Time-use for the iGeneration: A person-centered approach

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Abstract

This study used a person-centered approach to identify classes of adolescents characterized by unique patterns of time-use including traditional activities (paid work, homework, television, physical activity) and technological activities (gaming, social networking, Internet) and examined the relation to developmental outcomes (extracurricular activities, sleep, and academic attainment). A sample of 948 mainly Caucasian (56.6%) adolescents (43% male; M age = 15.4 years) from 28 high schools were surveyed. Time-use classes were derived using latent class analysis. Four classes of adolescent time-use were identified: Analoggers, Gamers, Digital All-rounders, and Social Networkers. Classes differentially associated with developmental outcomes, controlling for gender and socioeconomic status. The Gamers (15.7%; active in one domain) were more likely to be males, with the lowest engagement in extracurricular activities and lowest academic attainment when compared to other groups. The Digital All-rounders (24.9%; engagement in multiple domains) obtained the lowest amount of sleep, however, on average, participated in sport, and had an academic self-concept of similar levels to Analoggers (40.3%; highly engaged in traditional activities, less engaged in the technological domain). Social Networkers (19.1%), on average, heavily invested in one technological activity at the expense of other activities, having the poorest outcomes alongside Gamers. Examination of typologies of adolescent time-use can support understanding of technology-related activity patterns and associations with extracurricular activities, sleep and academic attainment. The person-centered approach enables us to disentangle contradictory findings related to adolescent technology use, particularly when comparing those who engage across a range of activities compared to those only engaged in one domain.

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APA

Vernon, L. (2019). Time-use for the iGeneration: A person-centered approach. Human Behavior and Emerging Technologies, 1(2), 91–102. https://doi.org/10.1002/hbe2.146

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