scholarly journals The Relationship Between Development Trajectories of Resilience and Social Support Systems Among Early Adolescents: Applications of Latent Class Growth Analysis and Multiple-Group Growth Mixture Modeling

2018 ◽  
Vol 39 (5) ◽  
pp. 1-16 ◽  
Author(s):  
Hayoung Kim ◽  
Yu Jin Lee ◽  
Yoonsun Han
Keyword(s):  
Latent Class ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Latent Class Growth Analysis ◽  
Growth Mixture ◽  
Multiple Group ◽  
Development Trajectories ◽  
Social Support Systems ◽  
The Relationship ◽  
Latent Class Growth

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.

Latent-Growth Modeling and Latent Class Growth (Mixture) Modeling

2013 ◽  
Author(s):  
Mary Rose Mamey ◽  
Trynke Hoekstra ◽  
Celestina Barbosa-Leiker ◽  
Sterling M. McPherson ◽  
John M. Roll
Keyword(s):  
Latent Class ◽  
Growth Modeling ◽  
Growth Mixture Modeling ◽  
Latent Growth Modeling ◽  
Mixture Modeling ◽  
Latent Growth ◽  
Growth Mixture ◽  
Latent Class Growth

scholarly journals Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups

2009 ◽  
Vol 33 (6) ◽  
pp. 565-576 ◽  
Author(s):  
Nilam Ram ◽  
Kevin J. Grimm
Keyword(s):  
Stress Response ◽  
Growth Curve ◽  
Growth Modeling ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Longitudinal Change ◽  
Growth Curve Model ◽  
Growth Mixture ◽  
Multiple Group ◽  
Curve Model

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

scholarly journals Development of a mixture model allowing for smoothing functions of longitudinal trajectories

2020 ◽  
pp. 096228022096601
Author(s):  
Ming Ding ◽  
Jorge E. Chavarro ◽  
Garrett M. Fitzmaurice
Keyword(s):  
Mixture Model ◽  
Growth Analysis ◽  
Latent Class ◽  
Parametric Modeling ◽  
Latent Class Growth Analysis ◽  
Growth Mixture Model ◽  
Longitudinal Trajectories ◽  
Growth Mixture ◽  
Smoothing Functions ◽  
Latent Class Growth

In the health and social sciences, two types of mixture models have been widely used by researchers to identify participants within a population with heterogeneous longitudinal trajectories: latent class growth analysis and the growth mixture model. Both methods parametrically model trajectories of individuals, and capture latent trajectory classes, using an expectation–maximization algorithm. However, parametric modeling of trajectories using polynomial functions or monotonic spline functions results in limited flexibility for modeling trajectories; as a result, group membership may not be classified accurately due to model misspecification. In this paper, we propose a smoothing mixture model allowing for smoothing functions of trajectories using a modified algorithm in the M step. Specifically, participants are reassigned to only one group for which the estimated trajectory is the most similar to the observed one; trajectories are fitted using generalized additive mixed models with smoothing functions of time within each of the resulting subsamples. The smoothing mixture model is straightforward to implement using the recently released “ gamm4” package (version 0.2–6) in R 3.5.0. It can incorporate time-varying covariates and be applied to longitudinal data with any exponential family distribution, e.g., normal, Bernoulli, and Poisson. Simulation results show favorable performance of the smoothing mixture model, when compared to latent class growth analysis and growth mixture model, in recovering highly flexible trajectories. The proposed method is illustrated by its application to body mass index data on individuals followed from adolescence to young adulthood and its relationship with incidence of cardiometabolic disease.

scholarly journals Investigating Approaches to Estimating Covariate Effects in Growth Mixture Modeling: A Simulation Study

2016 ◽  
Vol 77 (5) ◽  
pp. 766-791 ◽  
Author(s):  
Ming Li ◽  
Jeffrey R. Harring
Keyword(s):  
Latent Class ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Modeling Framework ◽  
Growth Mixture ◽  
Class Separation ◽  
Covariate Effects ◽  
One Step ◽  
The One ◽  
Class Variable

Researchers continue to be interested in efficient, accurate methods of estimating coefficients of covariates in mixture modeling. Including covariates related to the latent class analysis not only may improve the ability of the mixture model to clearly differentiate between subjects but also makes interpretation of latent group membership more meaningful. Very few studies have been conducted that compare the performance of various approaches to estimating covariate effects in mixture modeling, and fewer yet have considered more complicated models such as growth mixture models where the latent class variable is more difficult to identify. A Monte Carlo simulation was conducted to investigate the performance of four estimation approaches: (1) the conventional three-step approach, (2) the one-step maximum likelihood (ML) approach, (3) the pseudo class (PC) approach, and (4) the three-step ML approach in terms of their ability to recover covariate effects in the logistic regression class membership model within a growth mixture modeling framework. Results showed that when class separation was large, the one-step ML approach and the three-step ML approach displayed much less biased covariate effect estimates than either the conventional three-step approach or the PC approach. When class separation was poor, estimation of the relation between the dichotomous covariate and latent class variable was severely affected when the new three-step ML approach was used.

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.

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