Non- Factors Predict Educational and Occupational Criteria: More than .

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ID: 50748
2018
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Abstract
In a prior issue of the , I argued that the most important scientific issue in intelligence research was to identify specific abilities with validity beyond (i.e., variance common to mental tests) (Coyle, T.R. Predictive validity of non- residuals of tests: More than . 2014, , 21-25.). In this Special Issue, I review my research on specific abilities related to non- factors. The non- factors include specific math and verbal abilities based on standardized tests (SAT, ACT, PSAT, Armed Services Vocational Aptitude Battery). I focus on two non- factors: (a) , obtained after removing from tests, and (b) , defined as within-subject differences between math and verbal scores, yielding math tilt (math > verbal) and verbal tilt (verbal > math). In general, math residuals and tilt positively predict STEM criteria (college majors, jobs, GPAs) and negatively predict humanities criteria, whereas verbal residuals and tilt show the opposite pattern. The paper concludes with suggestions for future research, with a focus on theories of non- factors (e.g., investment theories, Spearman's Law of Diminishing Returns, Cognitive Differentiation-Integration Effort Model) and a magnification model of non- factors.
Reference Key
coyle2018nonjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Coyle, Thomas R;
Journal journal of intelligence
Year 2018
DOI
E43
URL
Keywords
ability tilt general intelligence (g) non-g factors non-g residuals specific abilities Deep learning smart cities crowd analysis oxidative stress transition metals saccharomyces cerevisiae stress resistance adaptive laboratory evolution iron stress pho84 evolutionary engineering antioxidant chitosan selenium alcohol nano chitooligosaccharide

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