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Aligning Predictor-Criterion Bandwidths: Specific Abilities as Predictors of Specific Performance

1
School of Psychological Science, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
2
School of Social Sciences, Singapore Management University, 90 Stamford Road, Level 4, Singapore 178903, Singapore
Received: 13 March 2018 / Revised: 24 May 2018 / Accepted: 25 June 2018 / Published: 7 September 2018
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Abstract

The purpose of the current study is to compare the extent to which general and specific abilities predict academic performances that are also varied in breadth (i.e., general performance and specific performance). The general and specific constructs were assumed to vary only in breadth, not order, and two data analytic approaches (i.e., structural equation modeling [SEM] and relative weights analysis) consistent with this theoretical assumption were compared. Conclusions regarding the relative importance of general and specific abilities differed based on data analytic approaches. The SEM approach identified general ability as the strongest and only significant predictor of general academic performance, with neither general nor specific abilities predicting any of the specific subject grade residuals. The relative weights analysis identified verbal reasoning as contributing more than general ability, or other specific abilities, to the explained variance in general academic performance. Verbal reasoning also contributed to most of the explained variance in each of the specific subject grades. These results do not provide support for the utility of predictor-criterion alignment, but they do provide evidence that both general and specific abilities can serve as useful predictors of performance. View Full-Text
Keywords: specific ability; second stratum abilities; academic performance; nested-factor models; relative importance analysis; predictor-criterion bandwidth alignment specific ability; second stratum abilities; academic performance; nested-factor models; relative importance analysis; predictor-criterion bandwidth alignment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wee, S. Aligning Predictor-Criterion Bandwidths: Specific Abilities as Predictors of Specific Performance. J. Intell. 2018, 6, 40.

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