Literature DB >> 22260190

A single g factor is not necessary to simulate positive correlations between cognitive tests.

Dennis J McFarland1.   

Abstract

In the area of abilities testing, one issue of continued dissent is whether abilities are best conceptualized as manifestations of a single underlying general factor or as reflecting the combination of multiple traits that may be dissociable. The fact that diverse cognitive tests tend to be positively correlated has been taken as evidence for a single general ability or "g" factor. In the present study, simulations of test performance were run to evaluate the hypothesis that multiple independent abilities that affect test performance in a consistent manner will produce a positive manifold. Correlation matrices were simulated from models using either one or eight independent factors. The extent to which these factors operated in a consistent manner across tests (i.e., that a factor that facilitates performance on one test tends to facilitate performance on other tests) was manipulated by varying the mean value of the randomly selected weights. The tendency of both a single factor and eight independent factors to produce positive correlations increased as the randomly selected weights operated in a more consistent fashion. Thus the presence of a positive manifold in the correlations between diverse cognitive tests does not provide differential support for either single factor or multiple factor models of general abilities.

Mesh:

Year:  2012        PMID: 22260190     DOI: 10.1080/13803395.2011.645018

Source DB:  PubMed          Journal:  J Clin Exp Neuropsychol        ISSN: 1380-3395            Impact factor:   2.475


  4 in total

Review 1.  How neuroscience can inform the study of individual differences in cognitive abilities.

Authors:  Dennis J McFarland
Journal:  Rev Neurosci       Date:  2017-05-24       Impact factor: 4.353

2.  Evaluation of multidimensional models of WAIS-IV subtest performance.

Authors:  Dennis J McFarland
Journal:  Clin Neuropsychol       Date:  2017-04-21       Impact factor: 3.535

3.  A distributed brain network predicts general intelligence from resting-state human neuroimaging data.

Authors:  Julien Dubois; Paola Galdi; Lynn K Paul; Ralph Adolphs
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-09-26       Impact factor: 6.237

4.  Modeling individual subtests of the WAIS IV with multiple latent factors.

Authors:  Dennis J McFarland
Journal:  PLoS One       Date:  2013-09-13       Impact factor: 3.240

  4 in total

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