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A Latent Profile Analysis of Internet use and Its Association with Psychological Well-Being Outcomes among Hong Kong Chinese Early Adolescents

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  • Cecilia M. S. Ma

    (The Hong Kong Polytechnic University)

Abstract

Nowadays, the Internet has penetrated into every aspect of our daily life. The aims of this study were (a) to examine the patterns of Internet use among Hong Kong early adolescents and (b) to explore whether such patterns differed in terms of the psychological well-being (happiness, loneliness, depression and hopelessness), sensation seeking and perceived social support. A total of 1401 Chinese adolescents participated in the study. Results from the latent profile analysis suggested that there were four latent profiles, and different patterns of the Internet use were associated with the psychological well-being, perceived social support and sensation seeking. The present study sheds light on the importance of considering the nature of online activity and the relationship between Internet use and psychosocial well-being. Implications of the study are discussed.

Suggested Citation

  • Cecilia M. S. Ma, 2018. "A Latent Profile Analysis of Internet use and Its Association with Psychological Well-Being Outcomes among Hong Kong Chinese Early Adolescents," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 13(3), pages 727-743, September.
    • Handle: RePEc:spr:ariqol:v:13:y:2018:i:3:d:10.1007_s11482-017-9555-2
    DOI: 10.1007/s11482-017-9555-2
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    1. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
    2. Daniel Shek & Xiang Li, 2016. "Perceived School Performance, Life Satisfaction, and Hopelessness: A 4-Year Longitudinal Study of Adolescents in Hong Kong," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 126(2), pages 921-934, March.
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