write a literature review about cyberbullying brainly

• Architecture and Design
• Asian and Pacific Studies
• Business and Economics
• Classical and Ancient Near Eastern Studies
• Computer Sciences
• Cultural Studies
• Engineering
• General Interest
• Geosciences
• Industrial Chemistry
• Islamic and Middle Eastern Studies
• Jewish Studies
• Library and Information Science, Book Studies
• Life Sciences
• Linguistics and Semiotics
• Literary Studies
• Materials Sciences
• Mathematics
• Social Sciences
• Sports and Recreation
• Theology and Religion
• Publish your article
• The role of authors
• Promoting your article
• Abstracting & indexing
• Publishing Ethics
• Why publish with De Gruyter
• How to publish with De Gruyter
• Our book series
• Our subject areas
• Your digital product at De Gruyter
• Contribute to our reference works
• Product information
• Tools & resources
• Product Information
• Promotional Materials
• Orders and Inquiries
• FAQ for Library Suppliers and Book Sellers
• Repository Policy
• Free access policy
• Open Access agreements
• Database portals
• For Authors
• Customer service
• People + Culture
• Journal Management
• How to join us
• Working at De Gruyter
• Mission & Vision
• De Gruyter Foundation
• De Gruyter Ebound
• Our Responsibility
• Partner publishers

Your purchase has been completed. Your documents are now available to view.

Cyberbullying in adolescents: a literature review

Cyberbullying is a universal public health concern that affects adolescents. The growing usage of electronic gadgets and the Internet has been connected to a rise in cyberbullying. The increasing use of the Internet, along with the negative outcomes of cyberbullying on adolescents, has required the study of cyberbullying. In this paper author reviews existing literature on cyberbullying among adolescents. The concept of cyberbullying is explained, including definitions, types of cyberbullying, characteristics or features of victims and cyberbullies, risk factors or causes underlying cyberbullying, and the harmful consequences of cyberbullying to adolescents. Furthermore, examples of programs or intervention to prevent cyberbullying and recommendations for further studies are presented.

Research funding: None declared.

Author contributions: Author has accepted responsibility for the entire content of this manuscript and approved its submission.

Competing interests: Author states no conflict of interest.

Informed consent: Not applicable.

Ethical approval: Not applicable.

1. Ovigli, D, Colombo, P. Information and communication technologies (ICT) in educational research in science museums in Brazil. IJEDICT 2020;16:272–86. Search in Google Scholar

2. Eleuteri, S, Saladino, V, Verrastro, V. Identity, relationships, sexuality, and risky behaviors of adolescents in the context of social media. Sex Relatsh Ther 2017;32:354–65. https://doi.org/10.1080/14681994.2017.1397953 . Search in Google Scholar

3. Saladino, V, Eleuteri, S, Verrastro, V, Petruccelli, F. Perception of cyberbullying in adolescence: a brief evaluation among Italian students. Front Psychol 2020;11:1–7. https://doi.org/10.3389/fpsyg.2020.607225 . Search in Google Scholar PubMed PubMed Central

4. Beale, AV, Hall, KR. Cyberbullying: what school administrators (and parents) can do. Clearing House 2007;81:8–12. https://doi.org/10.3200/tchs.81.1.8-12 . Search in Google Scholar

5. Brody, N, Vangelisti, AL. Bystander intervention in cyberbullying. Commun Monogr 2016;83:94–119. https://doi.org/10.1080/03637751.2015.1044256 . Search in Google Scholar

6. Athanasiou, K, Melegkovits, E, Andrie, EK, Magoulas, C, Tzavara, CK, Richardson, C. Cross-national aspects of cyberbullying victimization among 14–17-year-old adolescents across seven European countries. BMC Publ Health 2018;18:800. https://doi.org/10.1186/s12889-018-5682-4 . Search in Google Scholar PubMed PubMed Central

7. González-Cabrera, J, Tourón, J, Machimbarrena, JM, Gutiérrez-Ortega, M, Álvarez-Bardón, A, Garaigordobil, M. Cyberbullying in gifted students: prevalence and psychological well-being in a Spanish sample. Int J Environ Res Public Health 2019;16:2173. 10.3390/ijerph16122173 Search in Google Scholar PubMed PubMed Central

8. Lee, C, Shin, N. Prevalence of cyberbullying and predictors of cyberbullying perpetration among Korean adolescents. Comput Hum Behav 2017;68:352–8. https://doi.org/10.1016/j.chb.2016.11.047 . Search in Google Scholar

9. Safaria, T. Prevalence and impact of cyberbullying in a sample of Indonesian junior high school students. The Turk Online J Educ Technol 2016;15:82–91. Search in Google Scholar

10. Lee, M-S, Wu, W, Svanström, L, Dalal, K. Cyber bullying prevention: intervention in Taiwan. PLoS One 2013;8:e64031. https://doi.org/10.1371/journal.pone.0064031 . Search in Google Scholar PubMed PubMed Central

11. Whittaker, E, Kowalski, RM. Cyberbullying via social media. J Sch Violence 2015;14:11–29. https://doi.org/10.1080/15388220.2014.949377 . Search in Google Scholar

12. Vaillancourt, T, Faris, R, Mishna, F. Cyberbullying in children and youth: implications for health and clinical practice. Can J Psychiatry 2016;62:368–73. https://doi.org/10.1177/0706743716684791 . Search in Google Scholar PubMed PubMed Central

13. Nixon, C. Current perspectives: the impact of cyberbullying on adolescent health. Adolesc Health Med Ther 2014;5:143–58. https://doi.org/10.2147/ahmt.s36456 . Search in Google Scholar PubMed PubMed Central

14. Heiman, T, Shemesh, D. Cyberbullying experience and gender differences among adolescents in different educational settings. J Learn Disabil 2013;48:146–55. https://doi.org/10.1177/0022219413492855 . Search in Google Scholar PubMed

15. Paris, S, Robert, D. Cyberbullying by adolescents: a preliminary assessment. Educ Forum 2006;70:21–36. 10.1080/00131720508984869 Search in Google Scholar

16. Patchin, JW, Hinduja, S. Bullies move beyond the schoolyard A preliminary look at cyberbullying. Youth Violence Juv Justice 2006;4:148–69. https://doi.org/10.1177/1541204006286288 . Search in Google Scholar

17. Kowalski, RM, Limber, SP. Electronic bullying among middle school students. J Adolesc Health 2007;41(6 Suppl 1):S22–30. https://doi.org/10.1016/j.jadohealth.2007.08.017 . Search in Google Scholar PubMed

18. Raskauskas, J, Stoltz, AD. Involvement in traditional and electronic bullying among adolescents. Dev Psychol 2007;43:564–75. https://doi.org/10.1037/0012-1649.43.3.564 . Search in Google Scholar PubMed

19. Menesini, E, Nocentini, A, Palladino, BE, Frisén, A, Berne, S, Rosario, R. Cyberbullying definition among adolescents: a comparison across six European countries. Cyberpsychol Behav Soc Netw 2012;15:455–63. https://doi.org/10.1089/cyber.2012.0040 . Search in Google Scholar PubMed PubMed Central

20. Dooley, JJ, Pyżalski, J, Cross, D. Cyberbullying versus face-to-face bullying: a theoretical and conceptual review. Z Psychol 2009;217:182–8. https://doi.org/10.1027/0044-3409.217.4.182 . Search in Google Scholar

21. Nocentini, A, Calmaestra, J, Anja, A, Scheithauer, H, Ortega, R, Menesini, E. Cyberbullying: labels, behaviours and definition in three European countries. Aust J Guid Counsell 2010;20:129–42. https://doi.org/10.1375/ajgc.20.2.129 . Search in Google Scholar

22. Aboujaoude, E, Savage, MW, Starcevic, V, Salame, WO. Cyberbullying: review of an old problem gone viral. J Adolesc Health 2015;57:10–8. https://doi.org/10.1016/j.jadohealth.2015.04.011 . Search in Google Scholar PubMed

23. Ferrara, P, Ianniello, F, Villani, A, Corsello, G. Cyberbullying a modern form of bullying: let’s talk about this health and social problem. Ital J Pediatr 2018;44:14. https://doi.org/10.1186/s13052-018-0446-4 . Search in Google Scholar PubMed PubMed Central

24. Smith, PK, Mahdavi, J, Carvalho, M, Fisher, S, Russell, S, Tippett, N. Cyberbullying: its nature and impact in secondary school pupils. JCPP (J Child Psychol Psychiatry) 2008;49:376–85. https://doi.org/10.1111/j.1469-7610.2007.01846.x . Search in Google Scholar PubMed

25. Garett, R, Lord, LR, Young, SD. Associations between social media and cyberbullying: a review of the literature. mHealth 2016;2:46. https://doi.org/10.21037/mhealth.2016.12.01 . Search in Google Scholar PubMed PubMed Central

26. Li, Q. Bullying in the new playground: research into cyberbullying and cyber victimisation. Australas J Educ Technol 2007;23:435–54. 10.14742/ajet.1245 Search in Google Scholar

27. Zeljka, D, Vesna, C, Rajkovača, I, Včev, A, Miskic, D, Miskic, B. Cyberbullying in early adolescence: is there a difference between urban and rural environment? Am J Biomed Sci Res 2019;1:191–6. 10.34297/AJBSR.2019.01.000542 Search in Google Scholar

28. Ybarra, ML, Mitchell, KJ, Wolak, J, Finkelhor, D. Examining characteristics and associated distress related to internet harassment: findings from the second youth internet safety survey. Pediatrics 2006;118:e1169–77. https://doi.org/10.1542/peds.2006-0815 . Search in Google Scholar PubMed

29. Li, Q. Cyberbullying in schools: a research of gender differences. Sch Psychol Int 2006;27:157–70. 10.1177/0143034306064547 Search in Google Scholar

30. Ybarra, ML, Mitchell, KJ. Youth engaging in online harassment: associations with caregiver–child relationships, Internet use, and personal characteristics. J Adolesc 2004;27:319–36. https://doi.org/10.1016/s0140-1971(04)00039-9 . Search in Google Scholar

31. Dhond, V, Richter, S, McKenna, B, editors. Exploratory research to identify the characteristics of cyber victims on social media in New Zealand . Cham: Springer International Publishing; 2019. 10.1007/978-3-030-11395-7_18 Search in Google Scholar

32. Ybarra, ML, Mitchell, KJ. Prevalence and frequency of Internet harassment instigation: implications for adolescent health. J Adolesc Health 2007;41:189–95. https://doi.org/10.1016/j.jadohealth.2007.03.005 . Search in Google Scholar PubMed

33. Zhong, J, Zheng, Y, Huang, X, Mo, D, Gong, J, Li, M. Study of the influencing factors of cyberbullying among Chinese college students incorporated with digital citizenship: from the perspective of individual students. Front Psychol 2021;12:621418. https://doi.org/10.3389/fpsyg.2021.621418 . Search in Google Scholar PubMed PubMed Central

34. Wong-Lo, M, Bullock, LM, Gable, RA. Cyber bullying: practices to face digital aggression. Emot Behav Difficulties 2011;16:317–25. https://doi.org/10.1080/13632752.2011.595098 . Search in Google Scholar

35. Barlett, C, Gentile, D. Attacking others online: the formation of cyberbullying in late adolescence. Psychol Popular Media Culture 2012;1:123–35. https://doi.org/10.1037/a0028113 . Search in Google Scholar

36. Mason. Cyberbullying: a preliminary assessment for school personnel. Psychol Sch 2008;45:323–48. 10.1002/pits.20301 Search in Google Scholar

37. Pratto, F, Stewart, AL, Zeineddine, F. When inequality fails: power, group dominance, and societal change. J Soc Polit Psychol 2013;1:132–60. https://doi.org/10.5964/jspp.v1i1.97 . Search in Google Scholar

38. Koski, JE, Xie, H, Olson, IR. Understanding social hierarchies: the neural and psychological foundations of status perception. Soc Neurosci 2015;10:527–50. https://doi.org/10.1080/17470919.2015.1013223 . Search in Google Scholar PubMed PubMed Central

39. Walker, C, Sockman, B, Koehn, S. An exploratory study of cyberbullying with undergraduate university students. TechTrends 2011;55:31–8. 10.1007/s11528-011-0481-0 Search in Google Scholar

40. Pratto, F, Sidanius, J, Levin, S. Social dominance theory and the dynamics of intergroup relations: Taking stock and looking forward. Eur Rev Soc Psychol 2006;17:271–320. https://doi.org/10.1080/10463280601055772 . Search in Google Scholar

41. Sidanius, J, Pratto, F, Van Laar, C, Levin, S. Social dominance theory: its agenda and method. Polit Psychol 2004;25:845–80. https://doi.org/10.1111/j.1467-9221.2004.00401.x . Search in Google Scholar

42. Closson, LM. Aggressive and prosocial behaviors within early adolescent friendship cliques: what’s status got to do with it? Merrill-Palmer Q 2009;55:406–35. https://doi.org/10.1353/mpq.0.0035 . Search in Google Scholar

43. Salmivalli, C, Kärnä, A, Poskiparta, E. Counteracting bullying in Finland: the KiVa program and its effect on different forms of being bullied. IJBD (Int J Behav Dev) 2011;35:405–11. https://doi.org/10.1177/0165025411407457 . Search in Google Scholar

44. Chisholm, JF. Review of the status of cyberbullying and cyberbullying prevention. J Inf Syst Educ 2014;25:77–87. Search in Google Scholar

45. Bonanno, RA, Hymel, S. Cyber bullying and internalizing difficulties: above and beyond the impact of traditional forms of bullying. J Youth Adolesc 2013;42:685–97. https://doi.org/10.1007/s10964-013-9937-1 . Search in Google Scholar PubMed

46. Wolke, D, Copeland, WE, Angold, A, Costello, EJ. Impact of bullying in childhood on adult health, wealth, crime, and social outcomes. Psychol Sci 2013;24:1958–70. https://doi.org/10.1177/0956797613481608 . Search in Google Scholar PubMed PubMed Central

47. Foody, M, Samara, M, Carlbring, P. A review of cyberbullying and suggestions for online psychological therapy. Internet Interv 2015;2:235–42. https://doi.org/10.1016/j.invent.2015.05.002 . Search in Google Scholar

48. Dredge, R, Gleeson, J, Garcia, X. Cyberbullying in social networking sites: an adolescent victim’s perspective. Comput Hum Behav 2014;36:13–20. https://doi.org/10.1016/j.chb.2014.03.026 . Search in Google Scholar

49. Schenk, AM, Fremouw, WJ. Prevalence, psychological impact, and coping of cyberbully victims among college students. J Sch Violence 2012;11:21–37. https://doi.org/10.1080/15388220.2011.630310 . Search in Google Scholar

50. Burger, C, Bachmann, L. Perpetration and victimization in offline and cyber contexts: a variable- and person-oriented examination of associations and differences regarding domain-specific self-esteem and school adjustment. Int J Environ Res Publ Health 2021;18:10429. https://doi.org/10.3390/ijerph181910429 . Search in Google Scholar PubMed PubMed Central

51. Price, M, Dalgleish, J. Cyberbullying: experiences, impacts and coping strategies as described by Australian young people. Youth Stud Aust 2010;29:51–9. Search in Google Scholar

52. Jang, H, Song, J, Kim, R. Does the offline bully-victimization influence cyberbullying behavior among youths? Application of General Strain Theory. Comput Hum Behav 2014;31:85–93. https://doi.org/10.1016/j.chb.2013.10.007 . Search in Google Scholar

53. Ybarra, ML, West, M, Leaf, PJ. Examining the overlap in internet harassment and school bullying: implications for school intervention. J Adolesc Health 2007;41(6 Suppl 1):S42–50. https://doi.org/10.1016/j.jadohealth.2007.09.004 . Search in Google Scholar PubMed

54. Mishna, F, Kassabri, M, Gadalla, T, Daciuk, J. Risk factors or involvement in cyber bullying: victims, bullies and bully–victims. Child Youth Serv Rev 2012;34:63–70. https://doi.org/10.1016/j.childyouth.2011.08.032 . Search in Google Scholar

55. Wong, DSW, Chan, HC, Cheng, CHK. Cheng. Cyberbullying perpetration and victimization among adolescents in Hong Kong. Child Youth Serv Rev 2014;36:133–40. https://doi.org/10.1016/j.childyouth.2013.11.006 . Search in Google Scholar

56. Bauman, S, Toomey, RB, Walker, JL. Associations among bullying, cyberbullying, and suicide in high school students. J Adolesc 2013;36:341–50. https://doi.org/10.1016/j.adolescence.2012.12.001 . Search in Google Scholar PubMed

57. Fletcher, A, Yau, N, Jones, R, Allen, E, Viner, RM, Bonell, C. Brief report: cyberbullying perpetration and its associations with socio-demographics, aggressive behaviour at school, and mental health outcomes. J Adolesc 2014;37:1393–8. https://doi.org/10.1016/j.adolescence.2014.10.005 . Search in Google Scholar PubMed

58. Kraft, EM, Wang, J. Effectiveness of cyber bullying prevention strategies: a study on students’ perspectives. Int J Cyber Criminol 2009;3:513. Search in Google Scholar

59. Williford, A, Elledge, LC, Boulton, AJ, DePaolis, KJ, Little, TD, Salmivalli, C. Effects of the KiVa antibullying program on cyberbullying and cybervictimization frequency among Finnish youth. J Clin Child Adolesc Psychol 2013;42:820–33. https://doi.org/10.1080/15374416.2013.787623 . Search in Google Scholar PubMed

60. Menesini, E, Zambuto, V, Palladino, BE. 10 - online and school-based programs to prevent cyberbullying among Italian adolescents: what works, why, and under which circumstances. In: Campbell, M, Bauman, S, editors. Reducing cyberbullying in schools . Cambridge: Academic Press; 2018:35–43 pp. 10.1016/B978-0-12-811423-0.00010-9 Search in Google Scholar

61. David, A, Rio, J, Pueyo, A, Calvo, G. Effects of a cooperative learning intervention program on cyberbullying in secondary education: a case study. Qual Rep 2019;24:2426–40. Search in Google Scholar

62. Leung, ANM, Wong, N, Farver, JM. Farver. Testing the effectiveness of an E-course to combat cyberbullying. Cyberpsychol Behav Soc Netw 2019;22:569–77. https://doi.org/10.1089/cyber.2018.0609 . Search in Google Scholar PubMed

63. Barón, J, Buelga, S, Ayllón, E, Ferrer, B, Cava, M. Effects of intervention program Prev@cib on traditional bullying and cyberbullying. Int J Environ Res Publ Health 2019;16:527. 10.3390/ijerph16040527 Search in Google Scholar PubMed PubMed Central

64. Doane, A, Ehlke, S, Kelley, M. Bystanders against cyberbullying: a video program for college students. Int J Bull Prev 2020;2:41–52. https://doi.org/10.1007/s42380-019-00051-5 . Search in Google Scholar

© 2022 Walter de Gruyter GmbH, Berlin/Boston

• X / Twitter

Supplementary Materials

Please login or register with De Gruyter to order this product.

Journal and Issue

Articles in the same issue.

An official website of the United States government, Department of Justice.

