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Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation
Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797821/ https://www.ncbi.nlm.nih.gov/pubmed/31681066 http://dx.doi.org/10.3389/fpsyg.2019.02215 |
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author | Jung, Kwanghee Lee, Jaehoon Gupta, Vibhuti Cho, Gyeongcheol |
author_facet | Jung, Kwanghee Lee, Jaehoon Gupta, Vibhuti Cho, Gyeongcheol |
author_sort | Jung, Kwanghee |
collection | PubMed |
description | Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach—partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research. |
format | Online Article Text |
id | pubmed-6797821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67978212019-11-01 Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation Jung, Kwanghee Lee, Jaehoon Gupta, Vibhuti Cho, Gyeongcheol Front Psychol Psychology Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach—partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research. Frontiers Media S.A. 2019-10-11 /pmc/articles/PMC6797821/ /pubmed/31681066 http://dx.doi.org/10.3389/fpsyg.2019.02215 Text en Copyright © 2019 Jung, Lee, Gupta and Cho. 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, Kwanghee Lee, Jaehoon Gupta, Vibhuti Cho, Gyeongcheol Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation |
title | Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation |
title_full | Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation |
title_fullStr | Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation |
title_full_unstemmed | Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation |
title_short | Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation |
title_sort | comparison of bootstrap confidence interval methods for gsca using a monte carlo simulation |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797821/ https://www.ncbi.nlm.nih.gov/pubmed/31681066 http://dx.doi.org/10.3389/fpsyg.2019.02215 |
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