Show brief item record

dc.contributor.authorCoyle, Thomas R.
dc.date.accessioned2021-04-19T15:10:46Z
dc.date.available2021-04-19T15:10:46Z
dc.date.issued9/7/2018
dc.identifierdoi: 10.3390/jintelligence6030043
dc.identifier.citationJournal of Intelligence 6 (3): 43 (2018)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/420
dc.description.abstractIn a prior issue of the <i>Journal of Intelligence</i>, I argued that the most important scientific issue in intelligence research was to identify specific abilities with validity beyond <i>g</i> (i.e., variance common to mental tests) (Coyle, T.R. Predictive validity of non-<i>g</i> residuals of tests: More than <i>g</i>. <i>Journal of Intelligence</i> 2014, <i>2</i>, 21&ndash;25.). In this Special Issue, I review my research on specific abilities related to non-<i>g</i> factors. The non-<i>g</i> factors include specific math and verbal abilities based on standardized tests (SAT, ACT, PSAT, Armed Services Vocational Aptitude Battery). I focus on two non-<i>g</i> factors: (a) <i>non-g residuals</i>, obtained after removing <i>g</i> from tests, and (b) <i>ability tilt</i>, defined as within-subject differences between math and verbal scores, yielding math tilt (math &gt; verbal) and verbal tilt (verbal &gt; 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-<i>g</i> factors (e.g., investment theories, Spearman&rsquo;s Law of Diminishing Returns, Cognitive Differentiation-Integration Effort Model) and a magnification model of non-<i>g</i> factors.
dc.titleNon-g Factors Predict Educational and Occupational Criteria: More than g
dc.date.updated2021-04-19T15:10:46Z
dc.description.departmentPsychology


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show brief item record