Cyberbullying perpetration is defined as an aggressive, intentional act carried out by a group or individual, using electronic forms of contact, repeatedly and over time against a victim who cannot easily defend themself (Smith et al., 2008; Wright & Wachs, 2023). Cyberbullying perpetration is associated with a series of negative consequences, such as aggressive behaviors (Teng et al., 2020), substance abuse (Chan & Chui, 2017), psychosocial problems, and even suicidality (H.-F. Hu et al., 2019). As such, numerous studies have aimed to clarify the factors influencing cyberbullying perpetration. Scholars have suggested that depression is major predictor of cyberbullying perpetration and that high depression levels are associated with increased cyberbullying perpetration (Albikawi, 2023; Bitar et al., 2023; Lee et al., 2023); however, most employed a cross-sectional research design (Yang et al., 2013). Although several longitudinal studies have investigated the developmental effects of depression, the results have been inconsistent with those of the cross-sectional studies; therefore, the association of depression with cyberbullying perpetration remains unclear. For example, one meta-analysis suggested that depression did not predict cyberbullying perpetration over time, whereas cyberbullying perpetration did predict depression over time (Camerini et al., 2020). To clarify these inconsistent results, I examined the reciprocal association of depression with cyberbullying perpetration among middle school students by using a random-intercept cross-lagged panel model.

Studies have revealed that depression is associated with interpersonal relationship problems (Ding et al., 2020), low social support (Lundsberg et al., 2020), and emotional problems (Pisinger & Tolstrup, 2021); thus, individuals with depression are susceptible to experiencing feelings of frustration (Jibeen, 2017). Frustration–aggression theory suggests that frustration instigates various types of response, one of which is aggression (Pastore, 1952). According to this theory, the high frustration level of those with depression may lead them to engage in bullying both online and offline (Zhang et al., 2020). Moreover, the general aggression model suggests that individuals with emotional problems (e.g., depression) have more hostile cognition, which increases their tendency to engage in aggressive behaviors (Anderson & Bushman, 2002), including cyberbullying perpetration. Although cross-sectional studies have indicated that depression is a major predictor of cyberbullying perpetration (Camerini et al., 2020), few longitudinal studies have verified these results. Therefore, I further examined the effect of depression on cyberbullying perpetration with a longitudinal design.

 

Wright and Wachs (2019) reported that cyberbullying perpetration may increase people’s tendency to experience depression, which suggests that depression is not only an antecedent variable but also an outcome variable. Aggressive behaviors tend to lead to interpersonal relationship problems, such as low levels of peer acceptance (Wang et al., 2019), teacher acceptance (Kabiri et al., 2020), and parental acceptance (Chen, 2015); when individuals with aggressive behavior face these problems, their perceived social support decreases, which increases their level of depression (Bauman et al., 2012). Because aggressive behaviors have been found to be positively associated with cyberbullying perpetration (Teng et al., 2020), I argued that cyberbullying perpetration would also be a major predictor of depression. Additionally, studies have indicated that individuals experience numerous negative emotions (e.g., anger) when engaging in aggressive behaviors; thus, aggressive behaviors increase individuals’ later negative emotions (Kovácsová et al., 2016). Depression is a negative emotion; thus, when individuals’ level of negative emotions increases, their level of depression also tends to be enhanced (Radloff, 1977). The preceding literature review suggests the association between depression and cyberbullying perpetration may be bidirectional; the present study aimed to verify this. My hypothesis was that depression and cyberbullying perpetration would positively predict each other across time.

I collected data from students at a large middle school in China. All participants were recruited by cluster sampling and took part in the research on a voluntary basis. Four measurements were conducted between 2019 and 2023, each separated by 1 year (T1, T2, T3, and T4). The number of students who completed all four measurements was 460 (215 boys, 245 girls; M_age = 14.91 years, SD = 0.77).

A research team comprising doctoral and graduate students traveled from a different city to the school to conduct the measurements. The student monitors of each class maintained classroom discipline while the research team administered the questionnaire. At the end of the fourth measurement, all students were briefed regarding the purpose of the research. Moreover, they were thanked for their participation with small gifts (valued at RMB 1/USD 0.12) during each measurement. I obtained their parents’ written informed consent.