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Bullying and Cyberbullying

Bullying involving children and youth has become a topic of national conversation over the past few decades and is a major focus for schools across the United States and internationally (Gladden et al., 2014; Ybarra et al., 2019). Bullying can cause substantial harm to the children and youth who are victimized, to those who engage in bullying behaviors, and to the bystanders who witness bullying (Evans et al., 2018; Gladden et al., 2014; Zych, Farrington, and Ttofi, 2019). To address this problem, numerous antibullying interventions have been developed and implemented (Gaffney, Ttofi, and Farrington, 2019; Polanin et al., 2021). Along with these efforts, there has been a growing field of research on bullying, which strives to understand the causes, effects, and ways of effectively intervening and preventing bullying (National Academies of Sciences, Engineering, and Medicine, 2016).

While multiple definitions of bullying are used in research (Eriksen, 2018; Gladden et al., 2014; Liu and Graves, 2011; Smith et al., 2002; Polanin, 2012; Younan, 2018), bullying is generally considered to be unwanted aggressive behavior(s) by another youth or group of youth (who are not current dating partners or siblings) that involves a power imbalance and is repeated multiple times or is highly likely to be repeated (Gladden et al., 2014). Although attention to bullying has increased noticeably among researchers since the late 1990s, and many studies have been published, bullying research is still considered "underdeveloped and uneven" (National Academies of Sciences, Engineering, and Medicine, 2016, p. 31).

This literature review focuses on bullying that involves children and youth in elementary, middle, and high schools. The review summarizes research related to the scope of bullying in the United States; different types of bullying; theoretical foundations; predictors, risk factors, protective factors, and consequences of bullying; and interventions focused on prevention and/or reduction. Challenges and gaps in the literature are also identified.   

Bullying Definitions

Bullying is a type of aggressive behavior (Salmivalli, 2010), which can be physical (e.g., hitting, punching), verbal (e.g., name-calling, teasing), or psychological/relational (e.g., rumors, social exclusion). The research literature on aggressive peer relations also includes analyses of peer victimization and harassment, terms that often are used interchangeably with bullying; however, researchers have pointed out subtle differences between them (Eisenberg and Aalsma, 2005; Hong and Espelage, 2012; Jackman et al., 2020; Salmon et al., 2018). Bullying may occur in many places or contexts but mostly occurs in school or online. Typically, in the literature, individuals involved with bullying are classified as bullies, victims, bully-victims (those who are both bullies and victims), or bystanders.

The concept of bullying (Olweus 1991, 2010) was introduced and first defined as comprising three criteria: 1) intention (aggressiveness), 2) repetition, and 3) imbalance of power. Since then, research and interventions addressing bullying have expanded, with considerable definitional variation. To address the lack of uniformity, the Centers for Disease Control and Prevention (CDC) and U.S. Department of Education (DOE) together developed the following definition in a joint publication:

Bullying is any unwanted aggressive behavior(s) by another youth or group of youths who are not siblings or current dating partners that involves an observed or perceived power imbalance and is repeated multiple times or is highly likely to be repeated. Bullying may inflict harm or distress on the targeted youth including physical, psychological, social, or educational harm (Gladden et al., 2014, p. 7).

The current CDC/DOE definition applies to bullying that occurs between peers. It does not apply to abuse perpetrated by adults. It also excludes family violence and violence that occurs within the context of an intimate or dating relationship. These different forms of violence (e.g., child maltreatment, sibling violence, teen dating violence , intimate partner violence, elder maltreatment) may also include aggression that is physical, verbal, sexual, or relational. However, the context and nature of the relationship between the victim and the perpetrator in which these acts occur is different from that of peer violence. It is important to distinguish bullying from other types of aggression, abuse, and violence since programs that are developed to address one type of aggression may not be effective for other types (Ferguson et al., 2007; Gladden et al., 2014; Taub, 2001; Van Schoiack-Edstrom, Frey, and Beland, 2002).

The CDC and DOE also provided descriptions of the modes, types, and contexts of bullying (Gladden et al., 2014). Modes refer to whether the bullying is direct and indirect; types of bullying include the way bullying is done (e.g., physical, verbal, relational) and/or the intent of the bulling (identity based); and contexts refer to where the bullying occurs, such as in school or online.

Modes of Bullying

Bullying can either be direct or indirect. Direct bullying refers to aggressive behavior(s) that occur in the presence of the targeted youth. Examples of direct bullying include face-to-face attacks, such as pushing or name calling, or directing harmful written communication at a targeted youth. Indirect bullying refers to aggressive behavior(s) not directly communicated to the targeted youth, such as spreading harmful rumors about them (Gladden et al., 2014).

Types of Bullying

As described in the research literature, the types of bullying often vary by source. There are a variety of ways studies categorize bullying types. Most are related to how the bullying is done (e.g., physical, verbal) or the intent of the bullying (e.g., targeting LGBTQ youth or overweight youth).

The CDC and DOE’s joint publication identified four types of bullying: 1) physical, 2) verbal, 3) relational, and 4) damaging property.  Other publications that examine bullying by its intent (or source of power dynamic) identify subtypes such as identity-based bullying, sexual bullying, and weight-based bullying (e.g., Armitage, 2021; Garnett et al., 2014, Puhl et al., 2016). Because studies use different definitions and categorization, the different types of bullying often overlap with each other.

Physical bullying refers to the use of physical force by a child or youth against a targeted peer. Examples include behaviors such as pushing, tripping, hitting, kicking, punching, and spitting (Gladden et al., 2014).

Verbal bullying includes both oral and written communication against the targeted child or youth. Examples include taunting, humiliation, name calling, threatening or offensive written notes or hand gestures, sarcasm, inappropriate sexual comments, or verbal threats (Bauman, 2015; Gladden et al., 2014).

Relational bullying is a type of relational aggression, which can be direct or indirect. Relational aggression is the purposeful intent to inflict harm on another person through the removal or the threat of removal of a friendship or other social relationship (Crick and Grotpeter, 1995). Direct relational bullying includes efforts to isolate the targeted children or youth by keeping them from interacting with their peers or ignoring them (Bear et al., 2014; Gladden et al., 2014; Zhou et al., 2022). Indirect relational bullying includes spreading false and/or harmful rumors, publicly writing derogatory comments, or posting embarrassing images of them in a physical or electronic space without permission (Bear et al., 2014; Gladden et al., 2014; Zhou et al., 2022). Relational bullying is sometimes called social bullying (Borowsky, Taliaferro, and McMorris, 2013; Fitzpatrick and Bussey, 2010). There is some evidence suggesting that teachers perceive relational bullying as less serious, compared with other types of bullying (Bauman and Del Rio, 2006).

Damaging property . Another form of bullying is stealing, altering, or damaging targeted peers' property, which includes destroying the victims’ property in their presence or deleting their personal electronic information (Gladden et al., 2014).

Identity-based bullying . Researchers have identified the need for research examining co-occurrences of bullying and discrimination based on identity (e.g., Garnett et al., 2014; Farrell et al., 2014; Russell et al., 2012). Identity-based bullying, which is sometimes called prejudicial bullying, discriminatory bullying , or stigma-based bullying, resides in the intersection of bullying and bias (Menesini and Salmivalli, 2017). It refers to any form of bullying related to characteristics considered part of a person's identity or perceived identity group such as race, religion, disability, immigration status, sexual orientation, gender identity, or physical appearance (Farrell et al., 2014; Spiegler, 2016). This type of bullying is rooted in discrimination (Price et al., 2019). Compared with other types of bullying, identity-based bullying presents unique challenges to researchers since the mechanisms through which identity affects bullying behaviors remain poorly understood and may neglect broader, interacting systems of oppression that influence the bully–victim relationship (Galan et al., 2021; Price et al., 2019). Also, compared with studies on other types of bullying, studies that center on multiple aspects of social identity remain limited (Galan et al., 2021; Russell et al., 2012).

Sexual bullying . When children describe experiences of bullying, they often include descriptions of sexual harassment. Thus, some researchers have explored the overlap between bullying and sexual harassment (Duncan, 1999; Turner‑Moore, Milnes, and Gough, 2022). Sexual bullying has been defined as bullying, harassment, or unwanted touching that is sexualized, related to sexuality, or related to gender expression (Armitage, 2021; Duncan, 1999; Turner‑Moore, Milnes, and Gough, 2022). Subtypes include 1) appearance-based bullying (e.g., mean names about an individual’s body or clothes), 2) physical sexual bullying (e.g., unwanted touching), 3) sexual harassment (e.g., being sent sexual jokes, having photos taken up their skirt, being pressured to send sexual photos), 4) bullying related to sexual experience (e.g., sharing sexual photos/videos without permission, being called mean names and rumors because they have had sex), and 5) bullying related to sexual orientation, including mean names and rumors about being lesbian, gay, bisexual, transgender, or not having had sex (Milnes et al., 2015). Sexual bullying is identified much less often in research studies than other types and often overlaps with other types of bullying.

Weight-based bullying . Although identity-based bullying may include bullying based on appearance, the fact that weight is not static makes this type of bullying unique. Being overweight is one of the most prevalent reasons for peer harassment, teasing, and bullying (Janssen et al., 2004; Lumeng et al., 2010; Puhl et al., 2011; Puhl et al., 2016).  However, many of the national data collection efforts (see Scope of the Problem ) do not include measures of weight to assess this type of bullying.

Context of Bullying

Bullying may occur within multiple contexts, including at school and at school events, traveling to and from school, within a youth's neighborhood, or on the internet . Bullying that occurs using technology is considered electronic bullying or  cyberbullying.

Cyberbullying. In recent years, cyberbullying has gained much attention in research (Antoniadou and Kokkinos, 2015; Evangelio et al., 2022; Kowalski et al., 2014; Olweus, 2017; Polanin et al., 2021; Zhu et al., 2021). The joint report by the CDC and DOE (Gladden et al., 2014) considers bullying that uses technology or electronics a context in which bullying occurs and does not consider it conceptually distinct from bullying that occurs in person. Instead, cyberbullying is viewed as a context where verbal and relational aggression and causing damage to property (e.g., deleting personal data) occur through electronic means.

Conceptualizing cyberbullying is challenging, given the various forms and venues through which it can occur (Kowalaski et al., 2014); however, one study (Willard 2007) provided a taxonomy of the seven types of cyberbullying, as follows: 1) flaming (i.e., engaging in or provoking an online fight), 2) harassing (i.e., sending repetitive, offensive messages to a victim), 3)  using outing and trickery (i.e., soliciting personal information and then electronically sharing it without consent), 4) excluding (i.e., blocking an individual from online groups), 5) impersonating (i.e., posing as the victim and electronically communicating negative or inappropriate information to others), 6) cyberstalking (i.e., using electronic communication to send repetitive, threatening communications), and 7) sexting (i.e., distributing nude pictures of the victim without consent). However, studies still differ on how cyberbullying is defined (Kofoed and Staksrud, 2019).

Scope of the Problem

Several national data sources track the prevalence of bullying and bullying victimization. For example, in the United States, three of the main sources for national estimates are the 1) School Crime Supplement to the National Crime Victimization Survey , 2) the Youth Risk Behavior Surveillance System, and 3) the National Survey of Children’s Health . 

School Crime Supplement to the National Crime Victimization Survey. The School Crime Supplement (SCS) to the nationally representative National Crime Victimization Survey (NCVS) was co-designed by the Bureau of Justice Statistics and the National Center for Educational Statistics to collect information about victimization, crime, and safety at school and to present these national indicators to practitioners, policymakers, and the public. Biennially, the SCS collects data on bullying by asking students ages 12–18 in U.S. public and private elementary, middle, and high schools whether they had been bullied at school during the school year.

In 2019, the SCS reported the following:

• Victimization types and rates . During the school year, 22 percent of the students reported being bullied at school. This included 14 percent who said they were made fun of, called names, or insulted; 15 percent who were victims of rumors; 5 percent who were threatened with harm; 5 percent who were pushed, shoved, tripped, or spit on; 6 percent who were excluded from activities on purpose; and 2 percent whose property was destroyed on purpose (Irwin et al., 2022; NCES, 2022).
• Geography . During the school year, the percentage of students in rural areas who reported being bullied at school was higher than for students in other locales. Of the students who reported being bullied, 28 percent were enrolled in schools in rural areas, 22 percent were enrolled in schools in cities and in towns, and 21 percent were enrolled in schools in suburban areas (NCES, 2022).
• Changes over time . In 2019, the total percentage of students ages 12–18 who reported being bullied at school during the school year was 22 percent, compared with 28 percent in 2009 (Irwin et al., 2022; NCES, 2022).

The Youth Risk Behavior Surveillance System ( YRBSS ) Youth Risk Behavior Survey (YRBS) is administered every 2 years to students in grades 9 through 12 and is representative of public and private school students from all 50 states and the District of Columbia. The YRBS covers various risk behaviors, including bullying victimization on school property and electronic bullying victimization. The 2019  YRBS  was completed by 13,872 students in 136 schools (Creamer et al., 2020). The survey defines bullying as "when 1 or more students tease, threaten, spread rumors about, hit, shove, or hurt another student over and over again. It is not bullying when 2 students of about the same strength or power argue or fight or tease each other in a friendly way" (CDC, 2019, p. 7).

The 2019 YRBS reported the following:

• Bullying victimization types and rates . In the past year, 19.5 percent of high school students reported having been bullied at school, and 15.7 percent reported having been electronically bullied (Basile et al., 2020).
• Victimization by state . Data were available for 44 states and the District of Columbia and show some variation by state. States with the lowest reported in-school bullying victimization rates included the District of Columbia, Florida, Georgia, Ohio, Nevada, and Texas, all reporting that fewer than 15 percent of high school students were bullied at school. States with the highest reported in-school bullying rates included Alaska, California, Kentucky, and South Carolina, all reporting that more than 23 percent of students were victimized. States with the lowest reported electronic bullying victimization rates were District of Columbia, Florida, Georgia, and Nevada (with fewer than 12 percent of youth reporting cyberbullying victimization), and states with the highest rates were Alaska and New Hampshire, with more than 19 percent reporting victimization (CDC, n.d.a.).
• Changes over time . There was no change in the total percentage of students who reported being bullied at school from 2009 through 2019 or in the total percentage of students being electronically bullied from 2011 through 2019 (CDC, 2020).

The National Survey of Children's Health (NSCH) collects data on multiple, intersecting aspects of children's health and well-being, including bullying and bullying victimization (Child and Adolescent Health Measurement Initiative, 2021). The NSCH asks parents with children ages 6–17 the following questions (not involving siblings): "During the past 12 months, how often was this child bullied, picked on, or excluded by other children?" and "During the past 12 months, how often did this child bully others, pick on them, or exclude them?" (U.S. Department of Health and Human Services, 2022).

Among households with children ages 6 to 17 in 2019–2020, the survey reported the following:

• Bullying . Among parents with children between 6 and 17 years old, 17.2 percent reported that their child bullied others, picked on others, or excluded other children at least once in the past year. Among parents with children between 12 and 17 years old, 13.7 percent reported that their child bullied others at least once in the past year (Child and Adolescent Health Measurement Initiative, n.d.).
• Victimization . Of the respondents, 41.2 percent reported that their child was bullied, picked on, or excluded by other children at least once in the past year. Among parents with children between 12 and 17, 35.2 percent reported that their child was bullied by others at least once in the past year (Child and Adolescent Health Measurement Initiative, n.d.).

Gender, age, race, ethnicity, and sexual orientation

Studies often examine differences in prevalence rates of bullying victimization and perpetration by gender, age, race, ethnicity, and sexual orientation. Researchers have found consistent differences in bullying perpetration by age and gender; consistent differences in bullying victimization by sexual orientation, gender identity, and body size; and mixed results regarding victimization by gender.

Age . Data collection efforts and research studies generally find that bullying perpetration and victimization peaks and intensifies around mid-childhood to early adolescence and then decreases over time (Acquah et al., 2016; Baldry and Farrington, 2004; Irwin et al., 2022; Kennedy, 2019; Mitsopoulou and Grovazolias, 2015). For example, the SCS administered in 2019 found that higher percentages of students in grades 6–8 reported being bullied at school during the school year (ranging from 27 to 28 percent), compared with students in grades 9–12, which ranged from 16 to 19 percent (Irwin et al., 2022). Also, the YRBS found that bullying victimization rates were higher in middle school than in high school. Among the 14 states that reported both middle school and high school in-school bullying, victimization rates were much higher for the middle school students. For these 14 states, the percentage of middle school students who reported bullying victimization ranged from 31.9 to 44.9 percent, compared with the percentage of high school students reporting bullying victimization, which ranged from 13.9 to 22.7 percent (Basile et al., 2020; CDC, n.d.a.; CDC, n.d.b.). Given that bullying is a behavior that occurs among peers, bullying perpetration also appears to peak at the same time. Data from the 2019 and 2020 NSCH found that 20.9 percent of children ages 6–11 reported having bullied peers at least once in the past year, compared with 13.7 percent of children ages 12–17 (Child and Adolescent Health Measurement Initiative, n.d.). Also, a study of students in grades 6–12 in Canada found that the prevalence of bullying was highest for boys in grade 8 and for girls in grade 9 (Pepler et al., 2006).

Gender and bullying perpetration . Studies examining bullying commonly find that boys are more likely to perpetrate bullying behaviors than girls (Fergusson, Boden, and Horwood, 2014; Li, 2006; Mitsopoulou and Grovazolias, 2015; Olweus and Limber, 2010), a finding that has been called “rather consistent and substantial” (Smith et al., 2018, p. 4). For example, analysis of 2016 data from the NSCH found that girls ages 12–17 had a 35 percent lower rate of bullying their peers, compared with boys of the same age (Lebrun-Harris et al., 2019). More recent NSCH data continues to show this gender disparity: combined data from the 2019 and 2020 NSCH showed that 18.5 percent of adolescent boys bullied their peers at least once during the year, compared with 15.9 percent of adolescent girls. And the 2020 NSCH found that that 15.6 percent of adolescent boys bullied their peers at least once in the past year, compared with 13.1 percent of adolescent girls ( Child and Adolescent Health Measurement Initiative, n.d.) . However, this may not be the case for all types of bullying. Several studies have found that cyberbullying (Hinduja and Patchin, 2008; Slonje and Smith, 2008; P.K. Smith et al., 2008; Ybarra and Mitchell, 2004) and relational bullying perpetration rates (Casper, Card, and Barlow, 2020) are similar for boys and girls. Also, studies of bully-victims often show that boys are more likely to be both victims and perpetrators (Cook et al., 2010; Liu et al., 2021; Zych et al., 2021).

Gender and bullying victimization . In terms of bullying victimization by gender, findings are mixed. Some studies find that girls are more likely to be victims (Basile et al., 2020; Bevilacqua et al., 2017; Cassidy, 2008; Irwin et al., 2022). For example, the SCS found a higher percentage of female students (25 percent), compared with male students (19 percent), who reported being bullied at school during the school year (Irwin et al., 2022). Similarly, the 2019 YRBS found that girls reported higher levels of bullying victimization than boys (30.2 percent versus 19.2 percent, respectively). Specifically, girls experienced higher levels of bullying on school property (23.6 percent), compared with boys (15.4 percent). Also, compared with 10.9 percent of male students, 20.4 percent of the female students reported being bullied electronically (Basile et al., 2020). There were also some gender differences in changes over time. YRBS data (CDC, 2020) show that from 2009 to 2019, fewer male students were bullied at school (18.7 percent in 2009, compared with 15.4 percent in 2019), but more female students were bullied at school (21.2 percent in 2009, compared with 23.6 percent in 2019).

