scholarly journals Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Katerina M. Marcoulides ◽  
Laura Trinchera
Keyword(s):  
Mixture Models ◽  
Simulation Study ◽  
Growth Mixture Modeling ◽  
Individual Case ◽  
Mixture Modeling ◽  
Monte Carlo Simulation Study ◽  
New Approach ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Number Of Classes

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.

Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling

2021 ◽  
Vol 46 (2) ◽  
pp. 179-188
Author(s):  
Constance A Mara ◽  
Adam C Carle
Keyword(s):  
Longitudinal Data ◽  
Mixture Models ◽  
Growth Models ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Individual Variability ◽  
Latent Growth ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Latent Growth Mixture Modeling

Abstract Objective This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models. Methods Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software. Results The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models. Conclusions Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or “classes”) of individuals with shared trajectories that differ from the average trajectory.

scholarly journals development of deviant and delinquent behavior of adolescents

2006 ◽  
Vol 3 (1) ◽  
Author(s):  
Jost Reinecke
Keyword(s):  
Mixture Models ◽  
Latent Class ◽  
Unobserved Heterogeneity ◽  
Growth Mixture Modeling ◽  
Poisson Model ◽  
Continuous Variable ◽  
Individual Growth ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Latent Class Growth

The article presents applications of different growth mixture models considering unobserved heterogeneity within the framework of Mplus (Muthén and Muthén, 2001, 2004). Latent class growth mixture models are discussed under special consideration of count variables which can be incorporated into the mixture models via the Poisson and the zero-inflated Poisson model. Four-wave panel data from a German criminological youth study (Boers et al., 2002) is used for the model analyses. Three classes can be obtained from the data: Adolescents with almost no deviant and delinquent activities, a medium proportion of adolescents with a low increase of delinquency and a small number with a larger growth starting on a higher level. The best model fits are obtained with the zero-inflated Poisson model. Linear growth specifications are almost sufficient. The conditional application of the mixture models includes gender and educational level of the schools as time-independent predictors which are able to explain a large proportion of the latent class distribution. The stepwise procedure from latent class growth analysis to growth mixture modeling is feasible for longitudinal analyses where individual growth trajectories are heterogenous even when the dependent variable under study cannot be treated as a continuous variable.

scholarly journals Latent Class Trajectory and Growth Mixture Models in the Study of Physical Resilience

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 828-829
Author(s):  
Carl Pieper ◽  
Jane Pendergast ◽  
Megan Neely
Keyword(s):  
Mixture Models ◽  
Latent Class ◽  
Real Life ◽  
Individual Characteristics ◽  
Model Specification ◽  
Growth Mixture Models ◽  
Class Enumeration ◽  
Growth Mixture ◽  
Study Population ◽  
Number Of Classes

Abstract After a stressor, individuals may experience different trajectories of function and recovery. One potential explanation for this variation is differing trajectories may be indicators of differing classes or levels of resilience to the stressor. Latent Class Trajectory (LCTA) and Growth Mixture models (GMM) are two similar approaches used to discover the number and types of trajectories in a study population. Class membership may determine the shape and level of recovery, which may be predicted by individual characteristics. In this talk, we present some insights to using these models to successfully identify the number of classes of trajectories, membership of trajectory classes, and the functional form of the trajectory. We will identify methods for deciding class enumeration, indices for assessing fit quality, and, importantly, the importance of proper model specification. Real life and simulated examples will be shown to compare and contrast differences between GMM and LCTA results. Part of a symposium sponsored by Epidemiology of Aging Interest Group.

scholarly journals Sustainable Growth of Social Tourism: A Growth Mixture Modeling Approach Using Heterogeneous Travel Frequency Trajectories

2021 ◽  
Vol 18 (10) ◽  
pp. 5241
Author(s):  
Jaeseok Lee ◽  
Jooa Baek
Keyword(s):  
Latent Variable ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Optimal Number ◽  
Sustainable Growth ◽  
Growth Mixture ◽  
Latent Variable Analysis ◽  
Curve Modeling ◽  
Tourism And Hospitality ◽  
Number Of Classes

As travel activity has gained attention as one of the essential ways of understanding the sustainable growth of social tourism, a growing number of research projects have been conducted to elucidate the relationship between residents’ travel quantity (frequency) and quality (experience) in both macro and micro perspectives. Yet, very little research has highlighted that travel opportunities are not equally available to residents, especially a longitudinal perspective. The current study classified domestic travelers into four distinct classes using four years of longitudinal data from 5054 Korean residents. Latent growth curve modeling (LGCM) and growth mixture modeling (GMM) were employed to find out (1) the optimal number of classes, (2) the longitudinal travel frequency trajectory of each class, and (3) the distinctive demographic and travel characteristics of the four classes. This study provides some practical implications for policymakers when optimizing available resources for sustainable travel opportunities to relevant target sub-populations. Furthermore, detailed step-by-step analytic tutorials are also introduced for the extended application of longitudinal latent variable analysis in the tourism and hospitality fields, providing additional insights for relevant stakeholders.

Longitudinal Analysis of Adolescents’ Deviant and Delinquent Behavior

Methodology ◽  
2006 ◽  
Vol 2 (3) ◽  
pp. 100-112 ◽  
Author(s):  
Jost Reinecke
Keyword(s):  
Mixture Models ◽  
Latent Class ◽  
Unobserved Heterogeneity ◽  
Growth Mixture Modeling ◽  
Poisson Model ◽  
Continuous Variable ◽  
Individual Growth ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Latent Class Growth

This article presents applications of different growth mixture models considering unobserved heterogeneity within the framework of Mplus ( Muthén & Muthén, 2001a , 2001b , 2004 ). Latent class growth mixture models are discussed under special consideration of count variables that can be incorporated into the mixtures via the Poisson and the zero-inflated Poisson model. Fourwave panel data from a German criminological youth study (Boers et al., 2002) is used for the model analyses. Three classes can be obtained from the data: Adolescents with almost no deviant and delinquent activities, a medium proportion of adolescents with a low increase of delinquency, and a small number with a larger growth starting on a higher level. Considering the zero inflation of the data results in better model fits compared to the Poisson model only. Linear growth specifications are almost sufficient. The conditional application of the mixture models includes gender and educational level of the schools as time-independent predictors that are able to explain a large proportion of the latent class distribution. The stepwise procedure from latent class growth analysis to growth mixture modeling is feasible for longitudinal analyses where individual growth trajectories are heterogenous even when the dependent variable under study cannot be treated as a continuous variable.

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