research papers on bullying

Bullying: What We Know Based On 40 Years of Research

APA journal examines science aimed at understanding causes, prevention

WASHINGTON — A special issue of American Psychologist ® provides a comprehensive review of over 40 years of research on bullying among school age youth, documenting the current understanding of the complexity of the issue and suggesting directions for future research.

“The lore of bullies has long permeated literature and popular culture. Yet bullying as a distinct form of interpersonal aggression was not systematically studied until the 1970s. Attention to the topic has since grown exponentially,” said Shelley Hymel, PhD, professor of human development, learning and culture at the University of British Columbia, a scholarly lead on the special issue along with Susan M. Swearer, PhD, professor of school psychology at the University of Nebraska-Lincoln. “Inspired by the 2011 U.S. White House Conference on Bullying Prevention, this collection of articles documents current understanding of school bullying.”

The special issue consists of an introductory overview  (PDF, 90KB) by Hymel and Swearer, co-directors of the Bullying Research Network, and five articles on various research areas of bullying including the long-term effects of bullying into adulthood, reasons children bully others, the effects of anti-bullying laws and ways of translating research into anti-bullying practice.

Articles in the issue:

Long-Term Adult Outcomes of Peer Victimization in Childhood and Adolescence: Pathways to Adjustment and Maladjustment  (PDF, 122KB) by Patricia McDougall, PhD, University of Saskatchewan, and Tracy Vaillancourt, PhD, University of Ottawa.

The experience of being bullied is painful and difficult. Its negative impact — on academic functioning, physical and mental health, social relationships and self-perceptions — can endure across the school years. But not every victimized child develops into a maladjusted adult. In this article, the authors provide an overview of the negative outcomes experienced by victims through childhood and adolescence and sometimes into adulthood. They then analyze findings from prospective studies to identify factors that lead to different outcomes in different people, including in their biology, timing, support systems and self-perception.

Patricia McDougall can be contacted by email or by phone at (306) 966-6203.

A Relational Framework for Understanding Bullying: Developmental Antecedents and Outcomes  (PDF, 151KB) by Philip Rodkin, PhD, and Dorothy Espelage, PhD, University of Illinois, Urbana-Champaign, and Laura Hanish, PhD, Arizona State University.

How do you distinguish bullying from aggression in general? In this review, the authors describe bullying from a relationship perspective. In order for bullying to be distinguished from other forms of aggression, a relationship must exist between the bully and the victim, there must be an imbalance of power between the two and it must take place over a period of time. “Bullying is perpetrated within a relationship, albeit a coercive, unequal, asymmetric relationship characterized by aggression,” wrote the authors. Within that perspective, the image of bullies as socially incompetent youth who rely on physical coercion to resolve conflicts is nothing more than a stereotype. While this type of “bully-victim” does exist and is primarily male, the authors describe another type of bully who is more socially integrated and has surprisingly high levels of popularity among his or her peers. As for the gender of victims, bullying is just as likely to occur between boys and girls as it is to occur in same-gender groups.  

Dorothy Espelage can be contacted by email or by phone at (217) 333-9139.

Translating Research to Practice in Bullying Prevention  (PDF, 157KB) by Catherine Bradshaw, PhD, University of Virginia.

This paper reviews the research and related science to develop a set of recommendations for effective bullying prevention programs. From mixed findings on existing programs, the author identifies core elements of promising prevention approaches (e.g., close playground supervision, family involvement, and consistent classroom management strategies) and recommends a three-tiered public health approach that can attend to students at all risk levels. However, the author notes, prevention efforts must be sustained and integrated to effect change. 

Catherine Bradshaw can be contacted by email or by phone at (434) 924-8121.

Law and Policy on the Concept of Bullying at School  (PDF, 126KB) by Dewey Cornell, PhD, University of Virginia, and Susan Limber, PhD, Clemson University.

Since the shooting at Columbine High School in 1999, all states but one have passed anti-bullying laws, and multiple court decisions have made schools more accountable for peer victimization. Unfortunately, current legal and policy approaches, which are strongly rooted in laws regarding harassment and discrimination, do not provide adequate protection for all bullied students. In this article, the authors provide a review of the legal framework underpinning many anti-bullying laws and make recommendations on best practices for legislation and school policies to effectively address the problem of bullying.

Dewey Cornell can be contacted by email or by phone at (434) 924-0793.

