scholarly journals Transitional Life Events and Trajectories of Cigarette and Alcohol Use During Emerging Adulthood: Latent Class Analysis and Growth Mixture Modeling

2013 ◽  
Vol 74 (5) ◽  
pp. 727-735 ◽  
Author(s):  
Jimi Huh ◽  
Zhaoqing Huang ◽  
Yue Liao ◽  
Maryann Pentz ◽  
Chih-Ping Chou
2021 ◽  
pp. 088626052199912
Author(s):  
Valdemir Ferreira-Junior ◽  
Juliana Y. Valente ◽  
Zila M. Sanchez

Although many studies addressed bullying occurrence and its associations, they often use individual variables constructed from few items that probably are inadequate to evaluate bullying severity and type. We aimed to identify involvement patterns in bullying victimization and perpetration, and its association with alcohol use, school performance, and sociodemographic variables. Baseline assessment of a randomized controlled trial were used and a latent class analysis was conducted to identify bullying patterns among 1,742 fifth-grade and 2,316 seventh-grade students from 30 public schools in São Paulo, Brazil. Data were collected using an anonymous self-reported, audio-guided questionnaire completed by the participants on smartphones. Multinomial logistic regressions were performed to verify how covariant variables affected bullying latent classes. Both grades presented the same four latent classes: low bullying, moderate bullying victimization, high bullying victimization, and high bullying victimization and perpetration. Alcohol use was associated with all bullying classes in both grades, with odds ratio up to 5.36 (95% CI 3.05; 10.38) among fifth graders from the high bullying victimization and perpetration class. Poor school performance was also strongly associated with this class (aOR = 10.12, 95%CI = 4.19; 24.41). Black/brown 5th graders were 3.35 times more likely to fit into the high bullying victimization class (95% CI 1.34; 8.37). Lack of evidence for association of sociodemographic variables and bullying latent class among seventh-grade students was found. Bullying and alcohol use are highly harmful behaviors that must be prevented. However, prevention programs should consider how racial and gender issues are influencing the way students experience violence.


2020 ◽  
Author(s):  
Klaas J Wardenaar

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.


2019 ◽  
Vol 79 (6) ◽  
pp. 1156-1183 ◽  
Author(s):  
Myungho Shin ◽  
Unkyung No ◽  
Sehee Hong

The present study aims to compare the robustness under various conditions of latent class analysis mixture modeling approaches that deal with auxiliary distal outcomes. Monte Carlo simulations were employed to test the performance of four approaches recommended by previous simulation studies: maximum likelihood (ML) assuming homoskedasticity (ML_E), ML assuming heteroskedasticity (ML_U), BCH, and LTB. For all investigated simulation conditions, the BCH approach yielded the most unbiased estimates of class-specific distal outcome means. This study has implications for researchers looking to apply recommended latent class analysis mixture modeling approaches in that nonnormality, which has been not fully considered in previous studies, was taken into account to address the distributional form of distal outcomes.


2021 ◽  
Vol 112 ◽  
pp. 106640
Author(s):  
Annah K. Bender ◽  
Jacquelyn L. Meyers ◽  
Stacey Subbie-Saenz di Viteri ◽  
Marc Schuckit ◽  
Grace Chan ◽  
...  

2021 ◽  
Author(s):  
Giovanni Aresi ◽  
Angela Sorgente ◽  
Michael J. Cleveland ◽  
Elena Marta

Introduction: Two not mutually exclusive theories have been proposed to explain the effects of the COVID-19 pandemic on alcohol use: The Availability hypothesis contends that reduced opportunities to drink due to the closure of outlets and consumption sites should lead to decreases in alcohol use, whereas the Stress and Coping hypothesis argues that those exposed to stressful situations may increase drinking. Aims: This study aimed to test such hypotheses by describing pre/during-COVID-19-pandemic changes in patterns of alcohol use among the Italian young adults (18–34 years).Methods: This study involves the secondary analysis of data collected in 2015 and 2020 from nationally representative samples of Italian young adults. Latent class analysis (LCA) was used to identify common patterns of alcohol use.Results: Five classes were found: current non-drinker class (CND), weekend risky (WRD) and weekend non-risky drinkers (WnRD), daily non-risky (DnRD) and daily risky drinkers (DRD). Results indicate gender-specific changes in the prevalence of the five drinker profiles from 2015 to 2020.Conclusions: In support to the Availability hypothesis, increases in abstaining women and men were observed, however among men there were also increases in the prevalence of patterns characterized by risky drinking and related harm (Stress and Coping hypothesis).


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