Multi‐dimensional discriminative factors for Internet addiction among adolescents regarding gender and age

Abstract

Aims:The aim of the present study was to examine the discriminative effects of sociodemographic, individual, family, peers, and school life factors on Internet addiction in Taiwanese adolescents.Methods: A total of 8941 adolescents were recruited and completed the questionnaires. Multidimensional discriminative factors for Internet addiction were examined using chi-squared automatic interaction detection for gender and sex.Results: Depression and low family monitoring were the discriminative factors for Internet addiction in all four gender-and age-specified groups of adolescents. Low connectedness to school, high family conflict, having friends with habitual alcohol drinking, and living in rural areas also had discriminative effects on adolescent Internet addiction in adolescents of different gender and age.Conclusions: Multi-dimensional factors were able to discriminate between those adolescents with and without Internet addiction. It is suggested that parents and health and educational professionals monitor the Internet-using behaviors of adolescents who have the factors discriminating for Internet addiction identified in the present study.

Flow on the Internet: a longitudinal study of Internet addiction symptoms during adolescence

“…Similar to previous cross-sectional studies, males presented with higher IA symptom severity at both 16 and 18 years (although the difference was not statistically significant at 16 years) (Shi et al, 2017;Karacic & Oreskovic, 2017;Ostovar et al, 2016;Anderson et al, 2016;Chen et al, 2015;Choo et al, 2015;Gentile et al, 2011;Haagsma et al, 2013;Hong et al, 2014;Willoughby, 2008;Yu & Shek, 2013;Stavropoulos et al, 2013aStavropoulos et al, , 2013bYen et al, 2009;Shaw & Black, 2008;Johansson & Götestam, 2004).…”

“…Less developed social skills, higher relationship avoidance, and less communicative responses to stress in males (in the context of everyday interaction) may enhance the likelihood of excesive online socialization in males compared with females over time, and thus potentially maintaining higher levels of IA symptoms (Del Giudice, 2011). To our knowledge, although a higher IA risk for males has been shown repeatedly in cross-sectional studies, the present finding is perhaps the first to clarify the nature of IA gender differences longitudinally (Shi et al, 2017;Karacic & Oreskovic, 2017;Ostovar et al, 2016;Anderson et al, 2016;Chen et al, 2015;Choo et al, 2015;Gentile et al, 2011;Haagsma et al, 2013;Hong et al, 2014;Willoughby, 2008;Yu & Shek, 2013;Stavropoulos et al, 2013aStavropoulos et al, , 2013bYen et al, 2009;Shaw & Black, 2008;Johansson & Götestam, 2004). …”

“…Nevertheless, age related effects on IA behaviors during adolescence have been predominantly examined by cross-sectional studies, with some indicating no significant effects of age (Poli & Agrimi, 2012), others suggesting that older adolescents present with higher IA risk (Karacic & Oreskovic, 2017), and the majority of findings concluding that IA behaviors tend to decrease during adolescence (Yen, Ko, Yen, Chang & Cheng, 2009;Bakken, Wenzel, Götestam, Johansson & ØREN, 2009). In that line, and given the significance of the age of onset/ initiation of addictive behaviors in general for their future prognosis (Beard, Haas, Wickham & Stavropoulos, 2017), the past contradictory findings (Poli & Agrimi, 2012;Karacic & Oreskovic, 2017;Yen et al, 2009;Bakken et al, 2009) and the limitations of applying cross-sectional designs to assess maturation effects (e.g. different time cohorts may present with different growth patterns over the same period of development; Motti-Stefanidi & Masten, 2017); the present study is (to the best of the authors' knowledge) the first that aimed to assess this question by implementing a longitudinal design, whilst specifically investigating the time period between 16 and 18 years.…”

“…More specifically, longitudinal findings have shown that males are more inclined to develop problems with impulse-control and sensation-seeking behaviors than females, and these symptoms have been associated with IA (Kuss et al, 2014;Shulman, Harden, Chein, & Steinberg, 2015). Additionally, cross-sectional studies of Internet use have indicated adolescent males being at higher risk of IA than adolescent females (Shi, Wang, & Zou, 2017;Karacic & Oreskovic, 2017;Ostovar et al, 2016;Anderson et al, 2016;Chen et al, 2015;Choo et al, 2015;Gentile et al, 2011;Haagsma et al, 2013;Hong et al, 2014;Willoughby, 2008;Yu & Shek, 2013;Stavropoulos et al, 2013a;Yen et al, 2009;Shaw & Black, 2008;Johansson & Götestam, 2004). Higher digital skills and preference for more addictive applications (such as gaming) have been suggested to explain these findings (Kuss & Griffiths, 2012).…”

The Problematic Internet Entertainment Use Scale for Adolescents: Prevalence of Problem Internet Use in Spanish High School Students

“…The first is through the score on a specific scale: for example, it is diagnosed in subjects with five of the eight diagnostic criteria on the YDQ 34,40,44,47,52,64,65 ; or on the CIAS, it is diagnosed with a minimum score of 63/64. 35,37,41,48,59 A second approach would be to extract categories using percentiles (P), such as the 75 P 24 or 95 P…”