However, other studies have found that boys are more likely to be victims (Peguero, 2008; Smith et al., 2018; Zych et al., 2021). For example, analysis of data from the Education Longitudinal Study of 2002, which included almost 8,000 students in more than 500 schools, found that female students were less likely than male students to be bullied while in school (Peguero, 2008). These different research findings may result from the different definitions of bullying. In the study mentioned above (Peguero, 2008), bullying victimization was measured by responses to the following six items: 1) "someone threatened to hurt me at school," 2) "someone hit me," 3) "someone used strong-arm or forceful methods to get money or things from me,” 4) "someone bullied me or picked on me," 5) "I had something stolen from me at school," and 6) "someone purposely damaged or destroyed my belongings." Relational bullying was not included.

Race/Ethnicity . Most of the studies that examine bullying by race/ethnicity focus on victimization, and findings are mixed (Xu et al., 2022). Some studies find that white youth are more often bullied than youth of other races/ethnicities (Basile et al., 2020; Peguero, 2008), whereas others find that youth of two or more races are the most at-risk group for victimization (Galan et al., 2021; Irwin et al., 2022). Still, others find that Black youth are more likely to be victims, compared with youth of other races/ethnicities (Goldweber, Waasdorp, and Bradshaw, 2013). The SCS found that the percentage of students of two or more races (37 percent) who reported being bullied was higher than for white students (25 percent) and Black students (22 percent), which was in turn higher than the percentage of Asian students (13 percent) who reported being bullied (Irwin et al., 2022). Analysis of data from the 2019 YRBS found that, c ompared with Hispanic or Black students, white students reported the highest prevalence of both in-school and electronic bullying victimization: 23 percent of white students, 15 percent of Black students, and 15 percent of Hispanic students reported being bullied on school property; 19 percent of white students, 13 percent of Hispanic students, and 9 percent of Black students reported being electronically bullied (Basile et al., 2020; CDC, n.d.a.; Johns et al., 2020). Higher-than-average victimization rates also have been reported by Native American youth. Compared with Hispanic and Asian students, Native American youth reported higher levels of being bullied on school property, representing a statistically significant finding. Native American youth were also more likely to report being bullied, compared with white students and students of multiple races; however, these differences were not statistically significant. From 2009 through 2019, bullying rates by race have remained unchanged except for rates for Hispanic youth, who experienced a decrease in at-school victimization, from 18.5 percent to 14.8 percent.

Several studies have found that different measures of bullying may result in different prevalence rates (e.g., Branson and Cornell, 2009). However, the reason for racial or ethnic differences may be related to how youth of different races/ethnicities self-report bullying (Lewis et al., 2015; Sawyer, Bradshaw, and O’Brannon, 2008). Some researchers have concluded that cultural differences and social norms may inform how individuals perceive and identify with the label "bullying," resulting in rates of reporting that are not commensurate with the amount of bullying they actually experience (Goldweber, Waasdorp, and Bradshaw, 2013; Lewis et al., 2015; Sawyer, Bradshaw, and O’Brannon, 2008; Xu et al., 2022).

Sexual orientation . Studies that include measures of sexual orientation consistently find that lesbian, gay, and bisexual (LGB) youth have an increased likelihood of bullying victimization (Basile et al., 2020; Espelage, 2015; Garaigordobil et al., 2020; Jackman et al., 2020; Kosciw et al., 2020; Robinson and Espelage, 2011). The 2019 YRBS found that LGB students and those "unsure of their sexual identity" reported higher levels of both in-school and electronic bullying victimization than heterosexual students (Basile et al., 2020). Specifically, t he percentage of students who reported being electronically bullied was higher for LGB high school students (27 percent) than for students who were "unsure about their sexual identity" (19 percent), and both percentages were higher than the percentage for heterosexual students (14 percent). Also, the percentage of students who reported being bullied at school was higher for LGB high school students (32 percent) and for students who were "unsure about their sexual identity" (27 percent), compared with heterosexual students (17 percent) [Basile et al., 2020].

Intersectional identities. Several studies have also examined the influence of intersectional identities on bullying. For example, a study of 3,939 high school students in 13 public high schools in Pennsylvania, which measured the prevalence of identity-based bullying (based on race/ethnicity/national origin, gender identity, and sexual orientation), found that the highest rates of identity-based bullying were reported by gender-diverse Black and Hispanic youth (Galan et al., 2021).

Body mass index . While fewer data collection efforts include measures of body mass, studies that do include this information consistently find that being overweight or obese increases the likelihood of bullying victimization (Brixval et al., 2012; Lumberg et al., 2010; Puhl, Luedicke, and Heuer, 2011; Puhl et al., 2016; Thompson et al., 2020; Van Geel, Vedder, and Tanilon, 2014). For example, the 2019 YRBS data show that obese high school students were more likely to report being bullied at school than non-obese high school students (23.1 percent of obese students, compared with 18.7 percent of non-obese students) [CDC, n.d.a.].

Theoretical Foundation

Within the large body of research on bullying, many theoretical frameworks and models are used. Several are based on ecological and evolutionary contexts (Volk et al., 2014), which inform the approaches that are developed to address this social problem (Ttofi and Farrington, 2009).

• Ecological theories focus on the interactions between individuals and their social environments and how these interactions affect individual behavior. In bullying research, ecological approaches consider the social, physical, institutional, and community contexts where bullying occurs as well as the individual characteristics of the youth who bully or are bullied (Hong and Espelage, 2012; Smokowski and Evans, 2019; Swearer and Doll, 2001). For example, while individual characteristics, such as self-confidence, gender, and past experiences as a victim, may influence the likelihood of bullying, whether bullying occurs also depends on the context and environments that subsequently encourage or suppress such behavior, such as perceived peer norms, school climate and policies, and larger societal attitudes (Liu and Graves, 2011; Perkins, Craig, and Perkins, 2011).
• Ethological/evolutionary perspectives  view bullying as instinctual or innate and as a tool for achieving social dominance (Liu and Graves, 2011). Interventions based on ethological or evolutionary theories often offer bullies prosocial alternatives that achieve the same status goals as bullying behavior (Ellis et al., 2015).
• The general aggression model (GAM) provides a comprehensive framework that integrates domain-specific theories of aggression (Anderson and Bushman, 2002). In bullying research, the GAM focuses on both the individual and the situation that influences aggressive behavior (Ferguson and Dyck, 2012; Kowalaski et al., 2014; Vannucci et al., 2012).
• Cognitive and social–cognitive theories are influenced by theories of cognition and neurobiology. Research on bullying based on these theories focuses on the ways in which children process social information, as it relates to witnessing and re-enacting aggressive acts (Liu and Graves, 2011; McMahon et al., 2009; Wearer et al., 2014).
• Social bond theory emphasizes the importance of bonds between individuals and conventional society; it posits that offending behavior is caused by weak social bonds (Hirschi, 1969). Researchers have used this theory to examine the association between relationships at school and peer victimization (Cecen-Celik and Keith, 2019).
• Empowerment theory is used to design bullying prevention programs (Ortega-Barón et al., 2019). It involves strengthening individual, group, and community resources to allow adolescents to control their lives in both virtual and school settings (Zimmerman, 2000).
• The personal and social responsibility model argues that responsible behaviors can be taught and generalized to other contexts of life (Hellison, 1995). Programs based on this model encourage shared responsibility to address bullying (Ortega-Barón et al., 2019).
• Contact theory (Allport, 1954; Pettigrew, 1998) focuses on the conditions under which social contact can lead to the reduction of prejudices. It assumes that lack of adequate, equal, and cooperative contact between in- and out-group members leads to bias and conflict. This theory informs approaches that focus on cooperative learning and activities to reduce prejudices, which can minimize the imbalance of power that contributes to bullying (Protogerou and Flisher, 2012;Van Ryzin and Roseth, 2019).
• Genetic and other biologic theories consider how genetics and other biological factors influence bullying (Liu and Graves, 2011; Veldkamp et al., 2019).

Risk Factors for Bullying Victimization and Perpetration

Considerable research has been done on the risk factors for bullying victimization and perpetration (see also the Model Programs Guide literature review on Risk Factors for Delinquency ). These risk factors have been examined at the following ecological domains: 1) individual, 2) family, 3) peer group, 4) school, and 5) community. Further, some studies have examined the influences of one type of bullying on other types (Kowalski et al., 2014; Low and Espelage, 2012) and the relationships between victimization and perpetration (Galan et al., 2021; Mitsopoulou and Giovazolias, 2015). Several studies using meta-analytic techniques (which synthesize and summarize existing bodies of literature) have identified the risk factors with the strongest impacts.

One of the challenges in bullying research is identifying causality. Many studies have concluded that directionality between bullying and other factors is not clear and may be bi-directional or reciprocal (Reijntjes et al., 2010; Reijntjes et al., 2011; Walters and Espelage, 2021; Wang et al., 2017; Zych, Ortega-Ruiz, and Del Rey, 2015). Specifically, there is often difficulty differentiating between the predictors of bullying and the consequences of bullying. For example, studies have sometimes found that internalizing problems, such as depression and anxiety, may increase the likelihood of bullying victimization while also finding that bullying victimization may cause depression and anxiety (Reijntjes et al., 2011). Similarly, studies have found that children who are overweight or obese are not only at a heightened risk of bullying victimization, but also that children who are bullied are at a heightened risk of health problems, including obesity (Thompson et al., 2020).

Bullying Victimization

There are many studies examining the risk factors associated with bullying victimization, mostly in the individual domain. One systematic review of bullying and cyberbullying research compared many of the most-studied risk factors (Zych, Ortega-Ruiz, and Del Rey, 2015). The authors found that 1) risk factors related to internalizing problems were the strongest predictors of bullying and cyberbullying victimization, and 2) traditional bullying victimization was one of the strongest predictors of cyberbullying victimization specifically (Kowalski et al., 2014).  The risk factors with the largest effect sizes are described below, under their domains. Some of the other risk factors that demonstrate smaller (but still statistically significant) effect sizes are also described.

Individual Domain

The individual domain comprises characteristics that are directly related to a specific person (Development Services Group, 2015a). Some examples of individual risk factors related to bullying victimization include internalizing behaviors, such as depression and anxiety; externalizing behaviors, such as defiance and aggression; and other factors, such as attention deficit hyperactivity disorder (ADHD), poor problem-solving skills, and lack of self-control. Also, frequent internet use and risky internet behavior can increase the risk of cybervictimization specifically.

Internalizing behaviors are actions that are directed inward and include withdrawn, depressive, anxious, and avoidant behaviors (Cook et al., 2010). A meta-analysis of 152 articles published between 1970 and 2006 (Cook et al., 2010) found that having internalizing behavior problems was one of the most impactful predictors of bullying victimization. For example, a study of children in grades 3 to 7 found that children who were identified by peers as having internalizing problems (defined as withdrawal, anxiety-depression, and hovering behaviors) were more likely to be victimized by bullying one year later, compared with students without these characteristics (Hodges and Perry, 1999). A study of students in Scotland found that having depression at ages 11 and 13 predicted bullying victimization two years later, at ages 13 and 15 (Sweeting et al., 2010). Also, a study of students in grades 7 to 9, in Finland, found that youth who reported experiencing emotional loneliness, social loneliness, or social anxiety were more likely to be victimized by bullying than students without these difficulties (Acquah et al., 2016).

Externalizing behaviors are actions that are under-controlled and characterized by defiant, aggressive, disruptive, and noncompliant responses (Cook et al., 2010). Several research studies have found associations between externalizing behaviors and victimization, especially among younger children (Cook et al., 2010; Kochenderfer-Ladd, 2003; Reijntjes et al., 2010). For example, a study of children in preschool and kindergarten found that students with high levels of aggression in the fall of the school year (e.g., "fights with other children") were more likely to experience bullying victimization both in the fall and the spring of the school year, compared with students demonstrating low levels of aggression (Hanish et al., 2004). Another study of children in grades 1, 2, and 4 found that children who were physically victimized (e.g., "children who other kids push or hit" or "children who get picked on by other kids") were more likely to be considered aggressive by teachers (Hanish and Guerra, 2000).

Other individual factors that have been linked to bullying victimization include neuroticism, ADHD, poor coping skills, poor problem-solving skills, low levels of conscientiousness, and lack of self-control (Baldry and Farrington, 2005; Cook et al., 2010; Mitsopoulou and Giovazolias, 2015; Zych et al., 2021; Zych, Ortega-Ruiz, and Del Rey, 2015). Also, a meta-analysis of studies examining cyberbullying specifically found that cybervictimization was related to frequency of internet use, risky online behavior, anger, and social anxiety (Kowalski et al., 2014). Another meta-analysis of 40 studies on the association between empathy and bullying found that youth who were less skilled at understanding the emotions of others were more likely to be bullied (van Noorden et al., 2014).

Family Domain

Risk factors in the family domain are generally related to family structure, support, and functioning (Development Services Group, 2015a; Hoeve et al. 2012; Wong, Slotboom, and Bijleveld, 2010). Researchers have identified several risk factors for bullying victimization in the family domain, including abuse and neglect, maladaptive parenting, parental mental health problems, and low socioeconomic status (Benedini, Fagan, and Gibson, 2016; Lereya, Samara, and Wolke, 2014; Tippett and Wolke, 2014).

A meta-analysis of 70 studies found that bullying victims were more likely to have been abused or neglected , to have experienced maladaptive parenting , and to have overprotective parents , compared with youth who were not bullied (Lereya, Samara, and Wolke, 2014). For example, a study of older teens who self-identify as gay, lesbian, bisexual, queer, questioning, pansexual, or other, found that childhood emotional abuse was related to verbal, relational, and electronic bullying victimization when they were ages 15–19; physical abuse was correlated with physical bullying victimization; and sexual abuse was correlated with verbal and physical bullying victimization (Sterzing et al., 2016). Victims of bullying are also less likely to 1) have authoritative parents, 2) have good parent–child communication, 3) have parents who were involved and supportive, 4) receive parental supervision, or 5) have warm and affective parents (Lereya, Samara, and Wolke, 2014).

Peer, School, and Community Domains

Because there is less research examining the influence of peer, school, and community risk factors on bullying, when compared with individual and family risk factors, these three domains are included in the same section. Generally, risk factors in the peer domain are related to peer norms and attachment, socialization, and interactions with peers (Hoeve et al. 2009); risk factors at the school level are related to school attendance, academic performance, and attachment and commitment to school (Wong, Slotboom, and Bijleveld, 2010); and risk factors at the community level are related to the physical environment, economic and recreational opportunities, existing social supports, and other characteristics or structures that affect successful community functioning (Kaufman, 2005; Reingle, Jennings, and Maldonado-Molina, 2011).

As mentioned above, a large systematic review of bullying and cyberbullying research found that traditional bullying victimization by peers was one of the strongest predictors of cyberbullying victimization, indicating that youth who are bullied face-to-face are also likely to be bullied online (Kowalski et al., 2014). Some researchers have concluded that victims of cyberbullying share common characteristics with the victims of other types bullying (Dehue et al., 2012). Another study of more than 7,000 youth in Iceland found that youth who disliked attending school and youth who had a residential move in the past 12 months were more likely to be victims of group bullying than youth who liked attending school and youth who had not moved (Mann, et al., 2005).

Another risk factor in the community domain is related to urbanicity. Several studies have found that living in a rural community may increase the likelihood of bullying victimization. As mentioned in the Scope of the Problem section, the School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS) found that the percentage of students who reported being bullied at school was higher for students in rural areas than for students in cities, towns, or suburbs (NCES, 2022). Also, a study of 1,600 students in grades 7 and 8 in three schools in Utah found that students in the rural school were more likely to be physically and verbally bullied than students in the suburban school or in the urban school (Olsen, 2010). Finally, a study of transgender and gender-diverse adolescents found that bullying victimization was more common for students living farther away from cities (Eisenberg et al., 2019).

Bullying Perpetration

There are many studies examining the risk factors associated with bullying perpetration. In a systematic review (Zych, Ortega-Ruiz, and Del Rey, 2015) identifying and comparing several risk factors, externalizing behaviors and moral disengagement were found to be the two strongest predictors of bullying behavior. Also, normative beliefs about aggression and bullying victimization were rated as having medium effect sizes in predicting cyberbullying perpetration. The risk factors with the largest effect sizes are described below under their domains. Other risk factors that demonstrate smaller (but still statistically significant) effect sizes are also described.

Although some of the individual-level risk factors for bullying perpetration are similar to those mentioned above for bullying victimization, many of them are different. Risk factors for bullying perpetration in the individual domain include externalizing behavior problems, moral disengagement, antisocial beliefs about aggression, lack of social competence, internalizing behavior problems, and low levels of empathy.

Externalizing behaviors refer to actions that are under-controlled and characterized by a host of defiant, aggressive, disruptive, and noncompliant responses. A meta-analysis (Cook et al., 2010) found that this risk factor was one of the strongest predictors of bullying perpetration. For example, a study conducted in New Zealand found that in middle childhood, individuals who bullied their peers were more likely to have conduct problems compared with those who did not bully (Fergusson, Boden, and Horwood, 2014). Another study, involving sample of 310 adolescents in Canada, found that instrumental aggression (e.g., "I often hit, kick, or punch others to get what I want") predicted bullying behavior (Book, Volk, and Hosker, 2012).

Moral disengagement is a risk factor that is defined as a series of cognitive processes used to disengage moral standards, to achieve absolved guilt and allow immoral conduct (Bandura, 1991; Wang et al., 2017). It is also called moral neutralization of aggression (Ribeaud and Eisner, 2010; Zych et al., 2021) and can lead to aggressive behaviors. A meta-analysis (Gini, Pozzoli, and Hymel, 2014) of the relationship between moral disengagement and different types of aggressive behavior, among children and youth in several countries, found that moral disengagement was a significant correlate of both bullying and cyberbullying. For example, one U.K. study of students in grades 7 to 9 examined the relationship between bullying behaviors and the cognitive mechanisms applied by children to rationalize and justify harmful acts. The authors found that higher levels of moral disengagement were related to higher levels of both bullying and cyberbullying (Pornari and Wood, 2010). Another meta-analysis of cyberbullying studies found that moral disengagement predicted cyberbullying behaviors (Kowalaski et al., 2014).

Normative beliefs about aggression is a risk factor that refers to the extent to which individuals believe that an aggressive response is an appropriate social behavior (Huesmann and Guerra, 1997; Jiang et al., 2022). Meta-analyses have found that normative beliefs about aggression increase the likelihood of bullying and cyberbullying (Zych, Ortega-Ruiz, and Del Rey, 2015). For example, a longitudinal study of more than 7,000 ethnically diverse students in Colorado found that normative beliefs about bullying predicted bullying perpetration one year later (Gendron, Williams, and Guerra, 2010).

Lack of social competence refers to an individual's inability to interact effectively with others and to exhibit socially acceptable behaviors. This risk factor has been identified as a predictor of bullying perpetration (Cook et al., 2010). For example, in a study of 120 students in grades 4 to 6, the authors found that youth who bullied others were more likely to lack prosocial skills than those who did not bully others, as measured by their teachers (Larke and Beran, 2006). This finding was consistent for both direct bullying and indirect bullying perpetration.

