Non-g residuals of group factors predict ability tilt, college majors, and jobs: A non-g nexus

Thomas R. Coyle

Intelligence, 2018, vol. 67, issue C, 19-25

Abstract: This study examined the predictive power of non-g residuals of group factors (based on multiple tests) for diverse criteria (e.g., aptitude tests, college majors, occupations). Test scores were drawn from the National Longitudinal Survey of Youth (N=1950). Four group factors (math, verbal, speed, shop/technical) were estimated using the Armed Services Vocational Aptitude Battery, a diverse battery of 12 cognitive tests. The residuals of the group factors were estimated after removing g (variance common to all tests) and were correlated with aptitude test scores (SAT, ACT, PSAT), ability tilt (i.e., difference between math and verbal scores on the aptitude tests), and college majors and jobs in science, technology, engineering, and math (STEM) and the humanities. The math residuals correlated positively with math/STEM criteria and negatively with verbal/humanities criteria. In contrast, the verbal residuals showed the opposite pattern. The residuals of the two non-academic factors (speed and shop) generally correlated negligibly with all criteria. The results are the first to demonstrate the predictive power of group factor residuals for diverse criteria. The findings extend prior research on non-g factors for individual tests (SAT and ACT) and provide evidence of a non-g nexus involving group factors. The pattern of results supports investment theories, which predict that investment in one area (math) correlates positively with complementary criteria (math/STEM) but negatively with competing criteria (verbal/humanities).

Keywords: g; Non-g factors; Non-g residuals; College majors; Occupations; SAT, ACT, PSAT, ASVAB (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intell:v:67:y:2018:i:c:p:19-25

DOI: 10.1016/j.intell.2017.12.003

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