Cyberbullying, or aggression through electronic means towards a victim who cannot easily defend themselves, has become increasingly common in society. Researchers have shown that personality disorders and jealousy in close relationships may increase the likelihood that individuals will use aggression against their peers. However, no known research has examined the relationship between personality disorders, jealousy, and cyberbullying behaviors. The current study addresses this gap by examining associations between borderline personality disorder features, jealousy, and cyberbullying behaviors in adolescents. The sample includes 106 adolescents (53 males) with a mean age of 16.1 years (SD = .49), who completed self-report measures of borderline personality features, jealousy, and cyberbullying. Higher levels of borderline personality disorder features were associated with increased levels of cyberbullying behaviors. Jealousy fully mediated the relationship between borderline personality disorder features and cyberbullying behaviors. Limitations, directions for future research, and implications for society, intervention, and treatment are discussed.
5. Results
5.1. Descriptive statistics
Overall, 54% of participants reported using some form of cyberbullying against another person. Of the different types of cyberbullying, the most common forms were “making mean comments online” (45% reported they had done this at least once) and “spreading rumors or gossiping about someone online” (22% reported they had done this at least once).
A series of t-tests revealed that there were no gender differences for BPD features, t(102) = 1.65, p > .05, jealousy, t(102) = 1.72, p > .05, or cyberbullying, t(102) = .50, p > .05. Table 1 lists the means and standard deviations.
Table 1.
Means and standard deviations by gender.
Main variables Males Females
M SD M SD
Borderline personality features 2.25 .56 2.42 .50
Jealousy 1.76 .64 1.98 .58
Cyberbullying 1.16 .25 1.19 .24
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5.2. Measurement model
In prelude to the central analyses below, a measurement model for the cyberbullying items was first examined using structural equation modeling. The model showed adequate fit, (X2(2) = 1.26, p > .05, CFI = 1.00, RMSEA = .00). All items showed standardized factor loadings above .30.
5.3. Main analyses
A Structural Equation Model was constructed in AMOS (v. 21) with BPD features predicting cyberbullying, first without jealousy (though including all covariates). Overall model fit was acceptable, using some indicators, but not others, X2(14) = 17.05, p = .25, CFI = .88; RMSEA = .05. BPD features significantly predicted higher levels of cyberbullying in adolescence, (β = .36, p < .05). In terms of controls, time spent on the Internet was positively related to cyberbullying at trend level (β = .32, p = .06).
Jealousy was then introduced as a mediator in the model. Model fit was acceptable in this final model, X2(17) = 22.09, p = .18, CFI = .94; RMSEA = .05. Results revealed that BPD features were positively and significantly associated with jealousy (β = .62, p < .001), and jealousy was positively associated with cyberbullying (β = .70, p < .001). The direct path between BPD features and cyberbullying became non-significant when jealousy was added as a mediator in the model (β = −.063, p = .73). In terms of controls, SES was negatively related to BPD features (β = −.21, p < .05), and positively related to jealousy at trend level (β = .15, p = .09). Age was negatively related to cyberbullying, also at trend level (β = −.24, p = .07). Mediation was assessed using 2000 bootstrap resamples and a 95% CI. Bootstrapping is a commonly used technique to examine indirect effects in SEM, and specifically is beneficial when the assumptions of a large data set and normality of data (such in the current dataset) do not hold ( Cheung & Lau, 2008). The bootstrapping analysis revealed that jealousy was a significant and full mediator between BPD features and cyberbullying (β = .30, p < .01, see Fig. 1 for a visual representation).
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Fig. 1.
Associations between borderline personality disorder features and cyberbullying. Note: Figure before the slash refers to the value when jealousy is not included in the model. The figure after the slash refers to the value when jealousy is introduced as a mediator in the model. Correlations and error terms are not shown for exogenous variables. ∗∗∗p < .001; ∗p < .05; +p < .10.