I used a Chinese version (Lei et al., 2011) of the Center for Epidemiologic Studies Depression Scale to measure students’ depression (Radloff, 1977). The scale consists of 20 items, with responses made on a 4-point Likert scale ranging from 0 (rarely or none of the time) to 3 (most or all of the time). A sample item is “I worry about things that usually don’t bother me.” Cronbach’s alpha values were .88, .91, .93, and .92 at the four measurement points.

 

The Cyberbullying Perpetration Scale was used to measure students’ cyberbullying perpetration (Ybarra et al., 2007). I translated the items into Chinese, conducted an item analysis, and tested the reliability and validity of the resulting translations. This measure consists of three items rated on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). A sample item is “I have made rude or mean comments to someone online.” Cronbach’s alpha values were .81, .87, .88, and .92 at the four measurement points.

The data analysis entailed three steps. First, I used SPSS 21.0 to conduct a correlation analysis of the studied variables, an independent samples t test to identify gender differences in the studied variables across time, and a repeated measures analysis of variance to test whether the studied variables differed across time. Second, I used Mplus 7.0 to test the random-intercept cross-lagged panel model.

 

To evaluate each model I calculated χ², df, comparative fit index (CFI), Tucker–Lewis index (TLI), root-mean-square error of approximation (RMSEA), standardized root-mean-square residual (SRMR), and Akaike information criterion (AIC) statistics. CFI and TLI values larger than .90 suggest an adequate model fit, and RMSEA and SRMR values under 1.00 indicate the model fit is acceptable (L. Hu & Bentler, 1999). 

I conducted a Pearson correlation analysis to test the association between depression and cyberbullying perpetration across time (see Table 1). The results indicated that the associations between T1, T2, T3, and T4 scores for depression ranged from .21 to .54; and the associations between T1, T2, T3, and T4 scores for cyberbullying perpetration ranged from .14 to .31. At T1, T2, and T3, depression and cyberbullying perpetration were positively associated with each other across time, and T4 depression was positively associated with T4 cyberbullying perpetration. The association between depression and cyberbullying perpetration was in the expected direction.

The random-intercept cross-lagged panel model (model indices: χ² = 72.90, df = 15, CFI = .89, TLI = .79, RMSEA = .09, SRMR = .05, AIC = 22312.07) suggested that depression positively predicted cyberbullying perpetration across time and T1 cyberbullying perpetration positively predicted T2 depression (see Table 2). Moreover, the random intercept factors of depression and cyberbullying perpetration were not significantly associated (r = .16, p > .05), indicating that the initial level of depression was not associated with initial level of cyberbullying perpetration.

I found that depression positively predicted cyberbullying perpetration across time, which is consistent with the findings of Camerini et al. (2020). This may be because depressed individuals tend to have more attentional bias toward negative information, which leads them to experience more hostile cognition and anger that eventually leads to more aggressive behaviors (Holas et al., 2020). The online environment also provides individuals with diverse types of information (Sun et al., 2015); if they pay greater attention to negative information, then they may also engage in more cyberbullying perpetration. Moreover, people with depression are more likely to perceive ambiguous stimuli as threatening, leading them to engage in more aggressive behaviors to protect themselves (Barnicot et al., 2014), and this pattern may also be applicable to cyberbullying perpetration. For example, internet rumors may be perceived as threats by individuals with depression, leading them to engage in cyberbullying as a form of counterattack.

 

Notably, the present results reveal that individuals’ cyberbullying perpetration increased their depression across time. Cyberbullying perpetration is an antisocial behavior (i.e., behavior inconsistent with social norms), and an increased level of depression appears to be the result of cyberbullying perpetration (Ho et al., 2017). For example, cyberbullying perpetration has a negative influence on individuals’ interpersonal relationships (Wang et al., 2019), and individuals with poor interpersonal relationships may perceive a lack of social support, which could increase their level of depression (Bauman et al., 2012; Kabiri et al., 2020). Additionally, the general aggression model suggests that, in a circular manner, individuals’ aggressive behaviors are caused by their mental state (cognition, emotion, and arousal), and, further, when these aggressive behaviors occur, they affect the individual’s mental state (Anderson & Bushman, 2002). Similarly, depression is a negative emotion that may be influenced by individuals’ cyberbullying perpetration; this result is consistent with the general aggression model.

