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Regularized Exploratory Bifactor Analysis With Small Sample Sizes
Several methods of factor extraction have recently gained popularity as a procedure for dealing with estimation problems associated with small sample sizes, which can be found in the various behavioral science disciplines, such as comparative psychology and behavior genetics. Two popular approaches...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7179684/ https://www.ncbi.nlm.nih.gov/pubmed/32372995 http://dx.doi.org/10.3389/fpsyg.2020.00507 |
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author | Jung, Sunho Seo, Dong Gi Park, Jungkyu |
author_facet | Jung, Sunho Seo, Dong Gi Park, Jungkyu |
author_sort | Jung, Sunho |
collection | PubMed |
description | Several methods of factor extraction have recently gained popularity as a procedure for dealing with estimation problems associated with small sample sizes, which can be found in the various behavioral science disciplines, such as comparative psychology and behavior genetics. Two popular approaches for particularly small samples (below 50) include unweighted least squares factor analysis (ULS-FA) and regularized exploratory factor analysis (REFA). However, it is unclear how well each of the approaches performs with small samples in the context of exploratory bifactor modeling. In the current study, a comprehensive simulation study was conducted to evaluate the small sample behavior of the two approaches in terms of bifactor structure recovery under different sample size, factor loading, number of variables per factor, number of factors, and factor correlation experimental conditions. The results show that REFA is recommended for use over ULS-FA, particularly in the conditions involving low factor loadings, few group factors, or a small number of variables per factor. |
format | Online Article Text |
id | pubmed-7179684 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71796842020-05-05 Regularized Exploratory Bifactor Analysis With Small Sample Sizes Jung, Sunho Seo, Dong Gi Park, Jungkyu Front Psychol Psychology Several methods of factor extraction have recently gained popularity as a procedure for dealing with estimation problems associated with small sample sizes, which can be found in the various behavioral science disciplines, such as comparative psychology and behavior genetics. Two popular approaches for particularly small samples (below 50) include unweighted least squares factor analysis (ULS-FA) and regularized exploratory factor analysis (REFA). However, it is unclear how well each of the approaches performs with small samples in the context of exploratory bifactor modeling. In the current study, a comprehensive simulation study was conducted to evaluate the small sample behavior of the two approaches in terms of bifactor structure recovery under different sample size, factor loading, number of variables per factor, number of factors, and factor correlation experimental conditions. The results show that REFA is recommended for use over ULS-FA, particularly in the conditions involving low factor loadings, few group factors, or a small number of variables per factor. Frontiers Media S.A. 2020-04-09 /pmc/articles/PMC7179684/ /pubmed/32372995 http://dx.doi.org/10.3389/fpsyg.2020.00507 Text en Copyright © 2020 Jung, Seo and Park. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Jung, Sunho Seo, Dong Gi Park, Jungkyu Regularized Exploratory Bifactor Analysis With Small Sample Sizes |
title | Regularized Exploratory Bifactor Analysis With Small Sample Sizes |
title_full | Regularized Exploratory Bifactor Analysis With Small Sample Sizes |
title_fullStr | Regularized Exploratory Bifactor Analysis With Small Sample Sizes |
title_full_unstemmed | Regularized Exploratory Bifactor Analysis With Small Sample Sizes |
title_short | Regularized Exploratory Bifactor Analysis With Small Sample Sizes |
title_sort | regularized exploratory bifactor analysis with small sample sizes |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7179684/ https://www.ncbi.nlm.nih.gov/pubmed/32372995 http://dx.doi.org/10.3389/fpsyg.2020.00507 |
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