Low levels of empathy. Several studies examine the influence of empathy on peer victimization. One meta-analysis of 40 studies of the association between empathy and bullying found that lower levels of empathy predicted more bullying (van Noorden et al., 2014). The authors suggested that children involved in bullying appear to have an impaired ability to feel or experience what others feel, although they are not necessarily incapable of knowing what others feel. A meta-analysis of 27 studies examining personality traits, empathy, and bullying found that lower levels of cognitive and affective empathy were associated with the higher likelihood of bullying perpetration (Mitsopoulou and Giovazolias, 2015).

Additional individual factors that influence bullying perpetration include ADHD, lower levels of agreeableness, lower levels of conscientiousness, higher levels of emotional instability, and higher levels of extraversion (Benedict et al., 2014; Fergusson, Boden, and Horwood, 2014; Mitsopoulou and Giovazolias, 2015). Internalizing disorders can also influence bullying perpetration (Cook et al., 2010). For example, examination of data from more than 60,000 youth from the National Survey of Children’s Health found that diagnoses of depression or anxiety were strongly associated with being identified as a bully by their parents (Benedict et al., 2014). Finally, some research has found that narcissism is a risk factor for bullying for boys, but not for girls (Reijntjes et al., 2016). Additional risk factors for cyberbullying perpetration specifically include anger, a history of cybervictimization, a history of traditional bullying, a history of traditional bullying victimization, frequency of internet use, and risky online behavior (Kowalaski et al., 2014).

Like bullying victimization, risk factors for bullying perpetration are related to family structure, support, and functioning. Certain characteristics of parents may also increase the risk of bullying perpetration for their children.

Several studies have found that family environments have an effect on bullying behaviors (Cook et al., 2010). A study examining non-physical bullying at school (i.e., teasing, name calling, social exclusion, and rumor spreading) among early adolescents found that students who bullied their peers had lower levels of parental monitoring and were more likely to have experienced family violence than those who did not bully their peers (Low and Espelage, 2012). Similarly, a study of youth, ages 11 to 18, found that those who reported lower levels of parental monitoring also reported higher levels of bullying (Doty et al., 2021). In addition, a study that followed individuals for 30 years found that in elementary school, students who engaged in bullying behavior were more likely to have been exposed to sexual abuse at home (Fergusson, Boden, and Horwood, 2014).

Some studies have examined the relationship between parental characteristics and bullying. A study examining data from the 2003 and 2007 National Survey of Children's Health (NSCH) found that suboptimal maternal mental health and parental anger toward their children were both associated with higher odds that their children would bully their peers (Shetgiri, Lin, and Flores, 2013). Another study found that children who had mothers who were younger than 20 when they were born were more likely to report that they bullied others, compared with children who had older mothers (Fergusson, Boden, and Horwood, 2014).

Risk factors for bullying perpetration in the peer, school, and community domains are described together, because the body of research related to bullying in these domains is smaller than the research in the individual and family domains. Perceived group norms that bullying is acceptable behavior, overestimation of the amount of bullying occurring in school, poor academic performance, unsafe neighborhoods, and perceptions of normlessness within a community are all risk factors specific to bullying perpetration. Also, having a history of bullying victimization increases the risk of bullying perpetration.

Social norms are expectations about appropriate behavior that occurs in a group context; they have a powerful impact on individual behavior (McDonald and Crandall, 2015). A study of youth in five middle schools in New Jersey found that students who thought that bullying was commonplace in their schools and who thought that other students supported bullying were more likely to bully their peers than students who did not think these things (Perkins, Craig, and Perkins, 2011). This study also found that students overestimated the prevalence of bullying and pro-bullying attitudes in their schools. Another study of middle school students found that students who had friends who supported bullying behaviors were more likely to bully their peers than students who did not have friends who supported bullying (Nickerson and Mele-Taylor, 2014).

There is also some evidence that academic performance is related to bullying perpetration, with several studies finding that youth who bully their peers are more likely to have academic challenges (Cook et al., 2010; Zych, Farrington, and Ttofi, 2019). However, academic performance is often examined as an outcome, rather than a predictor of bullying (Kowalski et al., 2014).

Characteristics of communities, such as socioeconomic indicators, rates of violence or crime, and drug trafficking, can also have an influence on bullying (Cook et al. 2010). For example, a study of middle school students in a large school in the Midwest found that those with greater neighborhood safety concerns (e.g., "I see gang activity in my neighborhood") were more likely to bully their peers, compared with those without these concerns (Espelage, Bosworth, and Simon, 2000). Another study, of more than 7,000 youth in Iceland, found that youth perceptions of anomie/normlessness in their communities, which refers to the breakdown of social structure and the breakdown of legitimacy in a society (Basir and Bala, 2019), was associated with increases in group bullying (Mann et al. 2015).

Finally, bullying victimization is strongly associated with bullying perpetration. Analysis of data from the 2016 NSCH found that children ages 6–11 who were victims of bullying had more than six times the prevalence of bullying others, and adolescent victims had more than eight times the prevalence of bullying others, compared with those who were not victimized (Lebrun-Harris et al., 2019). This relationship has also been found in studies examining cyberbullying (Zych, Ortega-Ruiz, and Del Rey, 2015). A meta-analysis of cyberbullying studies found that the greatest predictor of cyberbullying perpetration was cybervictimization (Mitsopoulou and Giovazolias, 2015).

Bully-Victims

Bully-victims are individuals who both experience and perpetrate bullying. Analysis of this group is common in the literature, although less common than examinations of either bullying or victimization alone (Cook et al., 2010; Lereya, Samara, and Wolke, 2014). Researchers generally find that these youth face more challenges and have more risk factors, compared with youth who bully others but are not victims, or with youth who are victims but do not bully others (Haynie et al., 2001; Juvonen, Graham, and Schuster, 2003; Menesini and Salmivalli, 2017). A meta-analysis of 153 studies identified the following risk factors as the strongest for becoming a bully-victim: 1) externalizing behaviors, 2) social competence deficits, 3) poor self-related cognitions, 4) below average academic performance, 5) negative/unhealthy school climate, 6) unfavorable peer status, and 7) negative peer influence. Additional risk factors for both experiencing and perpetrating bullying include male gender, poor family/home environment, divorced parents, substance use, ADHD, sensation seeking, moral disengagement, and low self-control (Cook et al., 2010; Zych et al., 2021).

Commonalities and Differences in Risk Factors

Youth who bully their peers, who are victimized by bullying, and who play both roles share many of the same risk factors, compared with youth who are uninvolved in bullying. These include 1) social context factors such as dysfunctional families and home environments, poor school climate, and disorganized communities; and 2) individual risk factors such as internalizing behavior problems, externalizing behavior problems, social competence deficits, and poor social problem-solving skills (Cook et al., 2010; Liu et al., 2021; Zych, Ortega-Ruiz, and Del Rey, 2015). There are also some differences between groups. For example, poor academic performance, negative peer influence, and other-related cognitions, such as negative beliefs about others and lack of empathy, appear to be risk factors for bullying perpetration but not for victimization. Also, rejection by peers and self-related cognitions, such as low self-esteem, low self-efficacy, and lack of self-respect, are risk factors for being bullied but less so for perpetrating bullying (Cook et al., 2010; Liu et al., 2021). Also, though internalizing and externalizing behavior problems are risk factors for both bullying victimization and perpetration, the influence of externalizing behaviors is stronger on perpetration, and the influence of internalizing behaviors is stronger on victimization (Cook et al., 2010; Hysing et al., 2021).

Protective Factors Against Bullying Victimization and Perpetration

Protective factors are characteristics that reduce the likelihood of adversity leading to negative outcomes and behaviors (Vanderbilt–Adriance and Shaw, 2008). Like risk factors, protective factors exist in the individual, family, peer group, school, and community domains. (For more information on protective factors, see the Model Programs Guide literature review on Protective Factors Against Delinquency .) Meta-analytic research has identified statistically significant relationships between protective factors and bullying in all domains (Zych, Farrington, and Ttofi, 2019), and many of these factors demonstrate protection against more than one bullying role. For example, a positive school climate and positive parenting were related to both a lower likelihood of bullying and lower levels of victimization (Zych, Farrington, and Ttofi, 2019). This section describes risk-based protective factors , which can prevent initiation of bullying behaviors and victimization by mitigating the negative effect of risk factors on bullying. In subsequent sections, interactive protective factors , which can lessen the negative impact of bullying after it occurs, are discussed.

Protective Factors Against Bullying Victimization

Two of the most powerful protective factors against bullying victimization, social competence and favorable peer status , are in the individual and peer domains, respectively (Zych, Ortega-Ruiz, and Del Rey, 2015). These two protective factors are described below under their domains. Other protective factors that demonstrate smaller (but still statistically significant) effect sizes are also described.

Individual-level protective factors are personal characteristics that protect youth against victimization, problem behaviors, and other undesirable outcomes (Development Services Group, 2015b). Much of the research on factors that protect against bullying victimization include analyses of individual factors such as social competence, problem-solving skills, conscientiousness, openness, agreeableness, and empathy. The individual-level characteristic with the strongest evidence as a protective factor against bullying victimization is social competence.

Social competence is defined as an overall evaluative judgment of an individual’s social skills that enables them to interact effectively with others and to avoid or inhibit socially unacceptable behaviors. Social competence is one of the strongest protective factors against bullying victimization (Cook et al., 2010). Thus, children and youth who are more socially competent are less likely to become bullying victims. A meta-analysis of cyberbullying articles also found that youth with high levels of social competence are less likely to be cyberbullying victims (Kowalski et al., 2014).

Having strong social problem-solving skills also decreases the likelihood of bullying victimization (Baldry and Farrington, 2005; Cook et al., 2010; Deniz and Ersoy, 2015). A study of 461 students ages 11 to 15 found that those with stronger problem-solving skills were less likely to become victims of bullying, compared with students with poorer problem-solving skills. Problem-solving skills were measured using the following factors: 1) helplessness, 2) control, 3) creativity, 4) confidence, 5) approach style, and 6) avoidance style. Higher scores indicated that the student felt less helpless, more in control, more confident, more creative, and more likely to approach and less likely to avoid problems (Cassidy, 2008).

Researchers have also identified other individual-level protective factors . A meta-analysis of studies examining personality traits, empathy, and bullying behavior (Mitsopoulou and Giovazolias, 2015) found that higher levels of conscientiousness, openness, and agreeableness were related to lower levels of bullying victimization. In addition, as found in the meta-analysis mentioned above (Kowalski et al., 2014), individuals with higher levels empathy and less risky online behavior experienced lower levels of cyberbullying victimization.

The family domain can be an important source of protection for youth against negative life outcomes (Development Services Group, 2015b; Logan-Greene et al. 2011). Protective factors in the family domain typically are related to family structure, support, culture, and functioning. Meta-analytic research on the relationship between family-level factors and bullying has found that high parental involvement and support, and warm and affectionate family relationships were most likely to protect children and adolescents against peer victimization, followed by protective factors such as good family communication and supervision/monitoring (Lereya, Samara, and Wolke, 2013). Other protective factors in the family domain are related to parenting style and family composition.

Several studies have found that positive parenting styles and relationships can protect youth against bullying victimization. A study of 679 boys in Italy found that children with supportive and authoritative parents were less likely to be victimized, compared with children without these types of parents (Baldry and Farrington, 2005). Another study, of more than 12,000 adolescents in grades 5–10, found that students who did not experience bullying had higher levels of parental monitoring and were more satisfied with their families than students who were victims (Liu et al., 2011). Also, a meta-analysis of cyberbullying research found that better parental monitoring and parental control of technology protected against cyberbullying victimization (Kowalski et al., 2014). However, some studies have found that parental monitoring has no influence (Georgiou and Stavrinides, 2013).

Some studies have also examined the effect of family composition , finding that living in a two-parent household can protect youth against bullying victimization (Bevilacqua et al., 2017; Cassidy, 2008). For example, a study of more than 6,000 seventh grade students found that those living in two-parent households were less likely to be bullied and cyberbullied than those from single-parent households (Bevilacqua et al., 2017).

School, Peer, and Community Domains

The school, peer, and community domains can also protect youth against bullying victimization (Zych, Farrington, and Ttofi, 2019). Protective factors related to school generally focus on attendance, performance, attachment, and school climate. Protective factors in the peer domain are related to peer norms, attachment, socialization, and interaction processes, many of which occur in school. Some researchers have found that the impact of peers on negative youth outcomes depends on factors such as age, personality, and gender (Lösel and Farrington, 2012). Finally, protective factors within the community include the physical environment, the availability of economic and recreational opportunities, the social supports, and other characteristics or structures that affect successful functioning of the community and community members. Evidence has indicated that neighborhoods have a tremendous effect on adolescent development (Brooks-Gunn et al., 1993). Though school, peer, and community are three distinct domains, they are presented together in this section because there is less abundant research examining their influence on bullying than the research on individual and family factors.

Favorable peer status . Students who are both well liked and popular are less likely to be victims of bullying. Peer status is defined as the quality of relationships that children and adolescents have with their peers, including rejection, isolation, popularity, and likeability. A meta-analysis of 27 studies examining the relationship between peer status and bullying victimization found that peer status was one of the strongest predictors of bullying victimization (Cook et al., 2010; Zych, Farrington, and Ttofi, 2019; Zych et al., 2021). For example, a longitudinal study of 916 children in Switzerland found that bonding with classmates was a protective factor against victimization (Zych et al., 2021). Favorable peer status is also one of the most impactful protective factors against becoming a bully-victim (Cook et al., 2010; Zych, Farrington, and Ttofi, 2019; Zych et al., 2021). And a meta-analysis examining studies of relational bullying found that students with more peer acceptance and more positive friendship characteristics were less likely to be victims of relational aggression (Casper, Card, and Barlow, 2020).

Several other school- and community-level factors have also been identified as protective factors against bullying victimization. A systematic review on protective factors against bullying and cyberbullying found that supportive communities , a positive school climate , and school safety were found to protect children against bullying and cyberbullying victimization (Zych, Farrington, and Ttofi, 2019). Another study found that students who reported enjoying school and a greater sense of school belonging (e.g., enjoy being with classmates , classmates accept them as they are , classmates are kind and helpful) were less likely to be victimized by bullying, compared with students who did not report these positive school experiences (Liu et al., 2021). Finally an analysis of data from more than 6,000 students who participated in the 2011 School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS) found that interactionist security measures in school, which focus on increasing positive and open communication between students and school staff, decreased the likelihood of direct bullying victimization (e.g., being threatened, pushed, shoved, tripped, or spit on ; had their personal property destroyed ; or made to do things that they did not want to do ) [Cecen-Celik and Keith, 2019].

Protective Factors Against Bullying Perpetration

Two of the strongest protective factors against bullying perpetration, positive other-related cognitions and peer influence, are included in the individual and the peer domains, respectively (Zych, Ortega-Ruiz, and Del Rey, 2015). These two protective factors are described below under their domains. Other protective factors that demonstrate smaller (but still statistically significant) effect sizes are also described.

Much of the research on factors that protect against bullying perpetration include analyses of individual factors. As mentioned above, some of the strongest protective factors against bullying perpetration are related to other-related cognitions, which are thoughts, beliefs, feelings, or attitudes about others and include normative beliefs about others, empathy, and perspective taking. Social competence and problem-solving skills also protect youth against bullying behavior.

Researchers have examined the relationship between bullying perpetration and several types of other-related cognitions. For example, a study youth in Canada found that youth with higher levels of emotionality (characterized by a need to share with and emotionally attach to others) were less likely to bully another student based on their race, although emotionality had no impact on physical, verbal, social, or sexual bullying (Farrell et al., 2014). Additional individual-level factors such as honesty-humility , agreeableness (especially in boys), and openness have also been identified as influential protective factors (Book, Volk, and Hosker, 2012; Pronk et al., 2021). For example, a study involving 310 adolescents in Canada found that one of the most critical protective factors against bullying was honesty-humility, which is a personality trait characterized by truthfulness, fairness, sincerity, modesty, and lack of greed (Book, Volk, and Hosker, 2012). Also, a meta-analysis of personality traits, empathy, and bullying behavior (Mitsopoulou and Giovazolias, 2015) found that higher levels of agreeableness, affective empathy , and conscientiousness were associated with lower levels of bullying.

Social competence is another protective factor against bullying (Irshad and Atta, 2013; Cook et al., 2010; Zych et al., 2015); however, as mentioned above, this factor is stronger in preventing victimization than perpetration (Cook et al., 2010; Zych et al., 2015). A study of children in preschool found that children with higher levels of peer-reported social competence were less likely to bully others, compared with children who had lower levels (Camodeca, Caravita, and Coppola, 2014).

Some studies have found that having effective social problem-solving skills reduces the likelihood of demonstrating bullying behavior (Baldry and Farrington, 2005; Cassidy, 2008; Cook et al., 2010; Deniz and Ersoy, 2016). For example, a study of 461 children ages 11 to 15 found that children who were not victims of bullying had better problem-solving skills, compared with bullying victims (Cassidy, 2008).

Protective factors in the family domain that influence youth bullying perpetration include positive relationships with family, positive family communication styles, higher levels of parental monitoring, and authoritative parenting styles.

A small study of students ages 6–16 found that children who did not bully their peers were more likely to display positive relationships with members of their family than children who did bully (Connolly and Moore, 2003). Another study of more than 12,000 adolescents in grades 5–10 found that students who did not bully others had higher levels of  parental monitoring and were more satisfied with their families than students who bullied others (Liu et al., 2011). Finally, a study examining data from the 2003 and 2007 NSCH found that children whose parents had positive communication approaches and who met their children's friends were less likely to bully others (Shetgiri, Lin, and Flores, 2013).

International studies have also found associations between parental styles, parental monitoring, and bullying. A study of boys in Italy found that those with supportive and authoritative parents were less likely to bully their peers than boys without these types of parents (Baldry and Farrington, 2005), and a study of children in Switzerland found that parental monitoring was protective against bullying (Zych et al., 2021). A study of students in multiple schools in the Netherlands found that youth with authoritative or authoritarian parents were less likely to bully their peers, compared with youth who had permissive or neglectful parents (Dehue et al., 2012). Also, a meta-analysis of cyberbullying research found that parental monitoring had a statistically significant effect on reducing cyberbullying (Kowalaski et al., 2014).

Researchers have also identified protective factors against bullying perpetration in the school, peer, and community domains. These are generally related to peer influence, community cohesion, school safety, school relationships, and academic performance.

Several educational-related factors can protect youth against bulling perpetration. A meta-analysis of 153 studies found that higher levels of academic performance, which includes grade point average, standardized achievement test scores, and academic performance ratings, reduced the likelihood of bullying perpetration (Cook et al., 2010). Also, a meta-analysis of cyberbullying research found that school safety and school climate both demonstrated small but statistically significant protective effects against cyberbullying (Kowalski et al., 2014). Similarly, a longitudinal, prospective study of 916 students in Switzerland found that liking school, bonding with teachers, and bonding with classmates were all protective factors against bullying perpetration (Zych et al., 2021). Finally, another study found that students with a greater sense of belonging in school and who enjoyed school were less likely to bully their peers (Liu et al., 2021).

Peer influence is defined as the positive or negative impact of peers on the adjustment of children. It can include deviant peer group affiliations, prosocial group activities, and reinforcement for (in)appropriate behaviors. Several studies have found that students with positive peer influences are less likely to engage in bullying (Cook et al., 2010).

