Cited by
908
References
9
FWCI
32.04
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Academic Performance, Career Potential, Creativity, and Job Performance: Can One Construct Predict Them All? https://app.cochat.ai/papers/10.1037/0022-3514.86.1.148
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Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic Performance, Career Potential, Creativity, and Job Performance: Can One Construct Predict Them All? Journal of Personality and Social Psychology. https://doi.org/10.1037/0022-3514.86.1.148

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.