Joint Growth Trajectories of Bullying Perpetration and Victimization Among Korean Adolescents: Estimating a Second-Order Growth Mixture Model–Factor-of-Curves With Low Self-Control and Opportunity Correlates

2019 ◽  
Vol 66 (9) ◽  
pp. 1296-1337
Author(s):  
Sujung Cho ◽  
Jin Ree Lee
Keyword(s):  
Mixture Model ◽  
Second Order ◽  
Self Control ◽  
Growth Trajectories ◽  
Stable Group ◽  
Growth Mixture Model ◽  
Korean Adolescents ◽  
Bullying Perpetration ◽  
Growth Mixture ◽  
Bully Victims

Joint growth trajectories of bullying perpetration and victimization were examined using 5-year panel data (2004–2008) from a sample of 2,844 South Korean adolescents between the ages of 11 and 15 (fourth to eighth grade). The second-order growth mixture model revealed three distinct subgroups: bully-victims to low bully-victims transition (9.9%); moderate bully-victims to victim transition (6.8%); and a limited involvement/stable group (83.3%). Respondents with less self-control who associated with delinquent peers were more likely to be members of both the bully-victims to low bully-victims transition and the moderate bully-victims to victim transition groups, compared with the limited involvement/stable group. Relative to the limited involvement/stable group, adolescents with less self-control were more likely to be members of both transition groups even after controlling for opportunity measures. Delinquent peer associations partially mediated these associations.

scholarly journals 322 - Heterogeneity in dynamic change of cognitive function among Chinese elderly: A growth mixture model

2020 ◽  
Vol 32 (S1) ◽  
pp. 80-80
Author(s):  
Peiyuan Qiu ◽  
Weihong Kuang ◽  
Yan Cai ◽  
Yang Wan
Keyword(s):  
Risk Factors ◽  
Cognitive Function ◽  
Cognitive Decline ◽  
Mixture Model ◽  
Dynamic Change ◽  
Cognitive Change ◽  
Stable Group ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
Fresh Vegetables

Objectives:Our aim is to use the growth mixture model (GMM) to distinguish different trajectories of cognitive change in Chinese geriatric population and identify risk factors for cognitive decline in each subpopulation.Methods:We obtained data from the Chinese Longitudinal Health Longevity Survey, using the Chinese Mini-Mental State Examination (C-MMSE) as a proxy for cognitive function. We applied the GMM to identify heterogeneous subpopulations and potential risk factors.Results:Our sample included 2850 older adults, 1387 (48.7%) male and 1463 (51.3%) female with age range of 62 to 108 (average of 72.3). Using GMM and best fit statistics, we identified two distinct subgroups in respect to their longitudinal cognitive function: cognitively stable (91.4%) group with 0.42 C-MMSE points decline per 3 years, and cognitively declining (8.6%) group with 4.76 C-MMSE points decline per 3 years. Of note, vision impairment and hearing impairment had the highest associations with cognitive decline, with stronger association found in the cognitively declining group than the cognitively stable group. Cognitive activities were protective in both groups. Diabetes was associated with cognitive decline in cognitive declining group. Physical activities, social activities and intake of fresh vegetables, fruits, and fish products were protective in cognitive stable group.Conclusions:Using GMM, we identified heterogeneity in trajectories of cognitive change in Chinese elders. Moreover, we found risk factors specific to each subgroup, which should be considered in future studies.

Shape invariant mixture model for clustering non-linear longitudinal growth trajectories

2018 ◽  
Vol 28 (12) ◽  
pp. 3769-3784
Author(s):  
Zihang Lu ◽  
Wendy Lou
Keyword(s):  
Mixture Model ◽  
Linear Growth ◽  
Simulated Data ◽  
Real Data ◽  
Repeated Measurements ◽  
Growth Trajectories ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
Shape Invariant ◽  
Non Linear

