Predictors of internet addiction among high school students: A structural equation modelling

In this study, it was aimed to test the internet addiction model based on the cognitive-behavioral approach with structural equation model with together determined that the prevalence of internet addiction among high school students and whether internet addiction were significantly different according to socio-demographic variables. In this model, the effect of internet usage time, psychopathology, and situational cues on internet addiction of high school students are examined through maladaptive cognitions. In this research, which is a descriptive method based on relational screening model, the multistage sampling method is used. The sample of the study consisted of 2197 students selected from 34 high schools in the 2017-2018 academic year in İzmir province. The ages of the students who participated in the study were between the ages of 14-19 (x ̅ = 16.03, SD = 1.06), 26.7% (n = 586) were 9th, 24.5% (n = 539) were 10th, 23.5% (n = 516) were 11th, and 25.3% (n = 556) were 12th grade. In this study Internet Addiction Scale, Online Cognition Scale, Brief Symptom Inventory and Situational Cues Assessment Questionnaire and Personal Information Form were used. In order to determine the frequency of internet addiction in high school students, frequency and percentage values were calculated. Independent samples t-test and one-way ANOVA analyzes were performed to determine whether there was a significant difference in internet addiction mean scores in terms of socio-demographic variables. In order to test the direct and indirect effects of the Internet Addiction Model Based on The Cognitive-Behavioral Approach, path analysis was applied to determine the compatibility of the data obtained with the model with the Structural Equation Model. As a result of the research, it was found that the prevelance of internet addiction in high school students was % 10.2. Moreover, internet addiction mean scores were significantly differed in terms of internet usage time, internet activities and internet access tools, while no significant differences were found according to sex and age. However, internet addiction is directly predicted by situational cues and internet usage time; psychopathology is predicted by partial mediation of maladaptive cognition as well as direct effect and the model tested in the study has been determined to have adequate fit values. With this model, % 76 of the internet addiction variance in high school internet is explained. Shortly, internet addiction in high school students has been explained in a significant way with the internet addiction model based on the cognitive-behavioral approach and the factors that have an impact of this problem have been revealed in this research. The impact of multiple factors on internet addiction and the role of relationships between these factors may be a guide for further research. It is recommended that the factors involved in the model should be addressed when preparing internet addiction prevention and intervention programs in the field.