Classification of probable online social networking addiction: A latent profile analysis from a large-scale survey among Chinese adolescents

Li, Jibin; Wu, Anise M. S.; Feng, Li-Fen; Deng, Yang; Liu, Jinghua; Chen, Yuxia; Mai, Jin-Chen; Mo, Phoenix K. H.; Lau, Joseph

doi:10.1556/2006.2020.00047

Cited by 29 publications

(44 citation statements)

References 42 publications

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“…LCA has also been used to identify prototypical configurations or typologies of individuals based on participants' responses to the observed variables. Because all items of an assessment tool are employed to identify the latent classes, therefore, one latent class categorized by LCA includes individuals with the similar symptoms and similar severity of the specific disorder [28]. Compared with variable-centered analyses, such as correlation analysis, LCA focuses on individuals, LCA can take full advantage of the available information provided by the assessment tools and yield a more reasonable classification scheme.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with variable-centered analyses, such as correlation analysis, LCA focuses on individuals, LCA can take full advantage of the available information provided by the assessment tools and yield a more reasonable classification scheme. In some studies, LCA has been employed to establish scientific cut-off points for assessment tools as well, such as online social networking addiction, Bergen social media addiction scale (BSMAS), and the ten-item internet gaming disorder test (IGDT-10) [28][29][30]. Given that when the clinical interviews are absent, LCA is considered as the best method for understanding heterogeneity within diagnostic classes [28,31], an increasing number of studies are conducted by employing this approach.…”

Section: Introductionmentioning

confidence: 99%

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The relationships between negative emotions and latent classes of smartphone addiction

et al. 2021

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The relationships between negative emotions and latent classes of smartphone addiction

et al. 2021

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“…The findings verified the fitness of the six-class model, in which two of the classes were represented by children who experienced heavy Internet use including excessive online gaming. Similarly, in a study, LPA was applied to explore the subgroups of problematic online social networking use among adolescents and the appropriateness of the three-class model was also demonstrated (Li et al, 2020). However, the above-mentioned studies were restricted to one of the subtypes of PIU so that it was not conducive to our clear understanding.…”

Section: Introductionmentioning

confidence: 99%

Internet Behavior Preferences Predict Pathological Internet Use: A Latent Profile Analysis

et al. 2021

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Recent research in the underlying structure of pathological Internet use (PIU) has produced considerable debate among academics, in which a new “person-centered” approach of studying PIU has recently gathered support but produced mixed results. This study used the latent profile analysis (LPA) to estimate the types of PIU in a large sample of college students (n = 1,400, aged 17–25 years). Participants provided information on demographics, PIU, and Internet behavior preferences. The adolescent pathological Internet use (APIU), which served as the basis of LPA, was used for searching subgroups that represent participants with PIU. LPA identified the PIU classes, and regressions identified the psychological predictors of class membership. Participants were classified into pathological users, pathological-tendency users, preferential users, and ordinary Internet users. In comparison with pathological Internet users, lower Internet gaming and communication preference were associated with pathological-tendency users, preferential users, and ordinary Internet users. The distinct types of college students belong to each PIU class, suggesting that individual differences may be incorporated into the prevention efforts.

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“…This suggests that within a particular behavior, different clusters of risk or addiction levels can be distinguished. It was repeatedly shown for shopping addiction, food addiction [143], social networking sites addiction [144], gaming addiction [17], and pornography.…”

Section: Implications For Research and Interventionsmentioning

confidence: 99%

Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis

Atroszko

Charzyńska

2021

IJERPH

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Background: Relatively strong theoretical assumptions and previous studies concerning co-occurring addictive behaviors suggest a subpopulation representing general proclivity to behavioral addictions (BAs), and there are gender-specific subpopulations. This study aimed to compare latent profile analysis (LPA) and latent class analysis (LCA) as the methods of investigating different clusters of BAs in the general student population and among students positively screened for at least one BA. Participants and procedure: Analyses of six BAs (study, shopping, gaming, Facebook, pornography, and food) and their potential antecedents (personality) and consequences (well-being) were conducted on a full sample of Polish undergraduate students (N = 1182) and a subsample (n = 327) of students including individuals fulfilling cutoff for at least one BA. Results: LPA on the subsample mostly replicated the previous four profiles found in the full sample. However, LCA on a full sample did not replicate previous findings using LPA and showed only two classes: those with relatively high probabilities on all BAs and low probabilities. LCA on the subsample conflated profiles identified with LPA and classes found with LCA in the full sample. Conclusions: LCA on dichotomized scores (screened positively vs. negatively) were less effective in identifying clear patterns of interrelationships between BAs based on relatively strong theoretical assumptions and found in previous research. BAs can be investigated on the whole spectrum of behavior, and person-centered analyses might be more useful when they are based on continuous scores. This paper provides more detailed analyses of the four basic clusters of BAs, prevalence, and co-occurrence of particular BAs within and between them, their gender and personality risk factors, relationships to well-being, and their interrelationships as emerging from the results of this and previous studies.

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Classification of probable online social networking addiction: A latent profile analysis from a large-scale survey among Chinese adolescents

Cited by 29 publications

References 42 publications

The relationships between negative emotions and latent classes of smartphone addiction

The relationships between negative emotions and latent classes of smartphone addiction

Internet Behavior Preferences Predict Pathological Internet Use: A Latent Profile Analysis

Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis

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