Sex differences in tech tilt: Support for investment theories

Thomas R. Coyle

Intelligence, 2020, vol. 80, issue C

Abstract: This study examined sex differences in tech tilt, based on within-subject differences in technical abilities (e.g., mechanical and electrical) and academic abilities (math or verbal) on the Armed Services Vocational Aptitude Battery (ASVAB). The within-subject differences produced two types of tilt: tech tilt (tech > academic), indicating stronger technical abilities, and academic tilt (academic > tech), indicating stronger academic abilities. Tech tilt was correlated with math and verbal abilities on college aptitude tests (SAT, ACT, PSAT) and with jobs and college majors in STEM (science, technology, engineering, and math) and humanities. Males showed a tech tilt bias, and females showed an academic tilt bias. The tilt biases persisted after controlling for general intelligence (g). Tech tilt correlated negatively with academic abilities on the college aptitude tests (SAT, ACT, PSAT), with larger effects for females. In addition, relations of tech tilt with STEM jobs and majors were generally larger (and more often significant) for males, but only for tech tilt based on technical and verbal abilities. The negative relations of tech tilt with academic abilities on the college aptitude tests are consistent with investment theories, which predict that investment in one ability (technical) comes at the expense of competing abilities (academic). The sex differences in tech tilt and STEM support trait complexes involving abilities, interests, and vocational preferences (e.g., people versus things). Future research should examine whether spatial abilities and vocational interests mediate relations of tech tilt with sex and STEM criteria.

Keywords: Tech tilt; Ability tilt; STEM; ASVAB; SAT; ACT; PSAT (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intell:v:80:y:2020:i:c:s0160289620300155

DOI: 10.1016/j.intell.2020.101437

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