2022
DOI: 10.1155/2022/4287600
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Predicting Patterns of Problematic Smartphone Use among University Students: A Latent Class Analysis

Abstract: University students are consistently ranked among the highest users of smartphones. As such, recent research has focused on examining the antecedents and consequences of problematic smartphone use among university students. While this work has been instrumental to our understanding of the risk and protective factors of developing problematic smartphone use, it has been largely variable-centered and thus fails to recognize the diversity with which problematic smartphone use is experienced among university stude… Show more

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Cited by 8 publications
(5 citation statements)
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References 63 publications
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“…As a result of the study, three PSU change trajectory types were identified: a high-level group (7.7%), a mid-increasing group (62.5%), and a low-increasing group (29.8%). The finding is compatible with those of Parent et al. (2022) , whom identified three latent groups of PSU, this is a similar result, but at the same time, it is a new result that was not confirmed in previous studies.…”
Section: Discussionsupporting
confidence: 88%
See 2 more Smart Citations
“…As a result of the study, three PSU change trajectory types were identified: a high-level group (7.7%), a mid-increasing group (62.5%), and a low-increasing group (29.8%). The finding is compatible with those of Parent et al. (2022) , whom identified three latent groups of PSU, this is a similar result, but at the same time, it is a new result that was not confirmed in previous studies.…”
Section: Discussionsupporting
confidence: 88%
“…(2022) , whom identified three latent groups of PSU, this is a similar result, but at the same time, it is a new result that was not confirmed in previous studies. In the study by Parent et al. (2022) , three latent classes were identified: the ‘connected class,' the ‘problematic class,' and the ‘distracted class.'…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…To avoid solutions based on local maxima, we used 200 random sets of starting values initially and 50 final stage optimizations. Additionally, each latent class was defined with meaningful clinical interpretability [51]. Posterior probabilities from the model were used to assign each participant to their most likely class [18].…”
Section: Statistical Analysis Identification Of Potential Categoriesmentioning
confidence: 99%
“…The addiction is transformed from one person's physiological characteristics and addictive behavior is compulsive behavior. It begins as a pleasurable activity that progresses to serious demand and compulsive conduct [12]- [14]. The main goal was to show a framework that allowed an individual to input the lifestyle activities as input [15]- [17].…”
Section: Introductionmentioning
confidence: 99%