I identified a bidirectional association between depression and cyberbullying perpetration among middle school students. Thus, cyberbullying perpetration interventions should be aimed at mitigating depression and depression interventions should be aimed at reducing cyberbullying perpetration. Additionally, interventions should be implemented by families and teachers to address both depression and cyberbullying perpetration, which can break this chain of bidirectional association in various locations.

Two main limitations to this study should be noted. First, my sample comprised only Chinese middle school students, limiting the possibility of generalizing the results to other countries and age groups. Therefore, future studies could verify the current results with participants from different countries and age groups. Second, self-reported questionnaires were used to measure all the studied variables, which may have introduced common method bias into the later analysis. Multiple assessment methods used in future studies may reduce the effect of common method bias.

This study extends knowledge of the association of depression with cyberbullying perpetration among middle school students. I found that depression and cyberbullying perpetration predicted each other across time. These results serve as a reminder that reducing depression in middle school students would likely have strong positive effects on decreasing cyberbullying perpetration, while reducing cyberbullying perpetration would also have strong positive effects on decreasing depression.

Albikawi, Z. F. (2023). Anxiety, depression, self-esteem, Internet addiction and predictors of cyberbullying and cybervictimization among female nursing university students: A cross sectional study. International Journal of Environmental Research and Public Health, 20(5), Article 4293. https://doi.org/10.3390/ijerph20054293

 

Anderson, C. A., & Bushman, B. J. (2002). Human aggression. Annual Review of Psychology, 53, 27–51.

 

Barnicot, K., Wampold, B., & Priebe, S. (2014). The effect of core clinician interpersonal behaviours on depression. Journal of Affective Disorders, 167, 112–117. https://doi.org/10.1016/j.jad.2014.05.064

 

Bauman, E. M., Haaga, D. A. F., Kaltman, S., & Dutton, M. A. (2012). Measuring social support in battered women: Factor structure of the Interpersonal Support Evaluation List (ISEL). Violence Against Women, 18(1), 30–43.

 

Bitar, Z., Elias, M.-B., Malaeb, D., Hallit, S., & Obeid, S. (2023). Is cyberbullying perpetration associated with anxiety, depression and suicidal ideation among Lebanese adolescents? Results from a cross-sectional study. BMC Psychology, 11(1), Article 53. https://doi.org/10.1186/s40359-023-01091-9

 

Camerini, A.-L., Marciano, L., Carrara, A., & Schulz, P. J. (2020). Cyberbullying perpetration and victimization among children and adolescents: A systematic review of longitudinal studies. Telematics and Informatics, 49, Article 101362. https://doi.org/10.1016/j.tele.2020.101362

 

Chan, H. C. O., & Chui, W. H. (2017). The influence of low self-control on violent and nonviolent delinquencies: A study of male adolescents from two Chinese societies. The Journal of Forensic Psychiatry & Psychology, 28(5), 599–619.

 

Chen, F. F. (2015). The reliability and validity of the Chinese version of the revised-positive version of Rosenberg Self-Esteem Scale [In Chinese]. Advances in Psychology, 5(9), 531–535. https://doi.org/10.12677/AP.2015.59068

 

Ding, W., Meza, J., Lin, X., He, T., Chen, H., Wang, Y., & Qin, S. Z. (2020). Oppositional defiant disorder symptoms and children’s feelings of happiness and depression: Mediating roles of interpersonal relationships. Child Indicators Research, 13(1), 215–235. https://doi.org/10.1007/s12187-019-09685-9

 

Ho, S. S., Chen, L., & Ng, A. P. Y. (2017). Comparing cyberbullying perpetration on social media between primary and secondary school students. Computers and Education, 109, 74–84. https://doi.org/10.1016/j.compedu.2017.02.004

 

Holas, P., Krejtz, I., Wisiecka, K., Rusanowska, M., & Nezlek, J. B. (2020). Modification of attentional bias to emotional faces following mindfulness-based cognitive therapy in people with a current depression. Mindfulness, 11(6), 1413–1423. https://doi.org/10.1007/s12671-020-01353-2

 

Hu, H.-F., Liu, T.-L., Hsiao, R. C., Ni, H.-C., Liang, S. H.-Y., Lin, C.-F., Chan, H.-L., Hsieh, Y.-H., Wang, L.-J., Lee, M.-J., Chou, W.-J., & Yen, C.-F. (2019). Cyberbullying victimization and perpetration in adolescents with high‑functioning autism spectrum disorder: Correlations with depression, anxiety, and suicidality. Journal of Autism and Developmental Disorders, 49(10), 4170–4180. https://doi.org/10.1007/s10803-019-04060-7

 

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

 

Jibeen, T. (2017). Unconditional self acceptance and self esteem in relation to frustration intolerance beliefs and psychological distress. Journal of Rational-Emotive & Cognitive-Behavior Therapy, 35(2), 207–221.

