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.

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.

scholarly journals Latent Class Growth Analysis and Growth Mixture Modeling using R: A tutorial for two R-packages and a comparison with Mplus.

2020 ◽  
Author(s):  
Klaas J Wardenaar
Keyword(s):  
Latent Class ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Growth Trajectories ◽  
Growth Mixture ◽  
Software Packages ◽  
Model Configuration ◽  
R Packages ◽  
Latent Class Growth ◽  
Growth Analyses

Latent Class Growth Analyses (LCGA) and Growth Mixture Modeling (GMM) analyses are used to explain between-subject heterogeneity in growth on an outcome, by identifying latent classes with different growth trajectories. Dedicated software packages are available to estimate these models, with Mplus (Muthén & Muthén, 2019) being widely used . Although this and other available commercial software packages are of good quality, very flexible and rich in options, they can be costly and fit poorly into the analytical workflow of researchers that increasingly depend on the open-source R-platform. Interestingly, although plenty of R-packages to conduct mixture analyses are available, there is little documentation on how to conduct LCGA/GMM in R. Therefore, the current paper aims to provide applied researchers with a tutorial and coding examples for conducting LCGA and GMM in R. Furthermore, it will be evaluated how results obtained with R and the modeling approaches (e.g., default settings, model configuration) of the used R-packages compare to each other and to Mplus.

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.

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.

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