European Journal of Psychotraumatology
ISSN: 2000-8198 (Print) 2000-8066 (Online) Journal homepage: http://www.tandfonline.com/loi/zept20
Latent trajectory studies: the basics, how to interpret the results, and what to report Rens van de Schoot To cite this article: Rens van de Schoot (2015) Latent trajectory studies: the basics, how to interpret the results, and what to report, European Journal of Psychotraumatology, 6:1, 27514, DOI: 10.3402/ejpt.v6.27514 To link to this article: http://dx.doi.org/10.3402/ejpt.v6.27514
© 2015 Rens van de Schoot
Published online: 25 Jan 2017.
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Date: 27 February 2017, At: 17:07
EUROPEAN JOURNAL OF
PSYCHOTRAUMATOLOGY æ ESTIMATING PTSD TRAJECTORIES
Latent trajectory studies: the basics, how to interpret the results, and what to report Rens van de Schoot1,2* 1 Department of Methods and Statistics, Utrecht University, Utrecht, The Netherlands; 2Optentia Research Program, Faculty of Humanities, North-West University, Mahikeng, South Africa *Correspondence to: Rens van de Schoot, Email: [email protected]
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 (Muthe´n & Muthe´n, 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 chisquare 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 (Muthe´n & Muthe´n, 19982012) 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. Keywords: Latent Growth Mixture Modeling; latent growth curve analysis; mixture modeling This abstract is part of the Special Issue: Estimating PTSD trajectories - selected abstracts. More abstracts from this issue can be found at http://www.ejpt.net Received: 6 February 2015; Accepted: 6 February 2015; Published: 2 March 2015
Acknowledgement The author was supported by a grant from the Netherlands Organization for Scientific Research: NWO-VENI-451-11-008.
References Depaoli, S., Van de Schoot, R., Van Loey, N., & Sijbrandij, M. (2015). Using Bayesian statistics for modeling PTSD through Latent Growth Mixture Modeling: Implementation and discussion. European Journal of Psychotraumatology, 6, 27516, doi: http://dx.doi.org/10.3402/ejpt.v6.27516 Galatzer-Levy, I. (2015). Applications of Latent Growth Mixture Modeling and allied methods to posttraumatic stress response data. European Journal of Psychotraumatology, 6, 27515, doi: http://dx.doi.org/10.3402/ejpt.v6.27515 Jung, T., & Wickrama, K. A. S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2, 302317. Muthe´n, B., & Muthe´n, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882891. doi: 10.1111/j.15300277.2000.tb02070.x. Muthe´n, L., & Muthe´n, B. (19982012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthe´n & Muthe´n. Van de Schoot, R. (2015). Latent Growth Mixture Models to estimate PTSD trajectories. European Journal of Psychotraumatology, 6, 27503, doi: http://dx.doi.org/10.3402/ejpt.v6.27503 Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18, 450469.
European Journal of Psychotraumatology 2015. # 2015 Rens van de Schoot. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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. Citation: European Journal of Psychotraumatology 2015, 6: 27514 - http://dx.doi.org/10.3402/ejpt.v6.27514
1
(page number not for citation purpose)
ISSN: 2000-8198 (Print) 2000-8066 (Online) Journal homepage: http://www.tandfonline.com/loi/zept20
Latent trajectory studies: the basics, how to interpret the results, and what to report Rens van de Schoot To cite this article: Rens van de Schoot (2015) Latent trajectory studies: the basics, how to interpret the results, and what to report, European Journal of Psychotraumatology, 6:1, 27514, DOI: 10.3402/ejpt.v6.27514 To link to this article: http://dx.doi.org/10.3402/ejpt.v6.27514
© 2015 Rens van de Schoot
Published online: 25 Jan 2017.
Submit your article to this journal
Article views: 5
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=zept20 Download by: [80.82.77.83]
Date: 27 February 2017, At: 17:07
EUROPEAN JOURNAL OF
PSYCHOTRAUMATOLOGY æ ESTIMATING PTSD TRAJECTORIES
Latent trajectory studies: the basics, how to interpret the results, and what to report Rens van de Schoot1,2* 1 Department of Methods and Statistics, Utrecht University, Utrecht, The Netherlands; 2Optentia Research Program, Faculty of Humanities, North-West University, Mahikeng, South Africa *Correspondence to: Rens van de Schoot, Email: [email protected]
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 (Muthe´n & Muthe´n, 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 chisquare 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 (Muthe´n & Muthe´n, 19982012) 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. Keywords: Latent Growth Mixture Modeling; latent growth curve analysis; mixture modeling This abstract is part of the Special Issue: Estimating PTSD trajectories - selected abstracts. More abstracts from this issue can be found at http://www.ejpt.net Received: 6 February 2015; Accepted: 6 February 2015; Published: 2 March 2015
Acknowledgement The author was supported by a grant from the Netherlands Organization for Scientific Research: NWO-VENI-451-11-008.
References Depaoli, S., Van de Schoot, R., Van Loey, N., & Sijbrandij, M. (2015). Using Bayesian statistics for modeling PTSD through Latent Growth Mixture Modeling: Implementation and discussion. European Journal of Psychotraumatology, 6, 27516, doi: http://dx.doi.org/10.3402/ejpt.v6.27516 Galatzer-Levy, I. (2015). Applications of Latent Growth Mixture Modeling and allied methods to posttraumatic stress response data. European Journal of Psychotraumatology, 6, 27515, doi: http://dx.doi.org/10.3402/ejpt.v6.27515 Jung, T., & Wickrama, K. A. S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2, 302317. Muthe´n, B., & Muthe´n, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882891. doi: 10.1111/j.15300277.2000.tb02070.x. Muthe´n, L., & Muthe´n, B. (19982012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthe´n & Muthe´n. Van de Schoot, R. (2015). Latent Growth Mixture Models to estimate PTSD trajectories. European Journal of Psychotraumatology, 6, 27503, doi: http://dx.doi.org/10.3402/ejpt.v6.27503 Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18, 450469.
European Journal of Psychotraumatology 2015. # 2015 Rens van de Schoot. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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. Citation: European Journal of Psychotraumatology 2015, 6: 27514 - http://dx.doi.org/10.3402/ejpt.v6.27514
1
(page number not for citation purpose)