Community factors can also protect youth against engaging in bullying behavior. A study of more than 7,000 youth in Iceland (Mann et al., 2015) examined two community-level protective factors against group bullying: 1) intergenerational closure (e.g., "My parents know my friends") and 2) neighbors intervening in youth matters (e.g., "Neighbors would do something if a fight broke out in front of their house"). The study found that intergenerational closure protected against each of the three bullying behaviors examined: 1) participating with a group to tease an individual, 2) participating with a group to hurt an individual, 3) participating with a group to start a fight with another group. In addition, the study found that having neighbors who intervene in youth matters was a protective factor against participating with a group to hurt an individual.

Consequences of Bullying

Researchers have identified many consequences of bullying perpetration and victimization, mostly related to violence and offending, internalizing disorders, and academic difficulties. Several of the findings come from longitudinal studies and meta-analyses, which provide some of the highest levels of evidence in research. The evidence indicates that many of the consequences of bullying may even follow individuals into adulthood.

However, causality is often difficult to establish. Although the information presented below represents studies examining outcomes, it is important to note that relationships often can be reciprocal, as noted in the " Risk Factors for Bullying Victimization and Perpetration " section.

Aggression, violence, and offending. Much of the research examining the relationship between bullying and criminality focuses on the role of individuals who engage in bullying behaviors (e.g., Farrinton and Ttofi, 2011). Several studies have found that youth who bully their peers are more likely to become adults who commit offenses, who are violent, who are arrested, and who are convicted of crimes (Fergusson, Boden, and Horwood, 2014; Klomek, Sourander, and Elonheimo, 2015; Ttofi, Farrington, and Lösel, 2012). A meta-analysis of studies examining the effect of bullying on violence found that bullying perpetration at school was a significant predictor of an individual perpetrating violence an average of six years later, raising the risk of violent behavior later in life by about two thirds (Ttofi, Farrington, and Lösel, 2012). Bullying victims also had an increased risk of violence later in life by about one third (Ttofi, Farrington, and Lösel, 2012). Another study found that engaging in bullying behaviors increased the risk for developing an antisocial personality disorder as an adult (Copeland et al., 2013).

Internalizing symptoms . Much of the research on the effect of bullying on internalizing symptoms focuses on the effect of victimization. In a meta-analysis of 16 studies (Schoeler et al., 2018), the authors found that bullying victimization predicted internalizing symptoms, such as anxiety and depression—and that although the effect size was small, it was larger than the two other consequences that the study examined: externalizing symptoms and academic difficulties. The authors also pointed out that the adverse effects of victimization declined in the long term, indicating that bullying victimization may affect children's mental health in the short term, but there is potential for resilience over time. Another meta-analysis found that the probability of being depressed, up to 36 years later, was much higher for children who were bullied at school than for students who were not bullied (Ttofi et al., 2011). Bullying victimization was found to be a significant risk factor for later depression, even after the authors controlled for up to 20 major childhood risk factors.

Studies that examine cyberbullying specifically have found unfavorable psychosocial and behavioral outcomes related to both perpetration and victimization, including anxiety, depression, and loneliness (Kowalski et al., 2014). Also, researchers have found associations between cybervictimization and emotional problems, somatic symptom, and substance misuse, and have found associations between cyberbullying perpetration and lower levels of self-esteem and life satisfaction (Kowalski et al., 2014).

A meta-analysis examining several bullying roles found that children who bully others, who are victims, and who both bully others and are victims, were all at increased risk of suffering psychosomatic problems, compared with uninvolved peers (Gini and Pozzoli, 2008). Other studies have found that youth who both bully others and are victimized by bullying have an increased risk of depression, panic disorder, agoraphobia, and suicidality (Copeland et al. 2013).

Studies have also examined less common internalizing symptoms. For example, a study of 11 counties in North Carolina that followed participants between the ages of 9 and 16 found that, as adults, bullying victims had a higher prevalence of agoraphobia and panic disorder, compared with those who had not been victims (Copeland et al. 2013). A study of school-age children in 28 countries found that students who were bullied weekly were more likely to report feeling nervous, bad tempers, feeling low, difficulties in getting to sleep, morning tiredness, feeling left out, loneliness, and helplessness (Due et al., 2005).

Education-related difficulties . Several other consequences of bullying have been identified in the research literature, including difficulties related to school. These consequences include poor academic outcomes, truancy, reduced feelings of belonging at school, and greater likelihood of being a high school dropout (Cornell et al., 2013; Hysing et al., 2021; Moyano and Sanchez-Fuentes, 2020). For example, a study of high schools in Virginia found that both teacher-reported and student-reported prevalence of teasing and bullying predicted school dropout rates, even while controlling for school and community factors, such as school size, academic achievement, and community crime rates (Cornell et al., 2012). However, they did not find a relationship between student-reported victimization and dropout. A study of more than 10,000 students in Norway found that both victims and perpetrators of bullying had lower grade point averages than students uninvolved in bullying, but there was no difference in school absences (Hysing et al., 2021). Also, some researchers have examined whether school-related consequences vary by sexual orientation and gender identity. A study of more than 13,000 students found that students who identify as lesbian, gay, bisexual, transgender, and questioning (LGBTQ), as compared with straight-identified students, had more unexcused absences from school after bullying victimization (Robinson and Espelage, 2011).

Moderators and Interactive Protective Factors

In addition to studies that examine the direct consequences of bullying, many studies attempt to examine moderators or interactive protective factors. Moderators are variables that affect the strength of the relationship between a dependent and independent variable. Interactive protective factors can lessen the negative impact of bullying by interrupting the link between bullying and negative outcomes (Farrington and Ttofi, 2011; Hempill, Tollit, and Herrenkohl, 2014). Thus, interactive protective factors mitigate the impact of bullying after it has occurred. Many of these factors are the same as risk-based protective factors.

Age is one of the moderators most frequently examined in the bullying literature. For example, some meta-analyses revealed that the relationship between externalizing behaviors and bullying perpetration was stronger during the pre-adolescent years, compared with the adolescent years, and that internalizing behaviors predicted victimization for adolescents but not for younger children (Cook et al., 2010). Also, negative peer status was a significant predictor of bullying behavior during childhood, but not during adolescence (Cook et al., 2010). Another study found that while younger students punished youth who bullied by refusing to attribute status to them and providing negative reinforcement, older students awarded status to those who bully, providing positive reinforcement (van der Ploeg, Steglich, and Veenstra, 2019).

Family factors also have been found to moderate the effect of bullying perpetration and victimization on negative outcomes (Hemphill, Tollit, and Herrenkohl, 2014; Jantzer et al., 2015). One study found that while bullying behavior among youth ages 16 to 17 was associated with increased nonviolent antisocial behavior at ages 18 to 19, opportunities for prosocial involvement with their families lessened the impact (Hemphill, Tollit, and Herrenkohl, 2014). Another study found that among youth who were bullied, those that had higher levels of parental monitoring were less likely to engage in self-injurious and suicidal behavior, compared with victims who received less parental monitoring (Jantzer et al., 2015).

Other individual factors have been identified as interactive protective factors for victims of bullying. These include social problem-solving skills, academic performance, coping strategies, and temperament (Baldry and Farrington, 2005; Hemphill, Tollit, and Herrenkohl, 2014; Sugimora, Rudolph, and Agoston, 2014). For example, one study found that, among victims of bullying, those who reported strong adaptive stress/coping mechanisms and those who had high academic performance at ages 16 to 17 were less likely to be depressed in young adulthood (Hemphill, Tollit, and Herrenkohl, 2014).

Compared with research on bullying perpetration and victimization, a smaller body of literature examines bystanders who witness bullying. These studies generally examine either the effect of bullying on bystanders or the responses of bystanders. Researchers in several studies (e.g., Nickerson and Mele-Taylor, 2014; Salmivalli et al., 1996) have identified four bystander roles: 1) reinforcer to the person who is bullying (e.g., encourages, laughs), 2) assistant to the person who is bullying (e.g., joins in), 3) defender of the victim (e.g., comforts victim, tells teacher), and 4) outsider (e.g., is unaware of or ignores bullying). Given the potentially important role of bystanders, several bullying intervention programs include efforts to encourage bystanders to intervene to stop bullying (Polanin, Espelage, and Pigott, 2012).

Bystander Intervention Models

Researchers have used the bystander intervention model (Latane and Darley, 1970) to understand bystander actions (Jenkins and Nickerson, 2016; Menolascino and Jenkins, 2018). The bystander intervention model identifies five stages of an intervention: 1) notice the event, 2) interpret the event as an emergency that requires assistance, 3) accept responsibility for intervening, 4) know how to intervene or provide help, and 5) implement intervention decisions. Researchers have also developed a conceptual framework specific to bullying, suggesting that the decision to intervene depends on how bystanders define and evaluate the situation, the social context, and their own agency (Thornberg et al., 2012). Five themes related to bystander motives have emerged from qualitative research:

• Interpretation of harm in the bullying situation . The degree to which bystanders perceive the bullying situation as harmful to the victim influences their motivation to intervene. Researchers have found that bystanders are more likely to intervene when bullying is seen as harmful and nonroutine.
• Emotional reactions. Witnessing bullying can evoke different emotional reactions in bystanders, which can influence their decisionmaking processes. Bystanders who feel empathy are more likely to intervene, compared with bystanders who feel afraid of being victimized themselves or who enjoy the excitement of watching the bullying situation.
• Social evaluation occurs when the bystander considers existing social relationships and positions, such as friendship, social rank, and gender. For example, if the peer who is bullying is a person the bystander respects, the bystander may be less likely to intervene; if the victim is a friend, the bystander is more likely to intervene.
• Moral evaluation refers to judging the bullying situation in terms of right or wrong. It also includes the process of evaluating and attributing responsibility. Bystanders who believe bullying is wrong or who internalize a teacher's or coach's request to set a positive example to other students may be more likely to intervene. However, a bystander who blames the victim is less likely to intervene.
• Intervention self-efficacy refers to how effective bystanders believe their intervening actions would be (Thornberg et al., 2012).

Predictors of Bystander Intervention

Empirical research has identified several individual factors that may influence a bystander to defend a bullying victim, many of which align with the bystander intervention models described above. These include individual factors such as higher levels of empathy (cognitive, affective, and empathy in general) and better social skills (Barchia and Bussey, 2011; Gini et al., 2007; Jenkins et al., 2016; Menolascino and Jenkins, 2018). Some studies have found that children who were more likely to defend victims were more likely to report experiencing negative emotions like guilt and anger while witnessing bullying (Lambe et al., 2017; Malamut et al, 2021; Mazzone, Camodeca, and Salmivalli, 2016; Pronk et al., 2020). Other studies found that bystanders who defended victims were more likely to interpret bullying situations as an emergency and report greater knowledge on how to intervene (Jenkins and Nickerson, 2014). Finally, some studies have found that the strongest motivation for intervening was a sense of social justice (Cappadocia et al., 2012). This same study found that the strongest motivation for not intervening was a sense that it was not their place to intervene because the bullying situation did not directly involve them or was not severe.

Some studies, which have examined demographic factors such as gender and age, have found that girls and younger students are more likely to defend a victim than boys and older students (Lambe et al., 2017; Trach et al., 2010). However, other studies have found that boys are more likely to be defenders than girls (e.g., Nickerson and Mele-Taylor, 2014). Studies have also found differences in the influence of certain factors by gender (Cappadocia et al., 2012; Menolascino and Jenkins, 2018). For example, a study of 346 middle school students found that for boys, higher affective empathy was associated with a greater likelihood of interpreting bullying as an emergency and accepting responsibility for intervening. For girls, however, the perception of bullying as an emergency and accepting responsibility was stable regardless of their level of affective empathy (Menolascino and Jenkins, 2018).

Effects of Bullying on Bystanders

Studies have found that bystanders may later experience internalizing symptoms (Doumas and Midgett, 2021; Evans et al., 2018; Midgett and Doumas, 2019). For example, being a witness to bullying at school was associated with higher levels of anxiety and depression among a sample of middle school students (Midgett and Doumas, 2019), and witnessing cyberbullying was related to higher levels of anxiety, depression, and somatic complaints (Doumas and Midgett, 2020). Another study found that students ages 12 to 16 who reported witnessing bullying were more likely to also report somatic complaints, obsessive-compulsiveness, interpersonal sensitivity, depression, anxiety, hostility, paranoid ideation, psychoticism, non-clinical common concerns, and substance use (Rivers et al., 2009).

Finally, some studies have attempted to examine the impact of different types of bystander responses on the bystanders' outcomes. For example, a 5–year study of more than 8,000 middle and high school students, in North Carolina, found that bystanders who protected or defended victims later reported higher levels of self-esteem, academic achievement, and future optimism, compared with students who did not defend or protect victims (Evans et al., 2019). This study also found that bystanders who supported bullying perpetrators as reinforcers or assistants were more likely to later report aggression and poor academic achievement and less likely to report feeling optimism toward their futures. However, both types of bystanders reported internalizing symptoms, which suggests that witnessing bullying can negatively affect bystanders regardless of their actions.

Outcome Evidence

Meta-analyses of interventions to prevent and reduce bullying have been conducted by different researchers. Most of the outcome evidence is related to school-based interventions or interventions aimed at reducing cyberbullying. Some of these meta-analyses examined specific types of bullying, finding that interventions may have a greater impact on some types of bullying than others. For example, a meta-analysis examining the effectiveness of bullying prevention programs found that the programs were effective in reducing physical and relationship victimization but not in reducing verbal victimization (Kennedy, 2020).

School-Based Bullying Prevention Programs

School-based bullying prevention programs aim to prevent and reduce bullying in school settings. This includes reducing bullying perpetration and bullying victimization, creating positive school climates, and teaching about the important role of bystanders. Thus, the goals of school-based bullying prevention programs are usually to 1) prevent or reduce bullying perpetration, 2) prevent or reduce victimization, or 3) increase bystander intervention in bullying situations. Correspondingly, bullying prevention programs typically target those perpetrating, those being victimized, or those who are bystanders.

There are several meta-analyses examining the effect of bullying prevention programs in school settings. One examined the effects of 100 studies conducted from 1983–2016, involving more than 100,000 participants. The authors found a statistically significant reduction in bullying perpetration and bullying victimization (Gaffney, Ttofi, and Farrington, 2019). Another meta-analysis looked at the impact of bullying prevention programs on bystander behavior (Polanin et al., 2012). Based on 12 studies, the authors found that, after participating in the program, students were more likely to intervene in situations when they witnessed another student being bullied. However, this same meta-analysis also examined 8 studies that measured bystander empathy and found that the programs had no statistically significant effects on that outcome.

Many individual programs and interventions are included in these meta-analyses. For example, Positive Action is a curriculum-based approach to improve youth academics, behavior, and character. The intervention features interactive, ready-to-use kits that contain 15 to 20 minutes of scripted, user-friendly lessons for schools, families, and communities. The content concentrates on three core elements: 1) program philosophy; 2) the thoughts–actions–feelings circle; and 3) six content units on self-concept, positive actions for body and mind, social and emotional positive actions for managing oneself responsibly, social and emotional positive actions for getting along with others, social and emotional positive actions for being honest, and social and emotional positive actions for self-improvement. A study of the impact of the program on Chicago public school students in grade 3 found that students who participated in this intervention experienced several positive outcomes, including a statistically significant reduction in bullying behaviors, compared with youth in the comparison group (Li et al., 2011).

The Olweus Bullying Prevention program is a schoolwide, multicomponent intervention to reduce and prevent aggression and bullying among students. A study of this intervention in three urban public middle schools in the southeastern United States found that there was a statistically significant intervention effect on teachers' ratings of students' physical, verbal, and relational aggression and victimization. However, there was no statistically significant intervention effect on students' self-reports of physical and relational aggression and victimization (Farrell et al., 2018).

Parent-Involved Antibullying Programs for Youth

Parent-involved antibullying programs target school-age youth and their parents in interventions that provide training and information to reduce bullying and increase positive parenting practices. A meta-analysis of 16 studies, which examined the effects of parent-involved antibullying programs on several youth and parent outcomes, found that these programs reduced bullying victimization, reduced negative parenting, and improved positive parenting skills; however, there were no statistically significant effects on reducing youth depression (Chen, Zhu, and Chui, 2021).

One example, Stop School Bullying , is a preventative school-based program for elementary school students in grades 4 to 6, which is designed to involve students, educators, parents, and the community. The goal of the program was to increase awareness of the impact of bullying, increase empathy toward victims, and ultimately reduce rates of bullying and victimization. Teachers organized two meetings with parents throughout the intervention to enhance parental involvement and awareness. Also, leaflets were distributed to parents, students, teachers, and other community members throughout the surrounding area. Finally, a comprehensive website was developed that contained four microsites providing information for bullying prevention and awareness tailored to specific groups: students, teachers, parents, and the general community. The program showed a statistically significant reduction in bullying and victimization rates at schools that implemented the program, compared with a control group of schools that did not implement the program (Tsiantis et al., 2013).

Cyberbullying

Cyberbullying intervention and prevention programs use several different approaches to discourage students from engaging in online bullying and to build the capacity to respond to negative online behaviors. In general, these approaches can be categorized as individual-level, multi-level systemic, and universal or whole school. Systematic reviews of cyberbullying intervention and prevention programs have found statistically significant reductions in cyberbullying (Gaffney et al., 2019; Polanin et al., 2022). For example, a systematic review and meta-analysis of 50 studies examining outcomes for more than 45,000 students in K–12 settings found that school-based prevention programs showed statistically significant reductions in cyberbullying perpetration and victimization. Most of these programs incorporated skill-building activities, curricula and prepackaged materials, psychoeducational components to raise awareness of cyberbullying and increase knowledge of safety strategies, and/or multimedia materials to enhance student engagement. The review also found that programs that specifically target cyberbullying behavior were more effective in reducing cyberbullying, compared with general violence prevention programs (Polanin et al., 2022).

For example, the Prev@cib program was designed to decrease bullying and cyberbullying perpetration and victimization both in the classroom and in virtual environments. The program is based on three theoretical frameworks: 1) the ecological model, 2) the empowerment theory, and 3) the personal and social responsibility model. The program consists of 10, 1–hour sessions distributed in three modules related to information, awareness, and involvement. A study of 660 adolescents from four high schools in Valencia, Spain, found that there was a statistically significant decrease in bullying and victimization and cyberbullying and cybervictimization for youth who participated in the Prev@cib program, compared with the control group (Ortega-Barón et al., 2019).

Bullying is a common problem among children and youth that can result in adverse short-term and long-term consequences. Children and youth can be involved as perpetrators, victims, or as bystanders. They also can be involved in more than one role. Youth who engage in bullying behavior in school are more likely to commit violent and nonviolent offenses and become involved with the criminal justice system. Youth who are victims of bullying are more likely to experience anxiety, depression, substance misuse, and loneliness.

Researchers have identified many factors that increase the risk of bullying. These have been identified across all domains: individual, peer, school, family, and community. In the individual domain, risk factors for bullying perpetration include externalizing behaviors, low levels of empathy, moral disengagement, male gender, and low levels of social competence. Risk factors for victimization include being LGBTQ, being depressed, having underdeveloped social skills, and being overweight or obese. Risk factors from other domains include negative community, family, and school environments, and either rejection by peers or negative influences from peers. Several protective factors have been identified that reduce the likelihood of bullying and bullying victimization, including strong social problem-solving skills, favorable peer status, positive family relationships, and adequate parental monitoring.

