Abstract
This study examined the predictors and distal outcome in relation to the frequency of online activities and investigated the presence of prototypical trajectories following different patterns of general Internet use over 5 years. The data set consisted of a nationally representative sample of 2,840 fourth graders (Mage = 9.86 years) in South Korea at baseline. Analyses revealed rank-order stability in general Internet use with four latent classes: high stable (5.8%), high quadratic (20.3%), moderate stable (32.7%), and low stable (41.2%). Youth with higher levels of perceived parental monitoring knowledge, friendship closeness, and depressed mood at baseline were more likely to belong to the high stable class, while girls were more likely to be in the high quadratic or moderate stable classes relative to the low stable class. The high stable class had the greatest odds of reporting alcohol use at grade eight, whereas the low stable class had the lowest odds. Statement of contribution What is already known on this subject? Internet usage increases as children progress through to early adolescence and then levels off Longitudinal data have shown that adolescents’ greater Internet use is predictive of their higher levels of drinking What does this study add? To explore different developmental pathways of Internet use in relation to its antecedents and distal outcome Early adolescents who spend more time on the Internet have a greater chance of starting to drink The interindividual rank-order stability of general Internet usage exists at least before late childhood.
| Original language | English |
|---|---|
| Pages (from-to) | 483-497 |
| Number of pages | 15 |
| Journal | British Journal of Developmental Psychology |
| Volume | 35 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Nov 2017 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- alcohol use
- general Internet use
- general growth mixture modelling
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