 

Kabiri, S., Shadmanfaat, S. M. S. S., Choi, J., & Yun, I. (2020). The impact of life domains on cyberbullying perpetration in Iran: A partial test of Agnew’s general theory of crime. Journal of Criminal Justice, 66, Article 101633. https://doi.org/10.1016/j.jcrimjus.2019.101633

 

Kovácsová, N., Lajunen, T., & Rošková, E. (2016). Aggression on the road: Relationships between dysfunctional impulsivity, forgiveness, negative emotions, and aggressive driving. Transportation Research Part F, 42(2), 286–298. https://doi.org/10.1016/j.trf.2016.02.010

 

Lee, M. H. L., Kaur, M., Shaker, V., Yee, A., Sham, R., & Siau, C. S. (2023). Cyberbullying, social media addiction and associations with depression, anxiety, and stress among medical students in Malaysia. International Journal of Environmental Research and Public Health, 20(4), Article 3136. https://doi.org/10.3390/ijerph20043136

 

Lei, Z. H., Xu, R., Deng, S. B., & Yi, L. (2011). Reliability and validity of the Chinese version of the Epidemiologic Studies of Depression Scale among Chinese school students [In Chinese]. Chinese Journal of Mental Health, 25, 136–140.

 

Lundsberg, L. S., Cutler, A. S., Stanwood, N. L., Yonkers, K. A., & Gariepy, A. M. (2020). Association of pregnancy contexts with depression and low social support in early pregnancy. Perspectives on Sexual and Reproductive Health, 52(3), 161–170. https://doi.org/10.1363/psrh.12155

 

Pastore, N. (1952). The role of arbitrariness in the frustration-aggression hypothesis. The Journal of Abnormal and Social Psychology, 47(3), 728–731. https://doi.org/10.1037/h0060884

 

Pisinger, V., & Tolstrup, J. S. (2021). Are emotional symptoms and depression among young people with parental alcohol problems modified by socioeconomic position? European Child & Adolescent Psychiatry, 147, 1–9. https://doi.org/10.1007/s00787-020-01716-z

 

Radloff, L. S. (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. https://doi.org/10.1177/014662167700100306

 

Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 49(4), 376–385. https://doi.org/10.1111/j.1469-7610.2007.01846.x

 

Sun, F., Cheng, S., Ni, T., & Jin, X. (2015). Research of negative public opinion monitoring based on Sina microblogs: The first of a research series of negative internet public opinion against the government [In Chinese]. Journal of Intelligence, 85, 85–89.  

 

Teng, Z., Nie, Q., Zhu, Z., & Guo, C. (2020). Violent video game exposure and (cyber)bullying perpetration among Chinese youth: The moderating role of trait aggression and moral identity. Computers in Human Behavior, 104(3), Article 106193. https://doi.org/10.1016/j.chb.2019.106193

 

Wang, P., Wang, X., & Lei, L. (2019). Gender differences between student-student relationship and cyberbullying perpetration: An evolutionary perspective. Journal of Interpersonal Violence, 36(19–20), 9187–9207.

 

Wright, M. F., & Wachs, S. (2019). Does peer rejection moderate the associations among cyberbullying victimization, depression, and anxiety among adolescents with autism spectrum disorder? Children, 6(3), Article 41.

 

Wright, M. F., & Wachs, S. (2023). Cyberbullying involvement and depression among elementary school, middle school, high school, and university students: The role of social support and gender. International Journal of Environmental Research and Public Health, 20(4), Article 2835. https://doi.org/10.3390/ijerph20042835

 

Yang, S.-J., Stewart, R., Kim, J.-M., Kim, S.-W., Shin, I.-S., Dewey, M. E., Maskey, S., & Yoon, J.-S. (2013). Differences in predictors of traditional and cyber-bullying: A 2-year longitudinal study in Korean school children. European Child & Adolescent Psychiatry, 22(5), 309–318. https://doi.org/10.1007/s00787-012-0374-6

 

Ybarra, M. L., Diener-West, M., & Leaf, P. J. (2007). Examining the overlap in internet harassment and school bullying: Implications for school intervention. Journal of Adolescent Health, 41(6), S42–S50.

