Internet Addiction in Hong Kong Adolescents: A Three-Year Longitudinal Study

2013 ◽  
Vol 26 (3) ◽  
pp. S10-S17 ◽  
Author(s):  
Lu Yu ◽  
Daniel Tan Lei Shek
Keyword(s):  
Hong Kong ◽  
Longitudinal Study ◽  
Internet Addiction ◽  
Hong Kong Adolescents

scholarly journals Parenting approaches, family functionality, and internet addiction among Hong Kong adolescents

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Cynthia Sau Ting Wu ◽  
Ho Ting Wong ◽  
Kin Fai Yu ◽  
Ka Wing Fok ◽  
Sheung Man Yeung ◽  
...  
Keyword(s):  
Hong Kong ◽  
Internet Addiction ◽  
Hong Kong Adolescents ◽  
Family Functionality ◽  
Parenting Approaches

Self-concept clarity: A longitudinal study of Hong Kong adolescents

2010 ◽  
Vol 48 (3) ◽  
pp. 277-282 ◽  
Author(s):  
Joseph Wu ◽  
David Watkins ◽  
John Hattie
Keyword(s):  
Hong Kong ◽  
Longitudinal Study ◽  
Self Concept ◽  
Hong Kong Adolescents

scholarly journals Internet Addiction Phenomenon in Early Adolescents in Hong Kong

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Daniel T. L. Shek ◽  
Lu Yu
Keyword(s):  
Hong Kong ◽  
Internet Addiction ◽  
Economic Status ◽  
Demographic Variables ◽  
Behavioral Pattern ◽  
Addictive Behaviors ◽  
Demographic Correlates ◽  
Hong Kong Adolescents ◽  
One Year ◽  
Over Time

The present study investigated the prevalence and demographic correlates of Internet addiction in Hong Kong adolescents as well as the change in related behavior at two time points over a one-year interval. Two waves of data were collected from a large sample of students (Wave 1: 3,328 students, age=12.59±0.74years; Wave 2: 3,580 students, age=13.50±0.75years) at 28 secondary schools in Hong Kong. Comparable to findings at Wave 1 (26.4%), 26.7% of the participants met the criterion of Internet addiction at Wave 2 as measured by Young’s 10-item Internet Addiction Test. The behavioral pattern of Internet addiction was basically stable over time. While the predictive effects of demographic variables including age, gender, family economic status, and immigration status were not significant, Internet addictive behaviors at Wave 1 significantly predicted similar behaviors at Wave 2. Students who met the criterion of Internet addiction at Wave 1 were 7.55 times more likely than other students to be classified as Internet addicts at Wave 2. These results suggest that early detection and intervention for Internet addiction should be carried out.

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