Bystanders play an important role in bullying situations. Youth who witness bullying sometimes suffer some of the same negative consequences as victims, including anxiety and depression. A growing body of literature examines the predictors of bystander responses to bullying situations. For example, studies have found that bystanders who defend bullying victims are more likely to have higher levels of empathy and social skills, and experience guilt or anger at bullying situations, compared with bystanders who do not intervene.

Many interventions have been developed, implemented, and evaluated that attempt to prevent and reduce bullying. Most are implemented in schools. These programs vary in methods, scope, objectives, and the populations they target—and some have shown success. Specifically, research has found that 1) school-based programs can reduce bullying perpetration and victimization and increase bystander intervention in bullying situations, 2) programs that involve parents can be effective in reducing bullying and bullying victimization, and 3) programs aimed specifically at cyberbullying have demonstrated success in reducing both cyberbullying perpetration and victimization.

Acquah, E.O., Topalli, P.Z., Wilson, M.L., Junttila, N., and Niemi, P.M. 2016. Adolescent loneliness and social anxiety as predictors of bullying victimisation.  International Journal of Adolescence and Youth 21(3)320–331.

Anderson, C.A., and Bushman, B.J. 2002. Human aggression.  Annual Review of Psychology ,  53 (1):27–51.

Antoniadou, N. and Kokkinos, C.M. 2015. A review of research on cyber-bullying in Greece. International Journal of Adolescence and Youth 20(2):185–201.

Armitage, R. 2021. Bullying in children: Impact on child health.  BMJ Paediatrics Open 5(1). doi: doi:10.1136/bmjpo-2020-000939

Baldry, A.C., and Farrington, D.P. 2005. Protective factors as moderators of risk factors in adolescence bullying.  Social Psychology of Education 8(3):263–284.

Bandura, A. 1991. Social cognitive theory of moral thought and action. In Handbook of Moral Behavior and Development: Theory, Research and Applications , vol. 1, edited by W.M. Kurtines and G.L. Gewirtz. Hillsdale, NJ: Erlbaum, pp. 71–129.

Bandura, A. 1999. Moral disengagement in the perpetration of inhumanities. Personality and Social Psychology Review , 3, 193–209.

Barchia, K., and Bussey, K. 2011. Predictors of student defenders of peer aggression victims: Empathy and social cognitive factors.  International Journal of Behavioral Development 35(4):289–297.

Basile, K.C., Clayton, H.B., DeGue, S., Gilford, J.W., Vagi, K.J., Suarez, N.A., ... and Lowry, R. 2020. Interpersonal violence victimization among high school students—Youth Risk Behavior Survey, United States, 2019.  MMWR Supplements , 69(1): 28.

Bauman, S., and Del Rio, A. 2006. Preservice teachers’ responses to bullying scenarios: Comparing physical, verbal, and relational bullying.  Journal of Educational Psychology 98(1):219.

Bear, G., Yang, C., Mantz, L., Pasipanodya, E., Hearn, S., and Boyer, D. 2014. Technical Manual for Delaware School Survey: Scales of School Climate, Bullying Victimization, Student Engagement, and Positive, Punitive, and Social Emotional Learning Techniques. Newark, DE: Delaware Positive Behavior Support Project, University of Delaware Center for Disabilities Studies and the Delaware Department of Education.

Benedict, F.T., Vivier, P.M., and Gjelsvik, A. 2015. Mental health and bullying in the United States.  among children aged 6 to 17 years.  Journal of Interpersonal Violence 30(5):782–795.

Benedini, K.M., Fagan, A.A., and Gibson, C.L. 2016. The cycle of victimization: The relationship between childhood maltreatment and adolescent peer victimization. Child Abuse & Neglect 59:111–121.

Bevilacqua, L., Shackleton, N., Hale, D., Allen, E., Bond, L., Christie, D., ... and Viner, R.M. 2017. The role of family and school-level factors in bullying and cyberbullying: A cross-sectional study.  BMC Pediatrics 17(1):1–10.

Biswas, T., Scott, J.G., Munir, K., Thomas, H.J., Huda, M.M., Hasan, M.M., ... and Mamun, A.A. 2020. Global variation in the prevalence of bullying victimisation amongst adolescents: Role of peer and parental supports.  EClinicalMedicine , 20, 100276.

Book, A.S., Volk, A.A., and Hosker, A. 2012. Adolescent bullying and personality: An adaptive approach.  Personality and Individual Differences 52(2):218–223.

Borowsky, I.W., Taliaferro, L.A., and McMorris, B.J. 2013. Suicidal thinking and behavior among youth involved in verbal and social bullying: Risk and protective factors.  Journal of Adolescent Health 53(1):S4–S12.

Branson, Christopher E., and Cornell, D.G. 2009. A comparison of self and peer reports in the assessment of middle school bullying. Journal of Applied School Psychology 25(1):5–27.

Brixval, C.S., Rayce, S.L., Rasmussen, M., Holstein, B. E., and Due, P. 2012. Overweight, body image and bullying—an epidemiological study of 11-to 15-years olds.  The European Journal of Public Health 22(1):126–130.

Brooks-Gunn, J., Duncan, G.J., Klebanov, P.K., and Sealand, N. 1993. Do neighborhoods influence child and adolescent development? The American Journal of Sociology 99(2):353–395.

Byers, D.S., Mishna, F., and Solo, C. 2021. Clinical practice with children and adolescents involved in bullying and cyberbullying: gleaning guidelines from the literature.  Clinical Social Work Journal 49(1):20–34.

Camodeca, M., Caravita, S.C., and Coppola, G. 2015. Bullying in preschool: The associations between participant roles, social competence, and social preference.  Aggressive Behavior 41(4):310–321.

Cappadocia, M.C., Pepler, D., Cummings, J.G., and Craig, W. 2012. Individual motivations and characteristics associated with bystander intervention during bullying episodes among children and youth.  Canadian Journal of School Psychology 27(3):201–216.

Calvo-Morata, A., Alonso-Fernández, C., Freire, M., Martínez-Ortiz, I., and Fernández-Manjón, B. 2020. Serious games to prevent and detect bullying and cyberbullying: A systematic serious games and literature review.  Computers and Education  157, 103958.

Caravita, S. C., Strohmeier, D., Salmivalli, C., and Di Blasio, P. 2019. Bullying immigrant versus non-immigrant peers: Moral disengagement and participant roles.  Journal of School Psychology 75, 119–133.

Casper, D.M., Card, N.A., and Barlow, C. 2020. Relational aggression and victimization during adolescence: A meta-analytic review of unique associations with popularity, peer acceptance, rejection, and friendship characteristics.  Journal of Adolescence 80, 41–52.

Cassidy, T. (2009). Bullying and victimisation in school children: The role of social identity, problem-solving style, and family and school context.  Social Psychology of Education  12(1): 63-76.

[CDC] Centers for Disease Control and Prevention. n.d.a. 1991-2019 High School Youth Risk Behavior Survey Data. Retrieved August 6, 2022, from http://nccd.cdc.gov/youthonline/

CDC. n.d.b. 1991-2019 Middle School Youth Risk Behavior Survey Data. Retrieved August 6, 2022, from  http://nccd.cdc.gov/youthonline/  

CDC. 2019. 2019 State and Local Youth Risk Behavior Survey. Retrieved January 23, 2023, from https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2019/2019_YRBS-Standard-HS-Questionnaire.pdf

CDC. 2020. Youth Risk Behavior Survey Data Summary & Trends Report: 2009–2019. Retrieved from https://www.cdc.gov/healthyyouth/data/yrbs/pdf/YRBSDataSummaryTrendsReport2019-508.pdf

Cecen-Celik, H., and Keith, S. 2019. Analyzing predictors of bullying victimization with routine activity and social bond perspectives.  Journal of Interpersonal Violence 34(18):3807–3832.

Chen, Q., Zhu, Y., and Chui, W.H. 2021. A meta-analysis on effects of parenting programs on bullying prevention.  Trauma, Violence, and Abuse  22(5):1209–1220.

Child and Adolescent Health Measurement Initiative. n.d. 2019-2020 National Survey of Children's Health (NSCH) Data Query . Data Resource Center for Child and Adolescent Health supported by the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB). Retrieved August 4, 2022, from www.childhealthdata.org .

Child and Adolescent Health Measurement Initiative. 2021. "Fast Facts: 2019-2020 National Survey of Children's Health." Data Resource Center for Child and Adolescent Health, supported by the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB). Retrieved August 4, 2022, from https://www.childhealthdata.org/docs/default-source/nsch-docs/2019-2020-nsch-fast-facts-cahmi.pdf?sfvrsn=8fc75f17_2

Connolly, I., and O’Moore, M. 2003. Personality and family relations of children who bully.  Personality and Individual Differences 35(3):559–567.

Cook, C., Williams, K.R., Guerra, N.G., Kim, T.E., and Sadek, S. 2010. Predictors of bullying and victimization in childhood and adolescence: A meta-analytic investigation. School Psychology Quarterly 25(2):65–83.

Copeland, W.E., Wolke, D., Angold, A., and Costello, E.J. 2013. Adult psychiatric outcomes of bullying and being bullied by peers in childhood and adolescence . JAMA Psychiatry 70(4):419–426.

Cornell, D., Gregory, A., Huang, F., and Fan, X. 2013. Perceived prevalence of teasing and bullying predicts high school dropout rates.  Journal of Educational Psychology 105(1):138–149.

Crick, N.R., and Grotpeter, J.K. 1995. Relational aggression, gender, and social-psychological adjustment.  Child Development 66(3):710–722.

Daley, A., Solomon, S., Newman, P.A., and Mishna, F. 2007. Traversing the margins: Intersectionalities in the bullying of lesbian, gay, bisexual and transgender youth.  Journal of Gay and Lesbian Social Services 19(3-4):9–29.

de Bruyn, E.H., Cillessen, A.H., and Wissink, I.B. 2010. Associations of peer acceptance and perceived popularity with bullying and victimization in early adolescence.  The Journal of Early Adolescence 30(4):543–566.

Dehue, F., Bolman, C., Völlink, T., and Pouwelse, M. 2012. Cyberbullying and traditional bullying in relation with adolescents' perception of parenting.  Journal of Cybertherapy & Rehabilitation  5(1):25–33.

Development Services Group, Inc. 2015a. "Risk Factors for Delinquency." Literature review. Washington, D.C.: Office of Juvenile Justice and Delinquency Prevention. https://www.ojjdp.gov/mpg/litreviews/Risk%20Factors.pdf

Development Services Group, Inc. 2015b. "Protective Factors for Delinquency." Literature review. Washington, D.C.: Office of Juvenile Justice and Delinquency Prevention. https://www.ojjdp.gov/mpg/litreviews/Protective%20Factors.pdf  

Dishion, T.J., and Tipsord, J.M. 2011. Peer contagion in child and adolescent social and emotional development.  Annual Review of Psychology 62:189.

Doty, J.L., Lynne, S.D., Metz, A. S., Yourell, J.L., and Espelage, D.L. 2021. Bullying perpetration and perceived parental monitoring: A random intercepts cross-lagged panel model.  Youth & Society 53(8):1287-1310.

Doumas, D.M., and Midgett, A. 2020. Witnessing cyberbullying and internalizing symptoms among middle school students.  European Journal of Investigation in Health, Psychology and Education 10(4):957–966.

Doumas, D.M., and Midgett, A. 2021. The association between witnessing cyberbullying and depressive symptoms and social anxiety among elementary school students.  Psychology in the Schools 58(3):622–637.

Dresler-Hawke, E., and Whitehead, D. 2009. The behavioral ecological model as a framework for school-based anti-bullying health promotion interventions.  The Journal of School Nursing 25(3):195–204.

Due, P., Holstein, B.E., Lynch, J., Diderichsen, F., Nic Gabhain, S., Scheidt, P., Currie, C., and The Health Behaviour in School-Aged Children Bullying Working Group. 2005. Bullying and symptoms among school-aged children: International comparative cross sectional study in 28 countries. European Journal of Public Health 15(2):128–132.

Duncan, N. 1999. Sexual Bullying: Gender Conflict and Pupil Culture in Secondary Schools . Routledge.

Earnshaw, V.A., Reisner, S.L., Menino, D.D., Poteat, V.P., Bogart, L.M., Barnes, T.N., and Schuster, M.A. 2018. Stigma-based bullying interventions: A systematic review.  Developmental Review 48, 178–200.

Eisenberg, M.E., and Aalsma, M.C. 2005. Bullying and peer victimization: Position paper of the Society for Adolescent Medicine.  Journal of Adolescent Health 36(1):88–91.

Eisenberg, M.E., Gower, A.L., McMorris, B.J., Rider, G.N., and Coleman, E. 2019. Emotional distress, bullying victimization, and protective factors among transgender and gender diverse adolescents in city, suburban, town, and rural locations.  The Journal of Rural Health 35(2):270–281.

Ellis, B.J., Volk, A.A., Gonzalez, J.M., and Embry, D. 2016. The meaningful roles intervention: An evolutionary approach to reducing bullying and increasing prosocial behavior.  Journal of Research on Adolescence 26(4):622–637.

Eriksen, I.M. 2018. The power of the word: Students’ and school staff's use of the established bullying definition.  Educational Research 60(2):157–170.

Ersoy, M.E.D.E. 2016. Examining the relationship of social skills, problem solving and bullying in adolescents.  International Online Journal of Educational Sciences , 1309–2707.

Espelage, D.L. 2015. Bullying and K-12 students. In LGBTQ Issues in Education: Advancing a Research Agenda , edited by G.G. Wimberly. Washington, DC: American Educational Research Association, pp. 105-120.

Espelage, D.L., Bosworth, K., and Simon, T.R. 2000. Examining the social context of bullying behaviors in early adolescence.  Journal of Counseling & Development 78(3):326–333.

Evangelio, C., Rodriguez-Gonzalez, P., Fernandez-Rio, J., and Gonzalez-Villora, S. 2022. Cyberbullying in elementary and middle school students: A systematic review.  Computers and Education 176, 104356.

Evans, C.B., Smokowski, P.R., Rose, R.A., Mercado, M.C., and Marshall, K.J. 2019. Cumulative bullying experiences, adolescent behavioral and mental health, and academic achievement: An integrative model of perpetration, victimization, and bystander behavior.  Journal of Child and Family Studies 28(9):2415–2428.

Farrell, A.D., Sullivan, T.N., Sutherland, K.S., Corona, R., and Masho, S. 2018. Evaluation of the Olweus Bully Prevention Program in an urban school system in the USA. Prevention Science 19, 833–847.

Farrell, A.H., Della Cioppa, V., Volk, A.A., and Book, A.S. 2014. Predicting bullying heterogeneity with the HEXACO model of personality.  International Journal of Advances in Psychology 3(2):30–39.

Farrington, David P., and Maria M. Ttofi. 2011. Bullying as a predictor of offending, violence and later life outcomes. Criminal Behaviour and Mental Health 21:90–98.

Ferguson, C.J., and Dyck, D. 2012. Paradigm change in aggression research: The time has come to retire the general aggression model.  Aggression and Violent Behavior 17(3):220–228.

Ferguson, C.J., San Miguel, C., Koburn, Jr., J.C., and Sanchez, P. 2007. The effectiveness of school-based anti-bullying programs: A meta-analytic review. Criminal Justice Review 3(4):401–414.

Fergusson, D.M., Boden, J.M., and Horwood, L.J. 2014. Bullying in childhood, externalizing behaviors, and adult offending: Evidence from a 30-year study.  Journal of School Violence 13(1):146–164.

Fitzpatrick, S., and Bussey, K. 2011. The development of the social bullying involvement scales.  Aggressive Behavior 37(2):177–192.

Fortner, L.A. 2012. The relationship between childhood bullying victimization and social competence in emerging adulthood. The Florida State University.

Fredrick, S.S., Jenkins, L.N., and Ray, K. 2020. Dimensions of empathy and bystander intervention in bullying in elementary school.  Journal of School Psychology 79, 31–42.

Gaffney, H., Ttofi, M.M., and Farrington, D.P. 2019. Evaluating the effectiveness of school-bullying prevention programs: An updated meta-analytical review.  Aggression and Violent Behavior  45, 111–133.

Gaffney, H., Farrington, D.P., Espelage, D.L., and Ttofi, M.M. 2019. Are cyberbullying intervention and prevention programs effective? A systematic and meta-analytical review.  Aggression and Violent Behavior  45, 134–153.

Garaigordobil, M.G., Larrain, E.L., Garaigordobil, M., and Larrain, E. 2020. Bullying and cyberbullying in LGBT adolescents: Prevalence and effects on mental health. Comunicar Media Education Research Journal 28(1).

Garnett, B.R., Masyn, K.E., Austin, S.B., Miller, M., Williams, D.R., and Viswanath, K. 2014. The intersectionality of discrimination attributes and bullying among youth: An applied latent class analysis. Journal of Youth and Adolescence 43(8):1225–1239.

Gendron, B.P., Williams, K.R., and Guerra, N.G. 2011. An analysis of bullying among students within schools: Estimating the effects of individual normative beliefs, self-esteem, and school climate. Journal of School Violence 10(2):150–164.

Georgiou, S.N. 2008. Bullying and victimization at school: The role of mothers.  British Journal of Educational Psychology 78(1):109–125.

Georgiou, S.N., and Stavrinides, P. 2013. Parenting at home and bullying at school.  Social Psychology of Education 16(2):165–179.

Gini, G., Albiero, P., Benelli, B., and Altoe, G. 2007. Does empathy predict adolescents’ bullying and defending behavior?  Aggressive Behavior: Official Journal of the International Society for Research on Aggression 33(5):467–476.

Gini, G., and Pozzoli, T. 2008. Association between bullying and psychosomatic problems: A meta-analysis. Pediatrics 123, 1059–1065.

Gini, G., Pozzoli, T., and Hymel, S. 2014. Moral disengagement among children and youth: A meta ‐ analytic review of links to aggressive behavior.  Aggressive Behavior 40(1):56–68.

Gladden, R.M., Vivolo-Kantor, A.M., Hamburger, M.E., and Lumpkin, C.D. 2014 . Bullying Surveillance Among Youths: Uniform Definitions for Public Health and Recommended Data Elements, Version 1.0 . Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention and U.S. Department of Education.

Goldweber, A., Waasdorp, T.E., and Bradshaw, C.P. 2013. Examining associations between race, urbanicity, and patterns of bullying involvement.  Journal of Youth and Adolescence 42(2):206–219.

Graham, S. 2021. Exploration of identity-based bullying by race/ethnicity and other marginalized identities among adolescents.  JAMA Network Open 4(7): e2117827–e2117827.

Hanish, L.D., Eisenberg, N., Fabes, R. A., Spinrad, T.L., Ryan, P., and Schmidt, S. 2004. The expression and regulation of negative emotions: Risk factors for young children's peer victimization.  Development and Psychopathology  16(2):335–353.

Hanish, L.D., and Guerra, N.G. 2000. Predictors of peer victimization among urban youth.  Social Development  9(4):521–543.

Haynie, D.L., Nansel, T., Eitel, P., Crump, A.D., Saylor, K., Yu, K., and Simons-Morton, B. 2001. Bullies, victims, and bully/victims: Distinct groups of at-risk youth.  The Journal of Early Adolescence 21(1):29–49.

Hemphill, S.A., Tollit, M., and Herrenkohl, T.I. 2014. Protective factors against the impact of school bullying perpetration and victimization on young adult externalizing and internalizing problems.  Journal of School Violence 13(1):125–145.