Understanding the Psychology of Bullying: Moving Toward a Social-Ecological Diathesis-Stress Model by Susan Swearer, PhD, University of Nebraska-Lincoln, and Shelley Hymel, PhD, University of British Columbia.

Children’s involvement in bullying varies across roles and over time. A student may be victimized by classmates but bully a sibling at home. Bullying is a complex form of interpersonal aggression that can be both a one-on-one process and a group phenomenon. It negatively affects not only the victim, but the bully and witnesses as well. In this paper, the authors suggest an integrated model for examining bullying and victimization that recognizes the complex and dynamic nature of bullying across multiple settings over time.

Susan Swearer  can be contacted by email or by phone at (402) 472-1741. Shelley Hymel can be contacted by email or by phone at (604) 822-6022.

Copies of articles are also available from APA Public Affairs , (202) 336-5700.

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• Published: 13 February 2024

Bullying fosters interpersonal distrust and degrades adolescent mental health as predicted by Social Safety Theory

• Dimitris I. Tsomokos   ORCID: orcid.org/0000-0002-9613-7823 1 &
• George M. Slavich   ORCID: orcid.org/0000-0001-5710-3818 2  

Nature Mental Health volume  2 ,  pages 328–336 ( 2024 ) Cite this article

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Social Safety Theory predicts that socially threatening experiences such as bullying degrade mental health partly by fostering the belief that others cannot be trusted. Here we tested this prediction by examining how peer bullying in childhood impacted adolescent mental health, and whether this effect was mediated by interpersonal distrust and several other commonly studied mediators—namely diet, sleep and physical activity—in 10,000 youth drawn from the UK’s Millennium Cohort Study. Youth bullied in childhood developed more internalizing, externalizing and total mental health problems in late adolescence, and this effect was partially mediated by interpersonal distrust during middle adolescence. Indeed, adolescents who developed greater distrust were approximately 3.5 times more likely to subsequently experience clinically significant mental health problems than those who developed less distrust. Individual and school-based interventions aimed at reducing the negative impact of bullying on mental health may thus benefit from bolstering youths’ sense of trust in others.

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Data availability.

The data that support the findings of the present study are publicly available from the Millennium Cohort Study (UK Data Service) by application, under license. For further information on how to obtain the dataset, visit the UK Data Service website ( https://ukdataservice.ac.uk/ ) or the relevant website of the Centre for Longitudinal Studies ( https://cls.ucl.ac.uk/cls-studies/millennium-cohort-study/ ).

Code availability

Details of all the variable names, their processing and the full output of the R code are available on the Open Science Framework website ( https://osf.io/zjq9a ; ref. 5 in the Supplementary Information ). D.I.T. accessed the data and wrote the code.

Bitsko, R. H. et al. Mental health surveillance among children—United States, 2013–2019. MMWR Suppl. 71 , 1–42 (2022).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Ravens-Sieberer, U. et al. Impact of the COVID-19 pandemic on quality of life and mental health in children and adolescents in Germany. Eur. Child Adolesc. Psychiatry 31 , 879–889 (2022).

Article   PubMed   Google Scholar  

Newlove-Delgado, T. et al. Child mental health in England before and during the COVID-19 lockdown. Lancet Psychiatry 8 , 353–354 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Auerbach, R. P. et al. WHO World Mental Health Surveys International College Student Project: prevalence and distribution of mental disorders. J. Abnorm. Psychol. 127 , 623–638 (2018).

Racine, N. et al. Global prevalence of depressive and anxiety symptoms in children and adolescents during COVID-19: a meta-analysis. JAMA Pediatr. 175 , 1142–1150 (2021).

Youth Risk Behavior Survey: Data Summary and Trends Report (Center for Disease Control and Prevention, 2023).

Jones, S. et al. Mental health, suicidality, and connectedness among high school students during the COVID-19 pandemic—Adolescent Behaviors and Experiences Survey, United States, January–June 2021. MMWR Suppl. 71 , 16–21 (2022).

Slavich, G. M. Social safety theory: a biologically based evolutionary perspective on life stress, health, and behavior. Annu. Rev. Clin. Psychol. 16 , 265–295 (2020).

Slavich, G. M. Social Safety Theory: understanding social stress, disease risk, resilience, and behavior during the COVID-19 pandemic and beyond. Curr. Opin. Psychol. 45 , 101299 (2022).

Slavich, G. M. et al. Social Safety Theory: conceptual foundation, underlying mechanisms, and future directions. Health Psychol. Rev. 17 , 5–59 (2023).

