A Latent Transition Analysis of Internet Addiction in Early Adolescents and Its Contributing Factors

Dados Bibliográficos

ID11670835
AutoresXiaojin Hu (Hunan Normal University), Yuling Chen (0000-0002-8674-8356, Hunan Normal University), Zhihang Wang (0000-0002-9961-3346, Hunan Agricultural University), E Scott Huebner (0000-0002-2258-7573, University of South Carolina), Yu Ling (0000-0002-9728-960X, Hunan Normal University, autor correspondente)
Ano2022
Volume43
Fascículo5
Páginas603-631
Data de publicação2022-07-17
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoThe Journal of Early Adolescence (JOURNAL)
Identificadores do periódicoISSN: 0272-4316 • E-ISSN: 1552-5449
EditoraSAGE Publishing (PUBLISHER • US)
DOI10.1177/02724316221116045
OpenAlexW4285731549
IdiomaEN
Citações recebidas11
Referências citadas73

The purpose of the present study was to identify the patterns of Internet addiction in early adolescents and their associated protective factors in order to prevent and/or ameliorate internet addiction as early as possible. A 2-year longitudinal study was conducted among 621 Chinese early adolescents. Latent class and latent transition analyses were used to identify classes of the development of internet addiction. Three latent classes were identified, including a “Non-Internet Addiction Group” (43%), “Low Internet Addiction Group” (43%) and “High Internet Addiction Group” (14%). A Latent Transition Analysis of Adolescents in the Non-Internet Addiction Group and Low Internet Addiction Group demonstrated relative stability in class membership, whereas adolescents in the High Internet Addiction Group tended to change to the Low Internet Addiction Group. Results of multinomial logistic regression and latent transition analyses revealed that low impulsivity, high family care, and high student engagement in school all contributed to membership in the latent classes of internet use and transitions. Implications of these results for prevention and intervention and for future research are discussed

Addiction · Impulsivity · Latent class model · Multinomial logistic regression · Psychiatry · The Internet · World Wide Web · Child Development and Digital Technology · Clinical Psychology · Computer Science · Impact of Technology on Adolescents · Psychology · Social Media and Politics