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Non-g Factors Predict Educational and Occupational Criteria: More than g
In a prior issue of the Journal of Intelligence, I argued that the most important scientific issue in intelligence research was to identify specific abilities with validity beyond g (i.e., variance common to mental tests) (Coyle, T.R. Predictive validity of non-g residuals of tests: More than g. Jou...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480787/ https://www.ncbi.nlm.nih.gov/pubmed/31162470 http://dx.doi.org/10.3390/jintelligence6030043 |
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author | Coyle, Thomas R. |
author_facet | Coyle, Thomas R. |
author_sort | Coyle, Thomas R. |
collection | PubMed |
description | In a prior issue of the Journal of Intelligence, I argued that the most important scientific issue in intelligence research was to identify specific abilities with validity beyond g (i.e., variance common to mental tests) (Coyle, T.R. Predictive validity of non-g residuals of tests: More than g. Journal of Intelligence 2014, 2, 21–25.). In this Special Issue, I review my research on specific abilities related to non-g factors. The non-g factors include specific math and verbal abilities based on standardized tests (SAT, ACT, PSAT, Armed Services Vocational Aptitude Battery). I focus on two non-g factors: (a) non-g residuals, obtained after removing g from tests, and (b) ability tilt, 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-g factors (e.g., investment theories, Spearman’s Law of Diminishing Returns, Cognitive Differentiation-Integration Effort Model) and a magnification model of non-g factors. |
format | Online Article Text |
id | pubmed-6480787 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64807872019-05-29 Non-g Factors Predict Educational and Occupational Criteria: More than g Coyle, Thomas R. J Intell Commentary In a prior issue of the Journal of Intelligence, I argued that the most important scientific issue in intelligence research was to identify specific abilities with validity beyond g (i.e., variance common to mental tests) (Coyle, T.R. Predictive validity of non-g residuals of tests: More than g. Journal of Intelligence 2014, 2, 21–25.). In this Special Issue, I review my research on specific abilities related to non-g factors. The non-g factors include specific math and verbal abilities based on standardized tests (SAT, ACT, PSAT, Armed Services Vocational Aptitude Battery). I focus on two non-g factors: (a) non-g residuals, obtained after removing g from tests, and (b) ability tilt, 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-g factors (e.g., investment theories, Spearman’s Law of Diminishing Returns, Cognitive Differentiation-Integration Effort Model) and a magnification model of non-g factors. MDPI 2018-09-07 /pmc/articles/PMC6480787/ /pubmed/31162470 http://dx.doi.org/10.3390/jintelligence6030043 Text en © 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Commentary Coyle, Thomas R. Non-g Factors Predict Educational and Occupational Criteria: More than g |
title | Non-g Factors Predict Educational and Occupational Criteria: More than g |
title_full | Non-g Factors Predict Educational and Occupational Criteria: More than g |
title_fullStr | Non-g Factors Predict Educational and Occupational Criteria: More than g |
title_full_unstemmed | Non-g Factors Predict Educational and Occupational Criteria: More than g |
title_short | Non-g Factors Predict Educational and Occupational Criteria: More than g |
title_sort | non-g factors predict educational and occupational criteria: more than g |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480787/ https://www.ncbi.nlm.nih.gov/pubmed/31162470 http://dx.doi.org/10.3390/jintelligence6030043 |
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