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Latent trajectory studies: the basics, how to interpret the results, and what to report
BACKGROUND: In statistics, tools have been developed to estimate individual change over time. Also, the existence of latent trajectories, where individuals are captured by trajectories that are unobserved (latent), can be evaluated (Muthén & Muthén, 2000). The method used to evaluate such trajec...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Co-Action Publishing
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4348410/ https://www.ncbi.nlm.nih.gov/pubmed/25735413 http://dx.doi.org/10.3402/ejpt.v6.27514 |
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author | van de Schoot, Rens |
author_facet | van de Schoot, Rens |
author_sort | van de Schoot, Rens |
collection | PubMed |
description | BACKGROUND: In statistics, tools have been developed to estimate individual change over time. Also, the existence of latent trajectories, where individuals are captured by trajectories that are unobserved (latent), can be evaluated (Muthén & Muthén, 2000). The method used to evaluate such trajectories is called Latent Growth Mixture Modeling (LGMM) or Latent Class Growth Modeling (LCGA). The difference between the two models is whether variance within latent classes is allowed for (Jung & Wickrama, 2008). The default approach most often used when estimating such models begins with estimating a single cluster model, where only a single underlying group is presumed. Next, several additional models are estimated with an increasing number of clusters (latent groups or classes). For each of these models, the software is allowed to estimate all parameters without any restrictions. A final model is chosen based on model comparison tools, for example, using the BIC, the bootstrapped chi-square test, or the Lo-Mendell-Rubin test. METHOD: To ease the use of LGMM/LCGA step by step in this symposium (Van de Schoot, 2015) guidelines are presented which can be used for researchers applying the methods to longitudinal data, for example, the development of posttraumatic stress disorder (PTSD) after trauma (Depaoli, van de Schoot, van Loey, & Sijbrandij, 2015; Galatzer-Levy, 2015). The guidelines include how to use the software Mplus (Muthén & Muthén, 1998–2012) to run the set of models needed to answer the research question: how many latent classes exist in the data? The next step described in the guidelines is how to add covariates/predictors to predict class membership using the three-step approach (Vermunt, 2010). Lastly, it described what essentials to report in the paper. CONCLUSIONS: When applying LGMM/LCGA models for the first time, the guidelines presented can be used to guide what models to run and what to report. |
format | Online Article Text |
id | pubmed-4348410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Co-Action Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-43484102015-03-13 Latent trajectory studies: the basics, how to interpret the results, and what to report van de Schoot, Rens Eur J Psychotraumatol Estimating PTSD Trajectories BACKGROUND: In statistics, tools have been developed to estimate individual change over time. Also, the existence of latent trajectories, where individuals are captured by trajectories that are unobserved (latent), can be evaluated (Muthén & Muthén, 2000). The method used to evaluate such trajectories is called Latent Growth Mixture Modeling (LGMM) or Latent Class Growth Modeling (LCGA). The difference between the two models is whether variance within latent classes is allowed for (Jung & Wickrama, 2008). The default approach most often used when estimating such models begins with estimating a single cluster model, where only a single underlying group is presumed. Next, several additional models are estimated with an increasing number of clusters (latent groups or classes). For each of these models, the software is allowed to estimate all parameters without any restrictions. A final model is chosen based on model comparison tools, for example, using the BIC, the bootstrapped chi-square test, or the Lo-Mendell-Rubin test. METHOD: To ease the use of LGMM/LCGA step by step in this symposium (Van de Schoot, 2015) guidelines are presented which can be used for researchers applying the methods to longitudinal data, for example, the development of posttraumatic stress disorder (PTSD) after trauma (Depaoli, van de Schoot, van Loey, & Sijbrandij, 2015; Galatzer-Levy, 2015). The guidelines include how to use the software Mplus (Muthén & Muthén, 1998–2012) to run the set of models needed to answer the research question: how many latent classes exist in the data? The next step described in the guidelines is how to add covariates/predictors to predict class membership using the three-step approach (Vermunt, 2010). Lastly, it described what essentials to report in the paper. CONCLUSIONS: When applying LGMM/LCGA models for the first time, the guidelines presented can be used to guide what models to run and what to report. Co-Action Publishing 2015-03-02 /pmc/articles/PMC4348410/ /pubmed/25735413 http://dx.doi.org/10.3402/ejpt.v6.27514 Text en © 2015 Rens van de Schoot http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, allowing third parties to copy and redistribute the material in any medium or format, and to remix, transform, and build upon the material, for any purpose, even commercially, under the condition that appropriate credit is given, that a link to the license is provided, and that you indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. |
spellingShingle | Estimating PTSD Trajectories van de Schoot, Rens Latent trajectory studies: the basics, how to interpret the results, and what to report |
title | Latent trajectory studies: the basics, how to interpret the results, and what to report |
title_full | Latent trajectory studies: the basics, how to interpret the results, and what to report |
title_fullStr | Latent trajectory studies: the basics, how to interpret the results, and what to report |
title_full_unstemmed | Latent trajectory studies: the basics, how to interpret the results, and what to report |
title_short | Latent trajectory studies: the basics, how to interpret the results, and what to report |
title_sort | latent trajectory studies: the basics, how to interpret the results, and what to report |
topic | Estimating PTSD Trajectories |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4348410/ https://www.ncbi.nlm.nih.gov/pubmed/25735413 http://dx.doi.org/10.3402/ejpt.v6.27514 |
work_keys_str_mv | AT vandeschootrens latenttrajectorystudiesthebasicshowtointerprettheresultsandwhattoreport |