 

Zhang, D., Huebner, E. S., & Tian, L. (2020). Longitudinal associations among neuroticism, depression, and cyberbullying in early adolescents. Computers in Human Behavior, 112, Article 106475.

Albikawi, Z. F. (2023). Anxiety, depression, self-esteem, Internet addiction and predictors of cyberbullying and cybervictimization among female nursing university students: A cross sectional study. International Journal of Environmental Research and Public Health, 20(5), Article 4293. https://doi.org/10.3390/ijerph20054293

 

Anderson, C. A., & Bushman, B. J. (2002). Human aggression. Annual Review of Psychology, 53, 27–51.

 

Barnicot, K., Wampold, B., & Priebe, S. (2014). The effect of core clinician interpersonal behaviours on depression. Journal of Affective Disorders, 167, 112–117. https://doi.org/10.1016/j.jad.2014.05.064

 

Bauman, E. M., Haaga, D. A. F., Kaltman, S., & Dutton, M. A. (2012). Measuring social support in battered women: Factor structure of the Interpersonal Support Evaluation List (ISEL). Violence Against Women, 18(1), 30–43.

 

Bitar, Z., Elias, M.-B., Malaeb, D., Hallit, S., & Obeid, S. (2023). Is cyberbullying perpetration associated with anxiety, depression and suicidal ideation among Lebanese adolescents? Results from a cross-sectional study. BMC Psychology, 11(1), Article 53. https://doi.org/10.1186/s40359-023-01091-9

 

Camerini, A.-L., Marciano, L., Carrara, A., & Schulz, P. J. (2020). Cyberbullying perpetration and victimization among children and adolescents: A systematic review of longitudinal studies. Telematics and Informatics, 49, Article 101362. https://doi.org/10.1016/j.tele.2020.101362

 

Chan, H. C. O., & Chui, W. H. (2017). The influence of low self-control on violent and nonviolent delinquencies: A study of male adolescents from two Chinese societies. The Journal of Forensic Psychiatry & Psychology, 28(5), 599–619.

 

Chen, F. F. (2015). The reliability and validity of the Chinese version of the revised-positive version of Rosenberg Self-Esteem Scale [In Chinese]. Advances in Psychology, 5(9), 531–535. https://doi.org/10.12677/AP.2015.59068

 

Ding, W., Meza, J., Lin, X., He, T., Chen, H., Wang, Y., & Qin, S. Z. (2020). Oppositional defiant disorder symptoms and children’s feelings of happiness and depression: Mediating roles of interpersonal relationships. Child Indicators Research, 13(1), 215–235. https://doi.org/10.1007/s12187-019-09685-9

 

Ho, S. S., Chen, L., & Ng, A. P. Y. (2017). Comparing cyberbullying perpetration on social media between primary and secondary school students. Computers and Education, 109, 74–84. https://doi.org/10.1016/j.compedu.2017.02.004

 

Holas, P., Krejtz, I., Wisiecka, K., Rusanowska, M., & Nezlek, J. B. (2020). Modification of attentional bias to emotional faces following mindfulness-based cognitive therapy in people with a current depression. Mindfulness, 11(6), 1413–1423. https://doi.org/10.1007/s12671-020-01353-2

 

Hu, H.-F., Liu, T.-L., Hsiao, R. C., Ni, H.-C., Liang, S. H.-Y., Lin, C.-F., Chan, H.-L., Hsieh, Y.-H., Wang, L.-J., Lee, M.-J., Chou, W.-J., & Yen, C.-F. (2019). Cyberbullying victimization and perpetration in adolescents with high‑functioning autism spectrum disorder: Correlations with depression, anxiety, and suicidality. Journal of Autism and Developmental Disorders, 49(10), 4170–4180. https://doi.org/10.1007/s10803-019-04060-7

 

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

 

Jibeen, T. (2017). Unconditional self acceptance and self esteem in relation to frustration intolerance beliefs and psychological distress. Journal of Rational-Emotive & Cognitive-Behavior Therapy, 35(2), 207–221.