Gilbert, P. et al. Feeling safe and content: a specific affect regulation system? Relationship to depression, anxiety, stress, and self-criticism. J. Posit. Psychol. 3 , 182–191 (2008).

Article   Google Scholar  

Hostinar, C. E., Sullivan, R. M. & Gunnar, M. R. Psychobiological mechanisms underlying the social buffering of the hypothalamic–pituitary–adrenocortical axis: a review of animal models and human studies across development. Psychol. Bull. 140 , 256–282 (2014).

Slavich, G. M. & Irwin, M. R. From stress to inflammation and major depressive disorder: a social signal transduction theory of depression. Psychol. Bull. 140 , 774–815 (2014).

Chen, G. Y. & Nuñez, G. Sterile inflammation: sensing and reacting to damage. Nat. Rev. Immunol. 10 , 826–837 (2010).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Blascovich, J. & Mendes, W. B. in Handbook of Social Psychology Vol. 1, 5th edn 194–227 (John Wiley & Sons, 2010).

Flouri, E. et al. Prenatal and childhood adversity and inflammation in children: a population-based longitudinal study. Brain Behav. Immun. 87 , 524–530 (2020).

Article   CAS   PubMed   Google Scholar  

Iob, E. et al. Adverse childhood experiences and severity levels of inflammation and depression from childhood to young adulthood: a longitudinal cohort study. Mol. Psychiatry 27 , 2255–2263 (2022).

Furman, D. et al. Chronic inflammation in the etiology of disease across the life span. Nat. Med. 25 , 1822–1832 (2019).

Arseneault, L. Annual Research Review: the persistent and pervasive impact of being bullied in childhood and adolescence: implications for policy and practice. J. Child Psychol. Psychiatry 59 , 405–421 (2018).

Stewart, J. G. et al. Peer victimization and suicidal thoughts and behaviors in depressed adolescents. J. Abnorm. Child Psychol. 46 , 581–596 (2018).

Sinclair, H. C., Wilson, K. J. & Stubbs-Richardson, M. Advances in youth bullying research. Front. Psychol. 13 , 860887 (2022).

Biswas, T. et al. Global variation in the prevalence of bullying victimisation amongst adolescents: role of peer and parental supports. EClinicalMedicine 20 , 100276 (2020).

Wolke, D. & Lereya, S. T. Long-term effects of bullying. Arch. Dis. Child. 100 , 879–885 (2015).

Gini, G. & Pozzoli, T. Bullied children and psychosomatic problems: a meta-analysis. Pediatrics 132 , 720–729 (2013).

Sweeting, H. et al. Peer victimization and depression in early–mid adolescence: a longitudinal study. Br. J. Educ. Psychol. 76 , 577–594 (2006).

Lereya, S. T. et al. Being bullied during childhood and the prospective pathways to self-harm in late adolescence. J. Am. Acad. Child Adolesc. Psychiatry 52 , 608–618.e2 (2013).

Copeland, W. E. et al. Adult psychiatric outcomes of bullying and being bullied by peers in childhood and adolescence. JAMA Psychiatry 70 , 419–426 (2013).

Sharpe, H. et al. Changes in peer and sibling victimization in early adolescence: longitudinal associations with multiple indices of mental health in a prospective birth cohort study. Eur. Child Adolesc. Psychiatry 31 , 737–746 (2022).

Moore, S. E. et al. Consequences of bullying victimization in childhood and adolescence: a systematic review and meta-analysis. World J. Psychiatry 7 , 60–76 (2017).

Vaillancourt, T. et al. Longitudinal links between childhood peer victimization, internalizing and externalizing problems, and academic functioning: developmental cascades. J. Abnorm. Child Psychol. 41 , 1203–1215 (2013).

Schoeler, T. et al. Quasi-experimental evidence on short- and long-term consequences of bullying victimization: a meta-analysis. Psychol. Bull. 144 , 1229–1246 (2018).

van Geel, M. et al. Does peer victimization predict low self-esteem, or does low self-esteem predict peer victimization? Meta-analyses on longitudinal studies. Dev. Rev. 49 , 31–40 (2018).

Young-Jones, A. et al. Bullying affects more than feelings: the long-term implications of victimization on academic motivation in higher education. Social Psychology of Education 18 , 185–200 (2015).

Gaffney, H., Ttofi, M. M. & Farrington, D. P. Evaluating the effectiveness of school-bullying prevention programs: an updated meta-analytical review. Aggress. Violent Behav. 45 , 111–133 (2019).

