scholarly journals Smartphone Addiction, Social Support, and Cybercrime Victimization: A Discrete Survival and Growth Mixture Model

2021 ◽  
Vol 31 (1) ◽  
pp. 59-66
Author(s):  
Juan Herrero ◽  
Andrea Torres ◽  
Pep Vivas ◽  
Alberto Urueña
Keyword(s):  
Social Support ◽  
Mixture Model ◽  
Smartphone Addiction ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
Survival And Growth

Socially Connected but Still Isolated: Smartphone Addiction Decreases Social Support Over Time

2017 ◽  
Vol 37 (1) ◽  
pp. 73-88 ◽  
Author(s):  
Juan Herrero ◽  
Alberto Urueña ◽  
Andrea Torres ◽  
Antonio Hidalgo
Keyword(s):  
Social Support ◽  
Sensation Seeking ◽  
Personal Characteristics ◽  
Well Being ◽  
Smartphone Addiction ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
The Relationship ◽  
Over Time

Recent literature has suggested that smartphone addiction is negatively associated with users’ psychosocial well-being. Much of the research on this subject, however, is of a correlational nature, which has been pointed out as an important limitation that does not allow distinguishing the antecedents of the consequences. In this study, 416 smartphone users were followed for 1 year (three waves separated by 6 months each) to assess the relationship between smartphone addiction and social support. Cross-lagged model results indicated that social support predicts later addiction to the smartphone and that smartphone addiction decreases social support over time. Growth mixture model results indicated that the decrease in social support during the follow-up year was higher for users with greater smartphone addiction at the beginning of the study. Multivariate and univariate analyses of variance indicated that some personal characteristics of users (extroversion, neuroticism, and sensation-seeking) could affect the evolution of social support related to smartphone addiction. In general, these results suggest that the extensive use of a social communication technology such as the smartphone could have the paradoxical effect of diminishing the psychosocial well-being of its users.

scholarly journals Opioid withdrawal, craving, and use during and after outpatient buprenorphine stabilization and taper: A discrete survival and growth mixture model

2015 ◽  
Vol 41 ◽  
pp. 20-28 ◽  
Author(s):  
Thomas F. Northrup ◽  
Angela L. Stotts ◽  
Charles Green ◽  
Jennifer S. Potter ◽  
Elise N. Marino ◽  
...  
Keyword(s):  
Mixture Model ◽  
Opioid Withdrawal ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
Survival And Growth

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.

Estimating a Conditional Growth Mixture Model (GMM)

2021 ◽  
pp. 191-213
Author(s):  
Kandauda A. S. Wickrama ◽  
Tae Kyoung Lee ◽  
Catherine Walker O'Neal ◽  
Frederick Lorenz
Keyword(s):  
Mixture Model ◽  
Growth Mixture Model ◽  
Growth Mixture
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