 

Kabiri, S., Shadmanfaat, S. M. S. S., Choi, J., & Yun, I. (2020). The impact of life domains on cyberbullying perpetration in Iran: A partial test of Agnew’s general theory of crime. Journal of Criminal Justice, 66, Article 101633. https://doi.org/10.1016/j.jcrimjus.2019.101633

 

Kovácsová, N., Lajunen, T., & Rošková, E. (2016). Aggression on the road: Relationships between dysfunctional impulsivity, forgiveness, negative emotions, and aggressive driving. Transportation Research Part F, 42(2), 286–298. https://doi.org/10.1016/j.trf.2016.02.010

 

Lee, M. H. L., Kaur, M., Shaker, V., Yee, A., Sham, R., & Siau, C. S. (2023). Cyberbullying, social media addiction and associations with depression, anxiety, and stress among medical students in Malaysia. International Journal of Environmental Research and Public Health, 20(4), Article 3136. https://doi.org/10.3390/ijerph20043136

 

Lei, Z. H., Xu, R., Deng, S. B., & Yi, L. (2011). Reliability and validity of the Chinese version of the Epidemiologic Studies of Depression Scale among Chinese school students [In Chinese]. Chinese Journal of Mental Health, 25, 136–140.

 

Lundsberg, L. S., Cutler, A. S., Stanwood, N. L., Yonkers, K. A., & Gariepy, A. M. (2020). Association of pregnancy contexts with depression and low social support in early pregnancy. Perspectives on Sexual and Reproductive Health, 52(3), 161–170. https://doi.org/10.1363/psrh.12155

 

Pastore, N. (1952). The role of arbitrariness in the frustration-aggression hypothesis. The Journal of Abnormal and Social Psychology, 47(3), 728–731. https://doi.org/10.1037/h0060884

 

Pisinger, V., & Tolstrup, J. S. (2021). Are emotional symptoms and depression among young people with parental alcohol problems modified by socioeconomic position? European Child & Adolescent Psychiatry, 147, 1–9. https://doi.org/10.1007/s00787-020-01716-z

 

Radloff, L. S. (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. https://doi.org/10.1177/014662167700100306

 

Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 49(4), 376–385. https://doi.org/10.1111/j.1469-7610.2007.01846.x

 

Sun, F., Cheng, S., Ni, T., & Jin, X. (2015). Research of negative public opinion monitoring based on Sina microblogs: The first of a research series of negative internet public opinion against the government [In Chinese]. Journal of Intelligence, 85, 85–89.  

 

Teng, Z., Nie, Q., Zhu, Z., & Guo, C. (2020). Violent video game exposure and (cyber)bullying perpetration among Chinese youth: The moderating role of trait aggression and moral identity. Computers in Human Behavior, 104(3), Article 106193. https://doi.org/10.1016/j.chb.2019.106193

 

Wang, P., Wang, X., & Lei, L. (2019). Gender differences between student-student relationship and cyberbullying perpetration: An evolutionary perspective. Journal of Interpersonal Violence, 36(19–20), 9187–9207.

 

Wright, M. F., & Wachs, S. (2019). Does peer rejection moderate the associations among cyberbullying victimization, depression, and anxiety among adolescents with autism spectrum disorder? Children, 6(3), Article 41.

 

Wright, M. F., & Wachs, S. (2023). Cyberbullying involvement and depression among elementary school, middle school, high school, and university students: The role of social support and gender. International Journal of Environmental Research and Public Health, 20(4), Article 2835. https://doi.org/10.3390/ijerph20042835

 

Yang, S.-J., Stewart, R., Kim, J.-M., Kim, S.-W., Shin, I.-S., Dewey, M. E., Maskey, S., & Yoon, J.-S. (2013). Differences in predictors of traditional and cyber-bullying: A 2-year longitudinal study in Korean school children. European Child & Adolescent Psychiatry, 22(5), 309–318. https://doi.org/10.1007/s00787-012-0374-6

 

Ybarra, M. L., Diener-West, M., & Leaf, P. J. (2007). Examining the overlap in internet harassment and school bullying: Implications for school intervention. Journal of Adolescent Health, 41(6), S42–S50.

 

Zhang, D., Huebner, E. S., & Tian, L. (2020). Longitudinal associations among neuroticism, depression, and cyberbullying in early adolescents. Computers in Human Behavior, 112, Article 106475.