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

Gaffney, H., Ttofi, M. M. & Farrington, D. P. What works in anti-bullying programs? Analysis of effective intervention components. J. School Psychol. 85 , 37–56 (2021).

Marsh, H. W. et al. Peer victimization: an integrative review and cross-national test of a tripartite model. Educ. Psychol. Rev. 35 , 46 (2023).

Bonell, C. et al. Effects of the Learning Together intervention on bullying and aggression in English secondary schools (INCLUSIVE): a cluster randomised controlled trial. Lancet 392 , 2452–2464 (2018).

Herkama, S. et al. Sleeping problems partly mediate the association between victimization and depression among youth. J. Child Family Stud. 28 , 2477–2486 (2019).

Sosnowski, D. W., Kliewer, W. & Lepore, S. J. The role of sleep in the relationship between victimization and externalizing problems in adolescents. J. Youth Adolesc. 45 , 1744–1754 (2016).

Lee, K. S. & Vaillancourt, T. Developmental pathways between peer victimization, psychological functioning, disordered eating behavior, and body mass index: a review and theoretical model. Aggress. Violent Behav. 39 , 15–24 (2018).

Storch, E. A. et al. Peer victimization, psychosocial adjustment, and physical activity in overweight and at-risk-for-overweight youth. J. Pediatr. Psychol. 32 , 80–89 (2007).

Eisenberg, M. & Neumark-Sztainer, D. Peer harassment and disordered eating. Int. J. Adolesc. Med. Health 20 , 155–164 (2008).

Liang, K. et al. Food insecurity and bullying victimization among 170,618 adolescents in 59 countries. Front. Psychiatry 12 , 766804 (2021).

Uslaner, E. M. The Moral Foundations of Trust (Cambridge Univ. Press, 2002).

Rotenberg, K. J. in International Encyclopedia of the Social & Behavioral Sciences (eds Smelser, N. J. & Baltes, P. B.) 7866–7868 (Pergamon, 2001).

Neil, L. et al. Trust and childhood maltreatment: evidence of bias in appraisal of unfamiliar faces. J. Child Psychol. Psychiatry 63 , 655–662 (2022).

Turagabeci, A. R., Nakamura, K. & Takano, T. Healthy lifestyle behaviour decreasing risks of being bullied, violence and injury. PLoS ONE 3 , e1585 (2008).

Williford, A. et al. Effects of the KiVa anti-bullying program on adolescents’ depression, anxiety, and perception of peers. J. Abnorm. Child Psychol. 40 , 289–300 (2012).

Albaladejo-Blázquez, N. et al. Poor dietary habits in bullied adolescents: the moderating effects of diet on depression. Int. J. Environ. Res. Public Health 15 , 1569 (2018).

Vugteveen, J., de Bildt, A. & Timmerman, M. E. Normative data for the self-reported and parent-reported Strengths and Difficulties Questionnaire (SDQ) for ages 12–17. Child Adolesc. Psychiatry Ment. Health 16 , 5 (2022).

Kim, S. S. et al. Association between interpersonal trust, reciprocity, and depression in South Korea: a prospective analysis. PLoS ONE 7 , e30602 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Fermin, A. S. R. et al. The neuroanatomy of social trust predicts depression vulnerability. Sci. Rep. 12 , 16724 (2022).

Blakemore, S.-J. The social brain in adolescence. Nat. Rev. Neurosci. 9 , 267–277 (2008).

Frith, C. D. The social brain? Philos. Trans. R. Soc. B 362 , 671–678 (2007).

Krueger, F. & Meyer-Lindenberg, A. Toward a model of interpersonal trust drawn from neuroscience, psychology, and economics. Trends Neurosci. 42 , 92–101 (2019).

Donaldson, Z. R. & Young, L. J. Oxytocin, vasopressin, and the neurogenetics of sociality. Science 322 , 900–904 (2008).

Article   ADS   CAS   PubMed   Google Scholar  

Marsh, N. et al. Oxytocin and the neurobiology of prosocial behavior. Neuroscientist 27 , 604–619 (2021).

Yan, Z. & Kirsch, P. in The Neurobiology of Trust (ed. Krueger, F.) 315–337 (Cambridge Univ. Press, 2021).

Kosfeld, M. et al. Oxytocin increases trust in humans. Nature 435 , 673–676 (2005).

Declerck, C. H. et al. A registered replication study on oxytocin and trust. Nat. Hum. Behav. 4 , 646–655 (2020).