In longitudinal studies, it is often of great interest to cluster individual trajectories based on repeated measurements taken over time. Non-linear growth trajectories are often seen in practice, and the individual data can also be measured sparsely, and at irregular time points, which may complicate the modeling process. Motivated by a study of pregnant women hormone profiles, we proposed a shape invariant growth mixture model for clustering non-linear growth trajectories. Bayesian inference via Monte Carlo Markov Chain was employed to estimate the parameters of interest. We compared our model to the commonly used growth mixture model and functional clustering approach by simulation studies. Results from analyzing the real data and simulated data were presented and discussed.

scholarly journals Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248844
Author(s):  
Hai Nguyen ◽  
Dario Moreno-Agostino ◽  
Kia-Chong Chua ◽  
Silia Vitoratou ◽  
A. Matthew Prina
Keyword(s):  
Mixture Model ◽  
Multinomial Logistic Regression ◽  
Healthy Ageing ◽  
Rapid Decline ◽  
Stable Group ◽  
Growth Mixture Model ◽  
Lifestyle Risk Factors ◽  
Growth Mixture ◽  
Group 2 ◽  
Lifestyle Risk

Objectives In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. Setting and participants Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2–7 of the English Longitudinal Study of Ageing (ELSA). Methods Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors. Results Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a ‘high stable’ group (76% in ATHLOS, 61% in ELSA), 2) a ‘low stable’ group (22% in ATHLOS, 36% in ELSA) and 3) a ‘rapid decline’ group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4–2.1) more likely to be in the ‘rapid decline’ group and 11.7 times (OR = 11.7 95% CI: 10.9–12.6) more likely to be in the ‘low stable’ group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the ‘rapid decline’ and the ‘low stable’ groups (OR = 2.1, 95% CI: 1.2–3.8 and OR = 9.8, 95% CI: 7.5–12.7 respectively). Conclusion Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity.

Factors of Piecewise Growth Mixture Model: Distance and Pattern

2014 ◽  
Vol 46 (9) ◽  
pp. 1400 ◽  
Author(s):  
Yuan LIU ◽  
Fang LUO ◽  
Hongyun LIU
Keyword(s):  
Mixture Model ◽  
Growth Mixture Model ◽  
Growth Mixture

scholarly journals Does Money Matter? A Theory-Driven Growth Mixture Model to Explain Travel-Mode Choice with Experimental Data

Methodology ◽  
2006 ◽  
Vol 2 (3) ◽  
pp. 124-134 ◽  
Author(s):  
Eldad Davidov ◽  
Kajsa Yang-Hansen ◽  
Jan-Eric Gustafsson ◽  
Peter Schmidt ◽  
Sebastian Bamberg
Keyword(s):  
Mixture Model ◽  
Latent Class ◽  
Mode Choice ◽  
Growth Mixture Model ◽  
Travel Mode ◽  
High Importance ◽  
Growth Mixture ◽  
Travel Mode Choice ◽  
Changes Over Time ◽  
Over Time

In the present article we apply a growth mixture model using Mplus via STREAMS to delineate the mechanism underlying travel-mode choice. Three waves of an experimental field study conducted in Frankfurt Main, Germany, are applied for the statistical analysis. Five major questions are addressed: (1) whether the choice of public transport rather than the car changes over time; (2) whether a soft policy intervention to change travel mode choice has any effect on the travel-mode chosen; (3) whether one can identify different groups of people regarding the importance allocated to monetary and time considerations for the decision of which travel mode to use; (4) whether the different subgroups of people have different initial states and rates of change in their travel-model choices; (5) whether sociodemographic variables have an additional effect on the latent class variables and on the changes in travel-mode choice over time. We also found that choice of public transportation in our study is stable over time. Moreover, the intervention has an effect only on one of the classes. We identify four classes of individuals. One class allocates a low importance to both monetary and time considerations, the second allocates high importance to money and low importance to time, the third allocates high importance to both, and the fourth allocates a low importance to money and a high importance to time. We found no difference in the patterns of travel-mode changes over time in the four classes. We also found some additional effects of sociodemographic characteristics on the latent class variables and on behavior in the different classes. The model specification and the empirical findings are discussed in light of the theory of the allocation of time of Gary Becker.

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