## Results
### Main Analyses  
The first regression model assessed predictors of cyberbullying victimization. Age, cyberbullying perpetration, verbal aggression, and sex (dummy coded: 0 = male, 1 = female to approximate a continuous variable) were included as predictors through the enter method with cyberbullying victimization as the criterion variable. To verify that the as  
Figure 1: Frequency graph of verbal aggression.  
Figure 3: Frequency graph of cyberbullying victimization.  
Figure 2: Frequency graph of cyberbullying perpetration.  
sumptions of linearity were met, a scatterplot was created to identify outliers and to verify the linear relationship among variables. The assumption of linearity was not violated. The independence assumption was not violated, Durbin-Watson statistic = 1.97. To assess homoscedasticity, a scatterplot of the residuals was estimated. There is no obvious pattern, nor cone shape to the data. Additionally, correlations were examined (see Table 2 for Pearson correlations among variables), and collinearity diagnostics indicate an absence of multicollinearity, VIF = 1.0 for all predictors. Finally, Q-Q plots show normally distributed residuals and verify that the assumption of normality was not violated. The overall model was statistically significant, F (4414) = 13.28, \(p<0.001\), R\({}^{2}\) = 0.12, adjusted R\({}^{2}\) = 0.112, meaning the overall model accounted for approximately 11% of the variance in cyberbullying victimization. Sex \(\beta\) = 0.51, \(p<0.001\), sr\({}^{2}\) = 0.18, cyberbullying perpetration \(\beta\) = 0.34, \(p<0.001\), sr\({}^{2}\) = 0.22, and verbal aggression \(\beta\) = 0.23, \(p<0.05\), sr\({}^{2}\) = 0.14 were statistically significant predictors of cyberbullying victimization. Results from an independent samples \(t\)-test indicate a statistically significant difference in sex for victimization, \(t(423)=-3.37\), \(p<0.001\), with females reporting higher levels of victimization (\(M\) = 0.80, \(SD\) = 1.58) than males (\(M\) = 0.35, \(SD\) = 1.07).  
A second regression model estimated which variables predicted cyberbullying perpetration. Again, age, cyberbullying victimization, verbal aggression, and sex (dummy coded: 0 = male, 1 = female, to approximate a continuous variable) were included as predictors through the enter method with cyberbullying perpetration as the criterion variable. Once again, assumptions of linear regression were assessed. A scatterplot was created to check for outliers and verify that the variables do indeed have a linear relationship with one another. The assumption of linearity was not violated. Next, the interdependence assumption was checked, and the Durbin-Watson statistic indicates that this assumption was also not violated (D-W = 2.04). A scatterplot of residuals was created to verify that the data are not conical in shape, and collinearity diagnostics (VIF = 1.0) confirm that the assumption of homoscedasticity was not violated. Finally, Q-Q plots reveal that the residuals are normally distributed which means that the assumption of normality was not violated. The overall model was statistically significant, F (4414) = 12.69, \(p<0.001\), R\({}^{2}\) = 0.11, adjusted R\({}^{2}\) = 0.10, meaning the overall model accounted for approximately 10% of the variance in cyberbullying perpetration. Age \(\beta\) = 0.11, \(p<0.05\), sr\({}^{2}\) = 0.12, cyberbullying victimization \(\beta\) = 0.16, \(p<0.001\), sr\({}^{2}\) = 0.22, and verbal aggression \(\beta\) = 0.17, \(p<0.01\), sr\({}^{2}\) = 0.14 were statistically significant predictors of cyberbullying perpetration.