Neumann, I. D. & Landgraf, R. Balance of brain oxytocin and vasopressin: implications for anxiety, depression, and social behaviors. Trends Neurosci. 35 , 649–659 (2012).

Xie, S. et al. The association of oxytocin with major depressive disorder: role of confounding effects of antidepressants. Rev. Neurosci. 33 , 59–77 (2022).

Veiga, L. et al. Depressive symptomatology, temperament and oxytocin serum levels in a sample of healthy female university students. BMC Psychol. 10 , 36 (2022).

De Cagna, F. et al. The role of intranasal oxytocin in anxiety and depressive disorders: a systematic review of randomized controlled trials. Clin. Psychopharmacol. Neurosci. 17 , 1–11 (2019).

McQuaid, R. J. et al. Making room for oxytocin in understanding depression. Neurosci. Biobehav. Rev. 45 , 305–322 (2014).

Heinrichs, M. et al. Social support and oxytocin interact to suppress cortisol and subjective responses to psychosocial stress. Biol. Psychiatry 54 , 1389–1398 (2003).

Guzman-Holst, C. et al. Research review: do antibullying interventions reduce internalizing symptoms? A systematic review, meta-analysis, and meta-regression exploring intervention components, moderators, and mechanisms. J. Child Psychol. Psychiatry 63 , 1454–1465 (2022).

Joshi, H. & Fitzsimons, E. The Millennium Cohort Study: the making of a multi-purpose resource for social science and policy. Longitud. Life Course Stud. 7 , 409–430 (2016).

Plewis, I. et al. MCS: Technical Report on Sampling (Centre for Longitudinal Studies, 2004).

Goodman, R. The Strengths and Difficulties Questionnaire: a research note. J. Child Psychol. Psychiatry 38 , 581–586 (1997).

Lundh, L. G., Wångby-Lundh, M. & Bjärehed, J. Self-reported emotional and behavioral problems in Swedish 14 to 15-year-old adolescents: a study with the self-report version of the Strengths and Difficulties Questionnaire. Scand. J. Psychol. 49 , 523–532 (2008).

Hoare, E. et al. Association of child and adolescent mental health with adolescent health behaviors in the UK Millennium Cohort. JAMA Network Open 3 , e2011381 (2020).

Biddle, S. J. H. & Asare, M. Physical activity and mental health in children and adolescents: a review of reviews. Br. J. Sports Med. 45 , 886–895 (2011).

Haraden, D. A., Mullin, B. C. & Hankin, B. L. The relationship between depression and chronotype: a longitudinal assessment during childhood and adolescence. Depress. Anxiety 34 , 967–976 (2017).

Patalay, P. & Fitzsimons, E. Development and predictors of mental ill-health and wellbeing from childhood to adolescence. Soc. Psychiatry Psychiatric Epidemiol. 53 , 1311–1323 (2018).

Patalay, P. & Hardman, C. A. Comorbidity, codevelopment, and temporal associations between body mass index and internalizing symptoms from early childhood to adolescence. JAMA Psychiatry 76 , 721–729 (2019).

Mueller, M. A. E. & Flouri, E. Urban adolescence: the role of neighbourhood greenspace in mental well-being. Front. Psychol. 12 , 712065 (2021).

Blakemore, S.-J. Adolescence and mental health. Lancet 393 , 2030–2031 (2019).

Fitzsimons, E. et al. Poverty dynamics and parental mental health: determinants of childhood mental health in the UK. Soc. Sci. Med. 175 , 43–51 (2017).

Church, D. & Midouhas, E. Data Note: MEDIX Air Pollution Data at Ward Level, Linked to MCS1 and MCS2 (UCL Institute of Education, 2016).

Raghunathan, T. E. et al. A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodol. 27 , 85–96 (2001).

Google Scholar  

Rubin, D. B. Multiple Imputation for Nonresponse in Surveys (John Wiley, 1987).

R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2018).

Tsomokos, D. I. & Dunbar, R. I. M. The role of religion in adolescent mental health: faith as a moderator of the relationship between distrust and depression. Religion Brain Behav. https://doi.org/10.1080/2153599X.2023.2248230 (2023).

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D.I.T. was partially supported by Alphablocks Nursery School Ltd. G.M.S. was supported by grant #OPR21101 from the California Governor’s Office of Planning and Research/California Initiative to Advance Precision Medicine.

