Predictors of the risk of addiction to social networks and the Internet
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INTRODUCTION. This study was based on the general objective of identifying factors that predict the risk of becoming addicted to the Internet or social networks. METHOD. A descriptive design has been used for the research, using mean, skewness and kurtosis, with a binomial logistic regression. A sample of 217 university students, all of them first year students of the Faculty of Education Sciences of the University of Granada, was used for the research. In this study the demographic variables of age and gender were considered within the investigated students, it is observed that the students had a mean age of 19.37 years and a median of 18 years. In which we can highlight that the majority gender of the sample is female (66.8%) and the remaining 33.2% is male. On the other hand, the Adolescent Risk of Addiction to Social Networks and the Internet (ERA-RSI) scale was used for data collection. RESULTS. The factors that most accurately predict risk of social networking and Internet addiction in firstyear college students are normalization, personal difficulties, and ego. Loneliness proved to be predictive, but to a lesser degree, and, finally, disinhibition proved to have no predictive influence. DISCUSSION. It was found that the telephone applications that are constantly launched on the Internet have a great influence on the predictors of addiction.
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