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 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.

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