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Explaining Cyber Deviance among School-Aged Youth

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Abstract

With the increasing access to and ownership of computer-mediated devices among children and youth nowadays, it is important to understand the determinants associated with their participation in deviant activities online. The current study extends prior research by utilizing a South Korean adolescent sample and multivariate analyses in order to explore the theoretical and demographic correlates of multiple types of cyber deviance – media and software piracy, computer hacking, and online harassment. Findings reveal that low self-control and deviant peer association are related to specific forms of cyber deviance. Additionally, this study illustrates that while time spent online engaging activities is associated with youth participation in deviant behaviors online, smartphone ownership did not have an effect on their deviance. Implications for research and policy are discussed.

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Notes

  1. The socio-economic status (SES) of the districts in which schools are situated may not be reflective of the average SES in South Korea. Hence, it must be noted that the current sample may not yield a representative sample of the targeted population. Consent forms and letters describing the purpose and goals of the study were provided to participants and their parents in selected classrooms and their signatures were obtained before initiating the data collection. Prior to administering self-reported questionnaires, head teachers in each classroom were trained and educated about protecting human subjects” rights to privacy and anonymity, voluntary participation, and possibility to withdraw from the current study.

  2. Students were informed that participation in the survey was voluntary and anonymous. If the parents or students did not consent to participate in the study, those subjects were not included. Only with their full consents, the survey was administered to the students. Of the 779 students who participated in the survey, 24% (187/779) were in fifth grade, 21% (163/779) were in sixth grade, and 55% were (429/779) in ninth grade. In terms of age distribution for youth in South Korea, students in fifth grade tend to be 12 years old. Students in sixth grade tend to be 13 years old, and those in ninth grade tend to be 16 years old. The current sample is congruent with this pattern.

  3. In accordance with prior research on measurement of self-control (Grasmick et al. 1993; Tittle et al. 2003), it was reasonable to consider the measure of low self-control to be unidimensional given the sizeable difference in eigenvalues between the first (3.5) and second factor (1.3); hence, these items were collapsed into a single construct of low self-control.

  4. This scale was slightly different from the scales used for other technology measures in order to capture a wide variation of time spent using the Internet for school related activities.

  5. Prior to performing a multivariate regression analysis, multicollinearity and diagnostic tests were performed. The tolerance and variation inflation factor (VIF) diagnostics for multicollinearity were performed to examine if there are any highly intercorrelated independent variables in the regression models. Multicollinearity did not appear to bias the parameter estimates because the independent variables were not strongly correlated with each other (see Table 2). Overall, the results suggested that there is little concern regarding multicollinearity among independent variables under study.

  6. First, the Spearman”s correlation coefficient is a non-parametric measure of correlation that is especially useful when using skewed measures because they take into account the similarity of rank ordering (Hauke and Kossowski 2011). All of the dependent measures were highly skewed with excess zeros (see Table 1). Secondly, the Spearman’s Rho does not require a normal distribution and is useful for using ordinal ranked data (Faherty 2007). Because there is no strict assumption made concerning the population from which a sample is drawn, Spearman”s correlation coefficients are applicable to non-normal, skewed distribution (Doane and Seward 2011). Thus, it provided a more adequate correlational analysis for the current data. A Pearson’s correlation matrix was also conducted, but the levels of significance as well as the actual coefficients were very similar to those for the Spearman’s correlation.

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    Byung H. Lee

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Lee, B.H. Explaining Cyber Deviance among School-Aged Youth. Child Ind Res 11, 563–584 (2018). https://doi.org/10.1007/s12187-017-9450-2

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