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Tsomokos, D.I., Slavich, G.M. Bullying fosters interpersonal distrust and degrades adolescent mental health as predicted by Social Safety Theory. Nat. Mental Health 2 , 328–336 (2024). https://doi.org/10.1038/s44220-024-00203-7

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ORIGINAL RESEARCH article

Understanding alternative bullying perspectives through research engagement with young people.

• School of Education and Social Care, Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom

Bullying research has traditionally been dominated by largescale cohort studies focusing on the personality traits of bullies and victims. These studies focus on bullying prevalence, risk and protective factors, and negative outcomes. A limitation of this approach is that it does not explain why bullying happens. Qualitative research can help shed light on these factors. This paper discusses the findings from four mainly qualitative research projects including a systematic review and three empirical studies involving young people to various degrees within the research process as respondents, co-researchers and commissioners of research. Much quantitative research suggests that young people are a homogenous group and through the use of surveys and other large scale methods, generalizations can be drawn about how bullying is understood and how it can be dealt with. Findings from the studies presented in this paper, add to our understanding that young people appear particularly concerned about the role of wider contextual and relational factors in deciding if bullying has happened. These studies underscore the relational aspects of definitions of bullying and, how the dynamics of young people’s friendships can shift what is understood as bullying or not. Moreover, to appreciate the relational and social contexts underpinning bullying behaviors, adults and young people need to work together on bullying agendas and engage with multiple definitions, effects and forms of support. Qualitative methodologies, in particular participatory research opens up the complexities of young lives and enables these insights to come to the fore. Through this approach, effective supports can be designed based on what young people want and need rather than those interpreted as supportive through adult understanding.

Introduction

Research on school bullying has developed rapidly since the 1970s. Originating in social and psychological research in Norway, Sweden, and Finland, this body of research largely focusses on individualized personality traits of perpetrators and victims ( Olweus, 1995 ). Global interest in this phenomenon subsequently spread and bullying research began in the United Kingdom, Australia, and the United States ( Griffin and Gross, 2004 ). Usually quantitative in nature, many studies examine bullying prevalence, risk and protective factors, and negative outcomes ( Patton et al., 2017 ). Whilst quantitative research collates key demographic information to show variations in bullying behaviors and tendencies, this dominant bullying literature fails to explain why bullying happens. Nor does it attempt to understand the wider social contexts in which bullying occurs. Qualitative research on the other hand, in particular participatory research, can help shed light on these factors by highlighting the complexities of the contextual and relational aspects of bullying and the particular challenges associated with addressing it. Patton et al. (2017) in their systematic review of qualitative methods used in bullying research, found that the use of such methods can enhance academic and practitioner understanding of bullying.

In this paper, I draw on four bullying studies; one systematic review of both quantitative and qualitative research ( O’Brien, 2009 ) and three empirical qualitative studies ( O’Brien and Moules, 2010 ; O’Brien, 2016 , 2017 ) (see Table 1 below). I discuss how participatory research methodologies, to varying degrees, were used to facilitate bullying knowledge production among teams of young people and adults. Young people in these presented studies were consequently involved in the research process along a continuum of involvement ( Bragg and Fielding, 2005 ). To the far left of the continuum, young people involved in research are referred to as “active respondents” and their data informs teacher practice. To the middle of the continuum sit “students as co-researchers” who work with teachers to explore an issue which has been identified by that teacher. Finally to the right, sit “students as researchers” who conduct their own research with support from teachers. Moving from left to right of the continuum shows a shift in power dynamics between young people and adults where a partnership develops. Young people are therefore recognized as equal to adults in terms of what they can bring to the project from their own unique perspective, that of being a young person now.

Table 1. The studies.

In this paper, I advocate for the active involvement of young people in the research process in order to enhance bullying knowledge. Traditional quantitative studies have a tendency to homogenize young people by suggesting similarity in thinking about what constitutes bullying. However, qualitative studies have demonstrated that regardless of variables, young people understand bullying in different ways so there is a need for further research that starts from these perspectives and focusses on issues that young people deem important. Consequently, participatory research allows for the stories of the collective to emerge without losing the stories of the individual, a task not enabled through quantitative approaches.

What Is Bullying?

