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Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study
Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera (2019) recently proposed a mixture modeling approach that examines the presence of multiple la...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952509/ https://www.ncbi.nlm.nih.gov/pubmed/33716885 http://dx.doi.org/10.3389/fpsyg.2021.618647 |
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author | Marcoulides, Katerina M. Trinchera, Laura |
author_facet | Marcoulides, Katerina M. Trinchera, Laura |
author_sort | Marcoulides, Katerina M. |
collection | PubMed |
description | Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera (2019) recently proposed a mixture modeling approach that examines the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The purpose of this article was to conduct a simulation study that examines the performance of this new approach for determining the number of classes in growth mixture models. The performance of the approach to correctly identify the number of classes is examined under a variety of longitudinal data design conditions. The findings demonstrated that the new approach was a very dependable indicator of classes across all the design conditions considered. |
format | Online Article Text |
id | pubmed-7952509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79525092021-03-13 Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study Marcoulides, Katerina M. Trinchera, Laura Front Psychol Psychology Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera (2019) recently proposed a mixture modeling approach that examines the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The purpose of this article was to conduct a simulation study that examines the performance of this new approach for determining the number of classes in growth mixture models. The performance of the approach to correctly identify the number of classes is examined under a variety of longitudinal data design conditions. The findings demonstrated that the new approach was a very dependable indicator of classes across all the design conditions considered. Frontiers Media S.A. 2021-02-26 /pmc/articles/PMC7952509/ /pubmed/33716885 http://dx.doi.org/10.3389/fpsyg.2021.618647 Text en Copyright © 2021 Marcoulides and Trinchera. 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 Marcoulides, Katerina M. Trinchera, Laura Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study |
title | Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study |
title_full | Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study |
title_fullStr | Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study |
title_full_unstemmed | Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study |
title_short | Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study |
title_sort | residual-based algorithm for growth mixture modeling: a monte carlo simulation study |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952509/ https://www.ncbi.nlm.nih.gov/pubmed/33716885 http://dx.doi.org/10.3389/fpsyg.2021.618647 |
work_keys_str_mv | AT marcoulideskaterinam residualbasedalgorithmforgrowthmixturemodelingamontecarlosimulationstudy AT trincheralaura residualbasedalgorithmforgrowthmixturemodelingamontecarlosimulationstudy |