Table 4 Results of the tobit regression predicting cyber-victimisation and cyber-bullying
Cyber-victimisation | Cyber-bullying | Depressive symptoms (M1) | Depressive symptoms (M2) | |||||
|---|---|---|---|---|---|---|---|---|
Z | Sig | Z | Sig | Z | Sig | Z | Sig | |
Gender - female | 4.75 | < .001 | 1.02 | .307 | 3.14 | .002 | 2.79 | .005 |
Age | 1.48 | .138 | .67 | .502 | 3.58 | < .001 | 3.31 | .001 |
Country - Australia | 4.46 | < .001 | 4.11 | < .001 | -3.46 | .001 | -4.36 | < .001 |
Trad. bully/victim behaviors | ||||||||
Bullies vs non-involved | 2.50 | .012 | 9.32 | < .001 | 2.47 | .014 | 1.86 | .063 |
Victims vs non-involved | 8.31 | < .001 | 4.79 | < .001 | 9.89 | < .001 | 8.38 | < .001 |
Bully-victims vs non-involved | 8.96 | < .001 | 10.6 | < .001 | 8.89 | < .001 | 5.60 | < .001 |
Bullies vs victims | -3.83 | < .001 | 3.64 | < .001 | -5.18 | < .001 | -4.53 | < .001 |
Bullies vs bully-victims | -5.88 | < .001 | -3.48 | .001 | -6.18 | < .001 | -4.00 | < .001 |
Victims vs bully-victims | -3.02 | .002 | -6.31 | < .001 | -2.33 | .020 | -0.68 | .496 |
Cyber-victimisation | 4.83 | < .001 | ||||||
Cyber-bullying | 1.52 | .127 | ||||||