Researching school bullying has been problematic and is partly related to the difficulty in defining it ( Espelage, 2018 ). Broadly speaking, bullying is recognized as aggressive, repeated, intentional behavior involving an imbalance of power aimed toward an individual or group of individuals who cannot easily defend themselves ( Vaillancourt et al., 2008 ). In more recent times, “traditional” bullying behaviors have been extended to include cyber-bullying, involving the use of the internet and mobile-phones ( Espelage, 2018 ). Disagreements have been noted in the literature about how bullying is defined by researchers linked to subject discipline and culture. Some researchers for example, disagree about the inclusion or not of repetition in definitions ( Griffin and Gross, 2004 ) and these disagreements have had an impact on interpreting findings and prevalence rates. However, evidence further suggests that young people also view bullying in different ways ( Guerin and Hennessy, 2002 ; Cuadrado-Gordillo, 2012 ; Eriksen, 2018 ). Vaillancourt et al. (2008) explored differences between researchers and young people’s definitions of bullying, and found that children’s definitions were usually spontaneous, and did not always encompass the elements of repetition, power imbalance and intent. They concluded, that children need to be provided with a bullying definition so similarities and comparisons can be drawn. In contrast, Huang and Cornell (2015) found no evidence that the inclusion of a definition effected prevalence rates. Their findings, they suggest, indicate that young people use their own perceptions of bullying when answering self-report questionnaires and they are not influenced by an imposed definition.

Nevertheless, differences in children and young people’s bullying definitions are evident in the research literature and have been explained by recourse to age and stage of development ( Smith et al., 2002 ) and their assumed lack of understanding about what constitutes bullying ( Boulton and Flemington, 1996 ). Naylor et al. (2001) for example, found that younger children think similarly in their definitions of bullying, while Smith et al. (2002) found that 8 year olds did not distinguish as clearly between different forms of behavioral aggression as 14 year olds. Methodological limitations associated with understanding bullying have been identified by Forsberg et al. (2018) and Maunder and Crafter (2018) . These authors postulate that quantitative approaches, although providing crucial insights in understanding bullying, are reliant on pre-defined variables, which can shield some of the complexities that qualitative designs can unravel, as individual experiences of bullying are brought to the fore. Indeed, La Fontaine (1991) suggests that unlike standard self-report questionnaires and other quantitative methods used to collect bullying data, analyzing qualitative data such as those collected from a helpline, enables the voice of young people to be heard and consequently empowers adults to understand bullying on their terms rather than relying solely on interpretations and perceptions of adults. Moore and Maclean (2012) collected survey, as well as interview and focus group data, on victimization occurring on the journey to and from school. They found that what young people determined as victimization varied and was influenced by a multifaceted array of circumstances, some of which adults were unaware of. Context for example, played an important role where certain behaviors in one situation could be regarded as victimization while in another they were not. Specific behaviors including ignoring an individual was particularly hurtful and supporting a friend who was the subject of victimization could lead to their own victimization.

Lee (2006) suggests that some bullying research does not reflect individual experiences, and are thus difficult for participants to relate to. Canty et al. (2016) reiterates this and suggests that when researchers provide young people with bullying definitions in which to position their own experiences, this can mask some of the complexities that the research intends to uncover. Such approaches result in an oversight into the socially constructed and individual experiences of bullying ( Eriksen, 2018 ). Griffin and Gross (2004) further argue that when researchers use vague or ambiguous definitions an “overclassification of children as bullies or victims” (p. 381) ensues. Consequently, quantitative research does not consider children as reliable in interpreting their own lived experiences and therefore some of the interactions they consider as bullying, that do not fit within the conventional definitions, are concealed. This approach favors the adult definition of bullying regarding it as “more reliable” than the definitions of children and young people Canty et al. (2016) . The perceived “seriousness” of bullying has also been explored. Overall, young people and adults are more likely to consider direct bullying (face-to-face actions including hitting, threatening and calling names) as “more serious” than indirect bullying (rumor spreading, social exclusion, forcing others to do something they do not want to do) ( Maunder et al., 2010 ; Skrzypiec et al., 2011 ). This perception of “seriousness,” alongside ambiguous definitions of bullying, has further implications for reporting it. Despite the advice given to young people to report incidents of school bullying ( Moore and Maclean, 2012 ), the literature suggests that many are reluctant to do so ( deLara, 2012 ; Moore and Maclean, 2012 ).

Several factors have been highlighted as to why young people are reluctant to report bullying ( Black et al., 2010 ). deLara (2012) , found apprehension in reporting bullying to teachers due to the fear that they will either not do enough or too much and inadvertently make the situation worse, or fear that teachers will not believe young people. Research also shows that young people are reluctant to tell their parents about bullying due to perceived over-reaction and fear that the bullying will be reported to their school ( deLara, 2012 ; Moore and Maclean, 2012 ). Oliver and Candappa (2007) suggest that young people are more likely to confide in their friends than adults (see also Moore and Maclean, 2012 ; Allen, 2014 ). However, if young people believe they are being bullied, but are unable to recognize their experiences within a predefined definition of bullying, this is likely to impact on their ability to report it.