Hightow-Weidman, L.B., Phillips, G., Jones, K.C., Outlaw, A.Y., Fields, S.D., and Smith, J.C. 2011. Racial and sexual identity-related maltreatment among minority YMSM: Prevalence, perceptions, and the association with emotional distress.  AIDS Patient Care and STDs 25:S39–S45.

Hirschi, T. 1969. Causes of Delinquency . Berkeley, CA: University of California Press

Hoeve, M., Dubas, J.S., Eichelsheim, V.I., van der Laan, P.H., Smeenk, W., and Gerris, J.R.M. 2009. The relationship between parenting and delinquency: A meta-analysis. Journal of Abnormal Child Psychology 37:749–75.

Hoeve, M., Stams, G.J.J.M, van der Put, C.E., Dubas, J.S., van der Laan, P.H., and Gerris, J.R.M. 2012. A meta-analysis of attachment to parents and delinquency. Journal of Abnormal Child Psychology 40(5):771–85.

Hodges, E.V., and Perry, D.G. 1999. Personal and interpersonal antecedents and consequences of victimization by peers.  Journal of Personality and Social Psychology 76(4):677.

Hong, J.S., Davis, J.P., Sterzing, P.R., Yoon, J., Choi, S., and Smith, D.C. 2014. A conceptual framework for understanding the association between school bullying victimization and substance misuse.  American Journal of Orthopsychiatry 84(6):696.

Hong, J.S., and Espelage, D.L. 2012. A review of research on bullying and peer victimization in school: An ecological system analysis.  Aggression and Violent Behavior 17(4):311 – 322.

Huesmann L.R., and Guerra N.G. 1997. Children’s normative beliefs about aggression and aggressive behavior. Journal of Personality and Social Psychology 72(2):408–419.

Hymel, S., Schonert–Reichl, K.A., Bonanno, R.A., Vaillancourt, T., and Rocke Henderson, N. 2010. Bullying and morality. Understanding how good kids can behave badly. In Handbook of Bullying in Schools: An International Perspective, edited by S.R. Jimerson, S.M. Swearer, and D.L. Espelage. New York, NY: Routledge, pp. 101–118.

Hysing, M., Askeland, K.G., La Greca, A.M., Solberg, M.E., Breivik, K., and Sivertsen, B. 2021. Bullying involvement in adolescence: implications for sleep, mental health, and academic outcomes.  Journal of Interpersonal Violence 36(17–18). doi:10.1177/0886260519853409

Irwin, V., Wang, K., Cui, J., and Thompson, A. 2022. Report on Indicators of School Crime and Safety: 2021 (NCES 2022-092/NCJ 304625). National Center for Education Statistics, U.S. Department of Education, and Bureau of Justice Statistics, Office of Justice Programs, U.S. Department of Justice. Washington, DC.

Islam, M.I., Yunus, F.M., Kabir, E., and Khanam, R. 2022. Evaluating risk and protective factors for suicidality and self-harm in Australian adolescents with traditional bullying and cyberbullying victimizations.  American Journal of Health Promotion 36(1):73–83.

Jackman, K., Kreuze, E.J., Caceres, B.A., and Schnall, R. 2020. Bullying and peer victimization of minority youth: Intersections of sexual identity and race/ethnicity.  Journal of School Health 90(5):368–377.

Jadambaa, A., Thomas, H.J., Scott, J.G., Graves, N., Brain, D., and Pacella, R. 2019. Prevalence of traditional bullying and cyberbullying among children and adolescents in Australia: A systematic review and meta-analysis.  Australian and New Zealand Journal of Psychiatry 53(9):878–888.

Janssen, I., Craig, W.M., Boyce, W.F., and Pickett, W. 2004. Associations between overweight and obesity with bullying behaviors in school-aged children. Pediatrics 113(5):1187–1194.

Jantzer, V., Haffner, J., Parzer, P., Resch, F., and Kaess, M. 2015. Does parental monitoring moderate the relationship between bullying and adolescent nonsuicidal self-injury and suicidal behavior? A community-based self-report study of adolescents in Germany.  BMC Public Health 15(1):1–8.

Jenkins, L.N., Demaray, M.K., Fredrick, S.S., and Summers, K.H. 2016. Associations among middle school students’ bullying roles and social skills.  Journal of School Violence 15(3), 259–278.

Jenkins, L.N., and Nickerson, A.B. 2017. Bullying participant roles and gender as predictors of bystander intervention.  Aggressive Behavior 43(3):281–290.

Jiang, H., Liang, H., Zhou, H., and Zhang, B. 2022. Relationships among normative beliefs about aggression, moral disengagement, self-control and bullying in adolescents: a moderated mediation model.  Psychology Research and Behavior Management , 15, 183.

Johansson, S., and Englund, G. 2021. Cyberbullying and its relationship with physical, verbal, and relational bullying: A structural equation modelling approach.  Educational Psychology 41(3):320–337.

Johns, M.M., Lowry, R., Haderxhanaj, L.T., Rasberry, C.N., Robin, L., Scales, L., ... and Suarez, N.A. 2020. Trends in violence victimization and suicide risk by sexual identity among high school students—Youth Risk Behavior Survey, United States, 2015–2019.  MMWR Supplements 69(1):19.

Juvonen, J., Graham, S., and Schuster, M.A. 2003. Bullying among young adolescents: The strong, the weak, and the troubled.  Pediatrics 112(6):1231–1237.

Kaufman, J.M. 2005. Explaining the race/ethnicity–violence relationship: Neighborhood context and social psychological processes. Justice Quarterly 22(2):224–51.

Kennedy, R.S. 2020. A meta-analysis of the outcomes of bullying prevention programs on subtypes of traditional bullying victimization: Verbal, relational, and physical.  Aggression and Violent Behavior 55,101485.

Kljakovic, M., and Hunt, C. 2016. A meta-analysis of predictors of bullying and victimisation in adolescence.  Journal of Adolescence 49:134–145.

Klomek, A.B., Sourander, A., and Elonheimo, H. 2015. Bullying by peers in childhood and effects on psychopathology, suicidality, and criminality in adulthood.  The Lancet Psychiatry 2(10):930–941.

Kochenderfer-Ladd, B. 2003. Identification of aggressive and asocial victims and the stability of their peer victimization.  Merrill-Palmer Quarterly 49(4): 401 – 425.

Kofoed, J., and Staksrud, E. 2019. ‘We always torment different people, so by definition, we are no bullies’: The problem of definitions in cyberbullying research.  New Media & Society 21(4):1006–1020.

Kosciw, J.G., Greytak, E.A., Zongrone, A.D., Clark, C.M., and Truong, N.L. 2018. The 2017 National School Climate Survey: The Experiences of Lesbian, Gay, Bisexual, Transgender, and Queer Youth in our Nation's Schools. New York, NY: GLSEN.

Kowalski, R.M., Giumetti, G.W., Schroeder, A.N., and Lattanner, M.R. 2014. Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth.  Psychological Bulletin  140(4):1073.

Lambe, L.J., Hudson, C.C., Craig, W.M., and Pepler, D.J. 2017. Does defending come with a cost? Examining the psychosocial correlates of defending behaviour among bystanders of bullying in a Canadian sample.  Child Abuse and Neglect 65:112–123.

Larke, I.D., and Beran, T.N. 2006. The relationship between bullying and social skills in primary school students.  Issues in Educational Research 16(1):38–51.

Latane, B., and Darley, J.M. 1970. The Unresponsive Bystander: Why Doesn’t He Help? Englewood Cliffs, NJ: Prentice Hall.

Lebrun-Harris, L.A., Sherman, L.J., Limber, S.P., Miller, B.D., and Edgerton, E.A. 2019. Bullying victimization and perpetration among US children and adolescents: 2016 National Survey of Children’s Health.  Journal of Child and Family Studies 28(9):2543 – 2557.

Lereya, S.T., Samara, M., and Wolke, D. 2013. Parenting behavior and the risk of becoming a victim and a bully/victim: A meta-analysis study.  Child Abuse and Neglect 37(12):1091– 1108.

Lewis, C., Deardorff, J., Lahiff, M., Soleimanpour, S., Sakashita, K., and Brindis, C.D. 2015. High school students’ experiences of bullying and victimization and the association with school health center use. Journal of School Health 85(5):318–326.

Limber, Susan P. 2003. Efforts to address bullying in U.S. schools. American Journal of Health Education 34(5):S23–S29.

Liu, J, and Graves, N. 2011. Childhood bullying: A review of constructs, concepts, and nursing implications. Public Health Nursing 28(6):556–568.

Liu, J., Guo, S., Weissman, R., and Liu, H. 2021. Investigating factors associated with bullying utilizing latent class analysis among adolescents.  School Psychology International 42(1):11–32.

Lösel, F., and Farrington, D. 2012. Direct protective and buffering protective factors in the development of youth violence. American Journal of Preventive Medicine 43(2S1):S8–S23.

Low, S., and Espelage, D. 2013. Differentiating cyber bullying perpetration from non-physical bullying: Commonalities across race, individual, and family predictors.  Psychology of Violence 2(1):39.

Lumeng, J.C., Forrest, P., Appugliese, D.P., Kaciroti, N., Corwyn, R.F., and Bradley, R.H. 2010. Weight status as a predictor of being bullied in third through sixth grades.  Pediatrics  125(6):e1301–e1307.

Machimbarrena, J. M., and Garaigordobil, M. 2018. Prevalence of bullying and cyberbullying in the last stage of primary education in the Basque Country.  The Spanish Journal of Psychology  21.

Malamut, S. T., Trach, J., Garandeau, C. F., and Salmivalli, C. 2021. Examining the potential mental health costs of defending victims of bullying: A longitudinal analysis.  Research on Child and Adolescent Psychopathology 49(9):1197–1210.

Mann, M. J., Kristjansson, A. L., Sigfusdottir, I. D., and Smith, M. L. 2015. The role of community, family, peer, and school factors in group bullying: Implications for school ‐ based intervention.  Journal of School Health 85(7):477-486.

Mazzone, A., Camodeca, M., and Salmivalli, C. 2016. Interactive effects of guilt and moral disengagement on bullying, defending and outsider behavior.  Journal of Moral Education 45(4):419–432.

McDonald, R.I., and Crandall, C.S. 2015. Social norms and social influence.  Current Opinion in Behavioral Sciences 3:147–151.

Menolascino, N., and Jenkins, L.N. 2018. Predicting bystander intervention among middle school students.  School Psychology Quarterly 33(2):305.

Menesini, E., and Salmivalli, C. 2017. Bullying in schools: the state of knowledge and effective interventions.  Psychology, Health & Medicine 22(sup1):240–253.

Meter, D.J., and Card, N.A. 2015. Defenders of victims of peer aggression: Interdependence theory and an exploration of individual, interpersonal, and contextual effects on the defender participant role.  Developmental Review 38:222–240.

Midgett, A., and Doumas, D.M. 2019. Witnessing bullying at school: The association between being a bystander and anxiety and depressive symptoms.  School Mental Health 11(3):454-463.

Milnes, K., Turner-Moore, T., Gough, B., Denison, J., Gatere, L., Haslam, C., ... and Zoppi, I. 2015. Sexual bullying in young people across five European countries: Research report for the Addressing Sexual Bullying Across Europe (ASBAE) project.

Mitsopoulou, E., and Giovazolias, T. 2015. Personality traits, empathy and bullying behavior: A meta-analytic approach.  Aggression and Violent Behavior  21:61–72.

Modecki, K.L., Minchin, J., Harbaugh, A.G., Guerra, N.G., and Runions, K.C. 2014. Bullying prevalence across contexts: A meta-analysis measuring cyber and traditional bullying. Journal of Adolescent Health , 55(5):602 – 611.

Moore, S.E., Norman, R.E., Suetani, S., Thomas, H.J., Sly, P.D., and Scott, J.G. 2017. Consequences of bullying victimization in childhood and adolescence: A systematic review and meta-analysis.  World Journal of Psychiatry 7(1):60–76.

Moyano, N., and del Mar Sanchez-Fuentes, M. 2020. Homophobic bullying at schools: A systematic review of research, prevalence, school-related predictors, and consequences.  Aggression and Violent Behavior  53. doi:10.1016/j.avb.2020.101441

Muris, P., Jeurissen, A., Rooswinkel, M., and Meesters, C. 2022. Good traits, bad traits, and ‘ugly’ behavior: Relations between the dark triad, honesty-humility, other HEXACO personality traits, and externalizing problems in adolescents.  Journal of Child and Family Studies 1 –11.

National Academies of Sciences, Engineering, and Medicine. 2016. Preventing Bullying Through Science, Policy, and Practice. Washington, DC: The National Academies Press.

[NCES] National Center for Education Statistics. 2022. Bullying at school and electronic bullying.  The Condition of Education . U.S. Department of Education, Institute of Education Sciences. Retrieved August 4, 2022, from  https://nces.ed.gov/programs/coe/indicator/a10 .

Nickerson, A.B., and Mele-Taylor, D. 2014. Empathetic responsiveness, group norms, and prosocial affiliations in bullying roles.  School Psychology Quarterly  29(1): 99.

Obermann, M.L. 2011. Moral disengagement in self-reported and peer-nominated school bullying. Aggressive Behavior 37:133–144.

Olsen, N.E. 2010.  Bullying trends and reporting preferences among an urban, suburban, and rural school . Dissertation submitted to Brigham Young University.

Olweus, D., and Limber, S.P. (2010, November). What do we know about bullying: Information from the Olweus Bullying Questionnaire. Paper presented at the meeting of the International Bullying Prevention Association, Seattle, WA, November 2010 .

Olweus, D., and Limber, S.P. 2018. Some problems with cyberbullying research.  Current Opinion in Psychology 19:139–143.

Ortega-Barón, J., Buelga, S., Ayllón, E., Martínez-Ferrer, B., and Cava, M.J. 2019. Effects of intervention program Prev@cib on traditional bullying and cyberbullying.  International Journal of Environmental Research and Public Health  16:527–540.

Ostrov, J.M., Kamper-DeMarco, K.E., Blakely-McClure, S.J., Perry, K.J., and Mutignani, L. 2019. Prospective associations between aggression/bullying and adjustment in preschool: Is general aggression different from bullying behavior?  Journal of Child and Family Studies 28(9):2572–2585.

Peguero, A.A. 2008. Bullying victimization and extracurricular activity.  Journal of School Violence 7(3):71–85.

Peguero, A.A. 2019. Introduction to the special issue on significance of race/ethnicity in bullying.  International Journal of Bullying Prevention 1(3):159–160.

Pepler, D.J., Craig, W.M., Connolly, J.A., Yuile, A., McMaster, L., and Jiang, D. 2006. A developmental perspective on bullying.  Aggressive Behavior: Official Journal of the International Society for Research on Aggression 32(4):376–384.

Perkins, H.W., Craig, D.W., and Perkins, J.M. 2011. Using social norms to reduce bullying: A research intervention among adolescents in five middle schools.  Group Processes & Intergroup Relations 14(5):703–722.

Pichel, R., Foody, M., O’Higgins Norman, J., Feijóo, S., Varela, J., and Rial, A. 2021. Bullying, cyberbullying and the overlap: What does age have to do with it?  Sustainability 13(15):8527.

Polanin, J.R., Espelage, D.L., and Pigott, T.D. 2012. A meta-analysis of school-based bullying prevention programs’ effects on bystander intervention behavior. School Psychology Review 41(1):47–65.

Polanin, J.R., Espelage, D.L., Grotpeter, J.K., Ingram, K., Michaelson, L., Spinney, E., ... and Robinson, L. 2021. A systematic review and meta-analysis of interventions to decrease cyberbullying perpetration and victimization.  Prevention Science, 1–16.

Pornari, C.D., and Wood, J. 2010. Peer and cyber aggression in secondary school students: The role of moral disengagement, hostile attribution bias, and outcome expectancies. Aggressive Behavior 36:81–9.

Postigo, S., González, R., Montoya, I., and Ordoñez, A. 2013. Theoretical proposals in bullying research: A review.  Anales de psicología , 29(2):413-425.

Price, M., Polk, W., Hill, N.E., Liang, B., and Perella, J. 2019. The intersectionality of identity-based victimization in adolescence: A person-centered examination of mental health and academic achievement in a U.S. high school.  Journal of Adolescence  76:185–196.

Pronk, J., Olthof, T., Aleva, E.A., van der Meulen, M., Vermande, M.M., and Goossens, F.A. 2020. Longitudinal associations between adolescents' bullying ‐ related indirect defending, outsider behavior, and peer ‐ group status.  Journal of Research on Adolescence 30:87–99.

Pronk, J., Olthof, T., de Vries, R.E., and Goossens, F.A. 2021. HEXACO personality correlates of adolescents’ involvement in bullying situations. Aggressive Behavior 47(3):320–331.

Protogerou, C., and Flisher, A. 2012. Bullying in schools.  Crime, Violence and Injury in South Africa: 21st Century Solutions for Child Safety , 119–133.

Puhl, R.M., Luedicke, J., and Heuer, C. 2011. Weight ‐ based victimization toward overweight adolescents: Observations and reactions of peers.  Journal of School Health 81(11):696–703.

Puhl, R.M., Latner, J.D., O’Brien, K., Luedicke, J., Forhan, M., and Danielsdottir, S. 2016. Cross ‐ national perspectives about weight ‐ based bullying in youth: Nature, extent and remedies.  Pediatric Obesity 11(4):241–250.

Reijntjes, A., Kamphuis, J.H., Prinzie, P., Boelen, P.A., van der Schoot, M., and Telch, M.J. 2011. Prospective linkages between peer victimization and externalizing problems in children: A meta-analysis. Aggressive Behavior 37:215–222.

Reijntjes, A., Kamphuis, J.H., Prinzie, P., and Telch, M.J. 2010. Peer victimization and internalizing problems in children: A meta-analysis of longitudinal studies. Child Abuse and Neglect 34:244–252.

Reijntjes, A., Vermande, M., Thomaes, S., Goossens, F., Olthof, T., Aleva, L., and Van der Meulen, M. 2016. Narcissism, bullying, and social dominance in youth: A longitudinal analysis.  Journal of Abnormal Child Psychology 44(1):63–74.

Reingle, J.M., Jennings, W.G., and Maldonado-Molina, M.M. 2012. Risk and protective factors for trajectories of violent delinquency among a nationally representative sample of early adolescents. Youth Violence and Juvenile Justice 10(3):261–77.

Ribeaud, D., and Eisner, M. 2010. Are moral disengagement, neutralization techniques, and self-serving cognitive distortions the same? Developing a unified scale of moral neutralization of aggression. International Journal of Conflict and Violence 4(2):298–315.

Rivers, I., Poteat, V.P., Noret, N., and Ashurst, N. 2009. Observing bullying at school: The mental health implications of witness status.  School Psychology Quarterly 24(4):211–223.

Robinson, J.P., and Espelage, D.L. 2011. Inequities in educational and psychological outcomes between LGBTQ and straight students in middle and high school. Educational Researcher 40:315–330.

Russell, S.T., Sinclair, K.O., Poteat, V.P., and Koenig, B.W. 2012. Adolescent health and harassment based on discriminatory bias.  American Journal of Public Health 102(3):493–495.

Salmivalli, C. 2010. Bullying and the peer group: A review.  Aggression and Violent Behavior 15(2):112–120.

