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How to Compare Psychometric Factor and Network Models
In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We f...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709577/ https://www.ncbi.nlm.nih.gov/pubmed/33023229 http://dx.doi.org/10.3390/jintelligence8040035 |
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author | Kan, Kees-Jan de Jonge, Hannelies van der Maas, Han L. J. Levine, Stephen Z. Epskamp, Sacha |
author_facet | Kan, Kees-Jan de Jonge, Hannelies van der Maas, Han L. J. Levine, Stephen Z. Epskamp, Sacha |
author_sort | Kan, Kees-Jan |
collection | PubMed |
description | In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples. |
format | Online Article Text |
id | pubmed-7709577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77095772020-12-03 How to Compare Psychometric Factor and Network Models Kan, Kees-Jan de Jonge, Hannelies van der Maas, Han L. J. Levine, Stephen Z. Epskamp, Sacha J Intell Commentary In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples. MDPI 2020-10-02 /pmc/articles/PMC7709577/ /pubmed/33023229 http://dx.doi.org/10.3390/jintelligence8040035 Text en © 2020 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 | Commentary Kan, Kees-Jan de Jonge, Hannelies van der Maas, Han L. J. Levine, Stephen Z. Epskamp, Sacha How to Compare Psychometric Factor and Network Models |
title | How to Compare Psychometric Factor and Network Models |
title_full | How to Compare Psychometric Factor and Network Models |
title_fullStr | How to Compare Psychometric Factor and Network Models |
title_full_unstemmed | How to Compare Psychometric Factor and Network Models |
title_short | How to Compare Psychometric Factor and Network Models |
title_sort | how to compare psychometric factor and network models |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709577/ https://www.ncbi.nlm.nih.gov/pubmed/33023229 http://dx.doi.org/10.3390/jintelligence8040035 |
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