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Psychometric Network Analysis of the Hungarian WAIS
The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and reflective, higher-orde...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6789747/ https://www.ncbi.nlm.nih.gov/pubmed/31505834 http://dx.doi.org/10.3390/jintelligence7030021 |
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author | Schmank, Christopher J. Goring, Sara Anne Kovacs, Kristof Conway, Andrew R. A. |
author_facet | Schmank, Christopher J. Goring, Sara Anne Kovacs, Kristof Conway, Andrew R. A. |
author_sort | Schmank, Christopher J. |
collection | PubMed |
description | The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and reflective, higher-order latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. These competing theories of intelligence are compared using two different statistical modeling techniques: (a) latent variable modeling and (b) psychometric network analysis. Network models display partial correlations between pairs of observed variables that demonstrate direct relationships among observations. Secondary data analysis was conducted using the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV). The underlying structure of the H-WAIS-IV was first assessed using confirmatory factor analysis assuming a reflective, higher-order model and then reanalyzed using psychometric network analysis. The compatibility (or lack thereof) of these theoretical accounts of intelligence with the data are discussed. |
format | Online Article Text |
id | pubmed-6789747 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67897472019-10-16 Psychometric Network Analysis of the Hungarian WAIS Schmank, Christopher J. Goring, Sara Anne Kovacs, Kristof Conway, Andrew R. A. J Intell Article The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and reflective, higher-order latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. These competing theories of intelligence are compared using two different statistical modeling techniques: (a) latent variable modeling and (b) psychometric network analysis. Network models display partial correlations between pairs of observed variables that demonstrate direct relationships among observations. Secondary data analysis was conducted using the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV). The underlying structure of the H-WAIS-IV was first assessed using confirmatory factor analysis assuming a reflective, higher-order model and then reanalyzed using psychometric network analysis. The compatibility (or lack thereof) of these theoretical accounts of intelligence with the data are discussed. MDPI 2019-09-09 /pmc/articles/PMC6789747/ /pubmed/31505834 http://dx.doi.org/10.3390/jintelligence7030021 Text en © 2019 by the authors. 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 | Article Schmank, Christopher J. Goring, Sara Anne Kovacs, Kristof Conway, Andrew R. A. Psychometric Network Analysis of the Hungarian WAIS |
title | Psychometric Network Analysis of the Hungarian WAIS |
title_full | Psychometric Network Analysis of the Hungarian WAIS |
title_fullStr | Psychometric Network Analysis of the Hungarian WAIS |
title_full_unstemmed | Psychometric Network Analysis of the Hungarian WAIS |
title_short | Psychometric Network Analysis of the Hungarian WAIS |
title_sort | psychometric network analysis of the hungarian wais |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6789747/ https://www.ncbi.nlm.nih.gov/pubmed/31505834 http://dx.doi.org/10.3390/jintelligence7030021 |
work_keys_str_mv | AT schmankchristopherj psychometricnetworkanalysisofthehungarianwais AT goringsaraanne psychometricnetworkanalysisofthehungarianwais AT kovacskristof psychometricnetworkanalysisofthehungarianwais AT conwayandrewra psychometricnetworkanalysisofthehungarianwais |