Salmivalli, C., Lagerspetz, K., Bjorkqvist, K., Osterman, K., and Kaukiainen, A. 1996. Bullying as a group process: Participant roles and their relations to social status within the group. Aggressive Behavior 22:1–15.

Sandstrom, M.J., and Bartini, M. 2010. Do perceptions of discrepancy between self and group norms contribute to peer harassment at school? Basic and Applied Social Psychology , 32(3):217–225.

Sawyer, A.L., Bradshaw, C.P., and O’Brennan, L.M. 2008. Examining ethnic, gender, and developmental differences in the way children report being a victim of “bullying” on self-report measures. Journal of Adolescent Health 43(2):106–114.

Schoeler, T., Duncan, L., Cecil, C.M., Ploubidis, G.B., and Pingault, J.B. 2018. Quasi-experimental evidence on short-and long-term consequences of bullying victimization: A meta-analysis.  Psychological Bulletin 144(12):1229–1246.

Shetgiri, R., Lin, H., and Flores, G. 2013. Trends in risk and protective factors for child bullying perpetration in the United States.  Child Psychiatry and Human Development 44(1):89–104.

Sugimura, N., Rudolph, K.D., and Agoston, A.M. 2014. Depressive symptoms following coping with peer aggression: The moderating role of negative emotionality.  Journal of Abnormal Child Psychology 42(4):563–575.

Smith, P.K., López-Castro, L., Robinson, S., and Görzig, A. 2019. Consistency of gender differences in bullying in cross-cultural surveys.  Aggression and Violent Behavior 45:33–40.

Smith, P.K., Cowie, H., Olafsson, R.F., and Liefooghe, A.P.D. 2002. Definitions of bullying: A comparison of terms used, and age and gender differences, in a fourteen-country international comparison. Child Development 73(4):1119–33.

Smokowski, P R., and Evans, C.B. 2019. Consequences of bullying in childhood, adolescence, and adulthood: An ecological perspective. In  Bullying and Victimization Across the Lifespan . Springer: Cham, Switzerland, pp. 59-86.

Spiegler, J. 2016. Oct. 26. What is identity-based bullying – and how can I stop it? Edutopia . Retrieved October 26, 2022, from https://www.edutopia.org/article/what-is-identity-based-bullying-jinnie-spiegler

Sterzing, P.R., Hong, J.S., Gartner, R.E., and Auslander, W.F. 2016. Child maltreatment and bullying victimization among a community-based sample of sexual minority youth: The meditating role of psychological distress.  Journal of Child & Adolescent Trauma 9(4):283–293.

Swearer, S.M., and Doll, B. 2001. Bullying in schools: An ecological framework.  Journal of Emotional Abuse 2 (2-3):7–23.

Sweeting, H., Young, R., West, P., and Der, G. 2006. Peer victimization and depression in early–mid adolescence: A longitudinal study.  British Journal of Educational Psychology  76(3):577-594.

Thornberg, R., Tenenbaum, L., Varjas, K., Meyers, J., Jungert, T., and Vanegas, G. 2012. Bystander motivation in bullying incidents: To intervene or not to intervene?  Western Journal of Emergency Medicine  13(3):247.

Thompson, I., Hong, J. S., Lee, J. M., Prys, N.A., Morgan, J. T., and Udo-Inyang, I. 2020. A review of the empirical research on weight-based bullying and peer victimisation published between 2006 and 2016.  Educational Review  72(1):88-110.

Tippett, N., and Wolke, D. 2014. Socioeconomic status and bullying: A meta-analysis.  American Journal of Public Health  104(6):e48–e59.

Trach, J., Hymel, S., Waterhouse, T., and Neale, K. 2010. Bystander responses to school bullying: A cross-sectional investigation of grade and sex differences.  Canadian Journal of School Psychology 25(1):114–130.

Tsiantis, A.C.J., Beratis, I.N., Syngelaki, E.M., Stefanakou, A., Asimopoulos, C., Sideridis, G.D., and Tsiantis, J. The effects of a clinical prevention program on bullying, victimization, and attitudes toward school of elementary school students. 2013.  Behavioral Disorders 38 ( 4):243–257.

Ttofi, M. M., and Farrington, D. 2009. What works in preventing bullying: Effective elements of anti-bullying programmes. Journal of Aggression, Conflict and Peace Research 1(1):13–24.

Ttofi, M.M., Farrington, D., and L ӧ sel, F. 2012. School bullying as a predictor of violence later in life: A systematic review and meta-analysis of prospective longitudinal studies. Aggression and Violent Behavior 17(5):405–418.

Ttofi, M.M., Farrington, D.P., Lösel, F., and Loeber, R. 2011. Do the victims of school bullies tend to become depressed later in life? A systematic review and meta ‐ analysis of longitudinal studies.  Journal of Aggression, Conflict and Peace Research 3(2):63–73.

Turner-Moore, T., Milnes, K., and Gough, B. 2022. Bullying in five European countries: Evidence for bringing gendered phenomena under the umbrella of sexual bullying in research and practice.  Sex Roles 86(1):89–105.

U.K. Department for Education 2013. Preventing and tackling bullying. Retrieved from https://www.gov. uk/government/uploads/system/uploads/attachment_data/file/444862/Preventing_and_tackling_bullying_ advice.pdf

U.S. Department of Health and Human Services. 2022, April 4. National Survey of Children’s Health . Questionnaire. 26022244. NSCH-T2.

Van Geel, M., Vedder, P., and Tanilon, J. 2014. Are overweight and obese youths more often bullied by their peers? A meta-analysis on the relation between weight status and bullying.  International Journal of Obesity  38(10):1263-1267.

Van Noorden, T.H., Haselager, G.J., Cillessen, A.H., and Bukowski, W.M. 2015. Empathy and involvement in bullying in children and adolescents: A systematic review.  Journal of Youth and Adolescence  44(3):637 – 657.

Van Ryzin, M. J., and Roseth, C.J. 2019. Effects of cooperative learning on peer relations, empathy, and bullying in middle school.  Aggressive Behavior  45(6):643 – 651.

Veldkamp, S.A., Boomsma, D.I., de Zeeuw, E. L., van Beijsterveldt, C.E., Bartels, M., Dolan, C.V., and van Bergen, E. 2019. Genetic and environmental influences on different forms of bullying perpetration, bullying victimization, and their co-occurrence.  Behavior Genetics  49(5):432-443.

Vessey, J.A., DiFazio, R.L., and Strout, T.D. 2013. Youth bullying: A review of the science and call to action.  Nursing Outlook 61(5):337–345.

Volk, A.A., Dane, A.V., and Marini, Z.A. 2014. What is bullying? A theoretical redefinition.  Developmental Review 34(4):327–343.

Vreeman, R.C., and Carroll, A.E. 2007. A systematic review of school-based interventions to prevent bullying.  Archives of Pediatrics and Adolescent Medicine  161(1):78-88.

Walters, G.D., and Espelage, D.L. 2021. Reciprocity of cognitive and emotional antecedents to bullying: Bidirectional relations between cognitive impulsivity and anger.  Youth and Society . doi:10.1177/0044118X211053025

Wang, C., Ryoo, J.H., Swearer, S.M., Turner, R., and Goldberg, T.S. 2017. Longitudinal relationships between bullying and moral disengagement among adolescents.  Journal of Youth and Adolescence 46(6):1304–1317.

Willard, N.E. 2007. Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression, Threats, and Distress . Champaign, IL: Research Press.

Wong, J.S. 2009.  No Bullies Allowed: Understanding Peer Victimization, the Impacts on Delinquency, and the Effectiveness of Prevention Programs.  Dissertation submitted to the Pardee Rand Graduate School.

Wong, T.M.L., Slotboom, A., and Bijeveld, C.C.J.H. 2010. Risk factors for delinquency in adolescent and young adult females: A European review. European Journal of Criminology 7:266–284.

Xu, M., Macrynikola, N., Waseem, M., and Miranda, R. 2020. Racial and ethnic differences in bullying: Review and implications for intervention.  Aggression and Violent Behavior 50. doi:10.1016/j.avb.2019.101340

Ybarra, M.L., Espelage, D.L., Valido, A., Hong, J.S., and Prescott, T.L. 2019. Perceptions of middle school youth about school bullying.  Journal of Adolescence 75:175–187.

Younan, B. 2018. A systematic review of bullying definitions: How definition and format affect study outcome.  Journal of Aggression, Conflict and Peace Research . doi:10.1108/JACPR-02-2018-0347

Zhu, C., Huang, S., Evans, R., and Zhang, W. 2021. Cyberbullying among adolescents and children: A comprehensive review of the global situation, risk factors, and preventive measures.  Frontiers in Public Health  9. doi:10.3389/fpubh.2021.634909

Zych, I., Farrington, D.P., Llorent, V.J., Ribeaud, D., and Eisner, M.P. 2021. Childhood risk and protective factors as predictors of adolescent bullying roles.  International Journal of Bullying Prevention  3(2):138–146.

Zych, I., Farrington, D.P., and Ttofi, M.M. 2019. Protective factors against bullying and cyberbullying: A systematic review of meta-analyses.  Aggression and Violent Behavior 45:4–19.

Zych, I., Ortega-Ruiz, R., and Del Rey, R. 2015. Systematic review of theoretical studies on bullying and cyberbullying: Facts, knowledge, prevention, and intervention.  Aggression and Violent Behavior 23:1–21.

About this Literature Review

Suggested Reference: Development Services Group, Inc. 2022. "Bullying and Cyberbullying." Literature review. Washington, DC: Office of Juvenile Justice and Delinquency Prevention.  https://ojjdp.ojp.gov/model-programs-guide/literature-reviews/bullying-and-cyberbullying

Prepared by Development Services Group, Inc., under Contract Number: 47QRAA20D002V.

Last Update: February 2023

Relationships Among Self-Esteem, Bullying, and Cyberbullying in Adolescents: A Systematic Review

• PMID: 37879085
• DOI: 10.3928/02793695-20231013-01

The current systematic review explored relationships among self-esteem, bullying, and cyberbullying in adolescents. Three databases were used to search for articles pertaining to mental health, health care, and the social sciences. Keywords, including "bully," "victim," "victimization," "harassment," "aggression," and "abuse," were used to describe victims of cyberbullying and locate relevant articles. A total of 861 articles were found during the initial search, 110 were assessed for eligibility, and 10 ultimately met inclusion criteria. Most studies found correlations among self-esteem, bullying, and cyberbullying. Self-esteem was linked to being a victim of cyberbullying but not to being a perpetrator of cyberbullying or bullying in general. Students with high self-esteem at the beginning of the school year were more likely to become bullies as they got older, whereas those with low self-esteem were less likely to do so. In addition, self-esteem was found to have direct negative effects on victimization and cybervictimization, and victimization and cybervictimization were found to have direct negative effects on peer difficulties. Findings suggest that adolescent bullying and cyberbullying perpetration may be mitigated by higher levels of self-esteem. Parents should pay close attention to and limit adolescents' exposure to bullying and cyberbullying and stressful life events. Psychiatric nurses should routinely screen adolescents and develop interventions to enhance self-esteem and reduce bullying and cyberbullying. [ Journal of Psychosocial Nursing and Mental Health Services, 62 (5), 11-17.].

Publication types

• Systematic Review
• Adolescent Behavior / psychology
• Aggression / psychology
• Bullying* / psychology
• Crime Victims* / psychology
• Cyberbullying* / psychology
• Cyberbullying* / statistics & numerical data
• Self Concept*
• Students / psychology
• Students / statistics & numerical data

The Current State of Cyberbullying Research: A Brief Summary

The Committee on Education and Labor’s Subcommittee on Healthy Families and Communities heard testimony today about how teens are using and misusing technology, with a specific focus on cyberbullying and social networking. A full web cast of the testimony can be found here. The witnesses included a health care provider, an Internet safety advocate, a business-person, a school administrator, a teen, and talk show host Dr. Phil. While each of these persons was able to provide their own unique perspective about the problems facing youth online, I was struck by the fact that the panel did not include any cyberbullying or social networking researchers. This was particularly concerning because some questions about the state of the research were raised by the Representatives, but these queries couldn’t be adequately answered because researchers were not included on the panel.

So what is the current state of cyberbullying research? Well, this is a question that deserves a very long response, but I would like to briefly highlight a couple of issues here. When we first started exploring cyberbullying in 2002, there was literally no research that existed. Since then, we have conducted seven studies which include over 12,000 adolescents from over 80 schools using a variety of methodologies (a brief summary of the methods and findings from each can be found here ). We have written several academic articles and a book which details results from this research.

We also summarize the major findings of our research projects in fact sheets that are posted on our web site. While our methods have evolved over the course of our research program, the five most recent surveys have been based on random samples of known populations using a measure of cyberbullying that has proven reliable and valid.

And this is just a summary of our activities. There are several other very competent researchers from a variety of academic disciplines doing excellent work that is complementary to ours. Based on our studies and that of others, we know that a good proportion of adolescents have experienced cyberbullying at some point in their lifetimes but that the majority of those experiences were relatively minor in nature. Anywhere from 15-35% of teens have experienced some form of cyberbullying, depending on who is sampled and how cyberbullying is defined and measured (see chart with our rates here ). Some studies report lower or much higher rates, but we feel reasonably confident in estimating that about 20% of teens have been the victim of cyberbullying at some point in their lifetime (Smith, Mahdavi, Carvalho, Fisher, Russell, & Tippett, 2008). As expected, this number drops to between 5% and 10% when focusing on recent experiences (within the previous 30 days).

The percent of youth who admit to cyberbullying others at some point in their lives is a bit lower, though comparable. Across all of our studies (see chart here ), the rates range from about 11% to as high as 20%. These too are consistent with the weight of the available research conducted by others (Kowalski, Limber, & Agatston, 2008; Ybarra, Espelage, & Mitchell, 2007).

A couple of other broad generalizations can be made about cyberbullying, based on the extant literature:

– Adolescent girls are just as likely, if not more likely than boys to experience cyberbullying (as a victim and offender) (Kowalski et al., 2008; Hinduja & Patchin, 2009)

– Cyberbullying is related to low self-esteem, suicidal ideation, anger, frustration, and a variety of other emotional and psychological problems (Hinduja & Patchin, forthcoming; Patchin & Hinduja, 2006; Patchin & Hinduja, in review)

– Cyberbullying is related to other issues in the ‘real world’ including school problems, antisocial behavior, substance use, and delinquency (Hinduja & Patchin, 2007; Hinduja & Patchin, 2008)

– Traditional bullying is still more common than cyberbullying (Lenhart, 2007; Smith et al., 2008)

– Traditional bullying and cyberbullying are closely related: those who are bullied at school are bullied online and those who bully at school bully online (Hinduja & Patchin, 2009; Ybarra, Diener-West, & Leaf, 2007)

Despite the strides that have been made in more fully understanding cyberbullying, it is clear that more work is necessary. We as researchers need to better coordinate our efforts so that our results can more easily be understood by the public and more precisely be integrated into our own prevention and response efforts. This means settling on a uniform definition of cyberbullying – or at least clearly specifying how cyberbullying was operationalized in the work that researchers do.

We as researchers also need to utilize the most rigorous methods possible for the particular situation, and describe exactly how the study was conducted to enable thorough analysis and replication. Longitudinal, nationally-representative samples are not always feasible, but we shouldn’t sacrifice rigor for convenience.

We also need to move toward evaluating programs and curriculum designed to address cyberbullying. About 75 minutes into the hearings, Representative Bobby Scott from Virginia asked the panel if there was any research concerning effective bullying prevention programs. The panel didn’t have a good response. Actually, the Olweus Bullying Prevention Program has demonstrated effectiveness at reducing traditional bullying in a variety of school contexts, and has been formally vetted and evaluated by a number of reputable researchers. This said, no similar evaluations have yet been conducted to better understand the processes and outcomes of initiatives focused on addressing cyberbullying.

Even with these concerns, I am hopeful for the future of cyberbullying research. Sameer and I are aware of a number of studies under way that should shed more light on the relevant issues and recent high-profile incidents have pushed cyberbullying to the forefront of a national dialogue. And while we are glad to see cyberbullying finally receiving attention in the media, we all need to be mindful that media reports can sometimes misrepresent and slant research findings to serve a certain purpose. Please take some time to read the actual research yourself so that you can be personally educated and well-informed about these issues. If you have questions about how or why something was done in a study, we encourage you to contact the authors. Of course, we welcome any comments or questions you have about our work and we are sure that our colleagues are also open to your thoughts.

In short, there currently exists a meaningful and growing body of knowledge regarding the correlates and consequences of cyberbullying which can supplement the volumes of information published about traditional bullying. How we use this information to direct future studies, prevention and intervention efforts, and policy is the real question. The first step, in my view, is to make sure that the researchers are at the table.

References:

Hinduja, S. & Patchin, J. W. (2007). Offline consequences of online victimization: School violence and delinquency. Journal of School Violence, 6 (3), 89-112.

Hinduja, S. & Patchin, J. W. (2008). Cyberbullying: An exploratory analysis of factors related to offending and victimization. Deviant Behavior, 29 (2), 129-156.

Hinduja, S. & Patchin, J. W. (2009). Bullying beyond the Schoolyard: Preventing and Responding to Cyberbullying . Thousand Oaks, CA: Sage Publications.

Hinduja, S. & Patchin, J. W. (forthcoming). Bullying, cyberbullying, and suicide. Forthcoming in Archives of Suicide Research .

Kowalski, R. M., Limber, S. P. & Agatston, P.W. (2008). Cyber Bullying: Bullying in the Digital Age . Malden, MA: Wiley-Blackwell.

Lenhart, A. (2007). Cyberbullying and Online Teens . Pew Internet & American Life Project, June 27. (http://www.pewinternet.org/PPF/r/216/report_display.asp).

Patchin, J. W. & Hinduja, S. (2006). Bullies move beyond the schoolyard: A preliminary look at cyberbullying. Youth Violence and Juvenile Justice, 4 (2), 123-147.

Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., and Tippett, N. (2008). Cyberbullying: its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry 49(4): 376–385.

Ybarra, M., 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: S42–S50.

Ybarra, M. L., Espelage, D. L., & Mitchell, K. J. (2007). The Co-occurrence of Internet Harassment and Unwanted Sexual Solicitation Victimization and Perpetration: Associations with Psychosocial Indicators, Journal of Adolescent Health, 41 , S31-S41.

Enhancing Child Safety & Online Technologies: Final Report of the Internet Safety Technical Task Force

Related posts

“It has made me want to cut my wrists cause I couldn’t cope with going back to school and the girls starting again, I just felt like throwing up,” states a 16-year-old from the United Kingdom. I chose to start some of my post of with a thought of how these teenagers feel and words they think. This shows the people in disbelief because bullying has became worst in the world. It is also stated that girls practice bullying more than boys the most because they’re more emotional. Boys are more physical, maybe a fight rather than words being throw at each other. I 100% agree, because girls gossip about any and everything. People can get talked about if they don’t wear nice clothes, smell or look funny, smart, and dumb or things can even be made up because someone may be jealous or dislike you. Everyone was made different in this world, so I feel people should accept it more than abuse and talk about it. Four facts that state girls being bullied the most are the following: 1. More verbal (talkative) 2. More emotional and psychological (basically knows just what to say to make you feel worthless) 3. Arguable and less confrontation (don’t like being in presence too much, leading rumors are a big like) 4. Culturally and socially constrained

I am interested in this topic and want to help. How can I collaborate? I can write and do research. Thankxs

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.