期刊
PLOS ONE
卷 16, 期 3, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0248555
关键词
-
资金
- Scientific research projects of colleges and universities in Inner Mongolia Autonomous Region [NJSY20181]
- Inner Mongolia Normal University graduate students' research innovation fund [CXJJB20004, CXJJB19007]
This study found that negative emotional variables were significantly correlated with smartphone addiction proneness. Based on the scores on the Smartphone Addiction Scale, smartphone users were divided into three latent classes: low risk class, moderate class, and high risk class. Women were more likely to be classified in the high-risk class. The severity of depression and boredom was able to predict the membership of the latent class effectively; while social anxiety failed to do this in the high risk class.
The relationships between negative emotions and smartphone addiction has been tested through the literature. However, most of the studies applied variable-centered approaches. The heterogeneity of smartphone addiction severity has not been examined for the associations with negative emotion variables. The purposes of the present study is to explore the latent classes of smartphone addiction and analyze the relationships between depression, social anxiety and boredom and these subgroups. The Smartphone Addiction Scale-Short Version (SAS-SV) and three negative emotion scales were employed to conduct a survey of 539 college students. Mplus8.3 software was applied to perform the latent class analysis (LCA) based on the smartphone addiction symptom ratings. ANOVA and multinomial logistic regression were used to explore the differences among these latent categories and the associations between these subgroups and negative emotion variables. Results demonstrated that Negative emotional variables were significantly correlated with smartphone addiction proneness. Based on their scores on the Smartphone Addiction Scale, smartphone users were divided into three latent classes: low risk class, moderate class and high risk class. Women were more likely to be classified in the high-risk class. The severity of depression and boredom was able to predict the membership of the latent class effectively; while social anxiety failed to do this in the high risk class.
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