Research from psychology, sociology, education and other disciplines, utilizing both quantitative and qualitative approaches, have enabled the generation of bullying knowledge to date. However, in order to understand why bullying happens and how it is influenced by wider social constructs there is a need for further qualitative studies, which hear directly from children and young people themselves. The next section of this paper discusses the theoretical underpinnings of this paper, which recognizes that young people are active agents in generating new bullying knowledge alongside adults.

Theoretical Underpinnings – Hearing From Children and Young People

The sociology of childhood ( James, 2007 ; Tisdall and Punch, 2012 ) and children’s rights agenda more broadly ( United Nations Convention on the Rights of the Child, 1989 ) have offered new understandings and methods for research which recognize children and young people as active agents and experts on their own lives. From this perspective, research is conducted with rather than on children and young people ( Kellett, 2010 ).

Participatory methodologies have proven particularly useful for involving young people in research as co-researchers (see for example O’Brien and Moules, 2007 ; Stoudt, 2009 ; Kellett, 2010 ; Spears et al., 2016 ). This process of enquiry actively involves those normally being studied in research activities. Previously, “traditional” researchers devalued the experiences of research participants arguing that due to their distance from them, they themselves are better equipped to interpret these experiences ( Beresford, 2006 ). However, Beresford (2006) suggests that the shorter the distance between direct experience and interpretation, the less distorted and inaccurate the resulting knowledge is likely to be. Jones (2004) further advocates that when young people’s voices are absent from research about them the research is incomplete. Certainly Spears et al. (2016) , adopted this approach in their study with the Young and Well Cooperative Research Centre (CRC) in Australia. Young people played an active role within a multidisciplinary team alongside researchers, practitioners and policymakers to co-create and co-evaluate the learning from four marketing campaigns for youth wellbeing through participatory research. Through this methodological approach, findings show that young people were able to reconceptualize mental health and wellbeing from their own perspectives as well as share their lived experiences with others ( Spears et al., 2016 ). Bland and Atweh (2007) , Ozer and Wright (2012) , highlight the benefits afforded to young people through this process, including participating in dialog with decision-makers and bringing aspects of teaching and learning to their attention.

Against this background, data presented for this paper represents findings from four studies underpinned by the ethos that bullying is socially constructed and is best understood by exploring the context to which it occurs ( Schott and Sondergaard, 2014 ; Eriksen, 2018 ). This socially constructed view focusses on the evolving positions within young people’s groups, and argues that within a bullying situation sometimes a young person is the bully, sometimes the victim and sometimes the bystander/witness, which contrasts the traditional view of bullying ( Schott and Sondergaard, 2014 ). The focus therefore is on group relationships and dynamics. For that reason, Horton (2011) proposes that if bullying is an extensive problem including many young people, then focusing entirely on personality traits will not generate new bullying knowledge and will be problematic in terms of interventions. It is important to acknowledge that this change in focus and view of bullying and how it is manifested in groups, does not negate the individual experiences of bullying rather the focus shifts to the process of being accepted, or not, by the group ( Schott and Sondergaard, 2014 ).

The Studies

This section provides a broad overview of the four included studies underpinned by participatory methodologies. Table 1 presents the details of each study. Young people were involved in the research process as respondents, co-researchers and commissioners of research, along a continuum as identified by Bragg and Fielding (2005) . This ranged from “active respondents” to the left of the continuum, “students as co-researchers” in the middle and “students as researchers” to the right of the continuum. Young people were therefore recognized as equal to adults in terms of what they can bring to the project from their own unique perspectives ( Bradbury-Jones et al., 2018 ).

A key finding from study one ( O’Brien, 2009 ) was the lack of voice afforded to young people through the research process and can be seen to reflect the far left of Bragg and Fielding (2005) continuum, as young people were not directly involved as “active respondents” but their views were included in secondary data analysis and informed the studies that followed. For example, the quantitative studies used an agreed academic definition of bullying which may or may not have influenced how young participants defined bullying within the studies. On the other hand, the qualitative study involved a group of students in deciding which questions to ask of the research participants and in interpreting the findings.

In contrast, study two ( O’Brien and Moules, 2010 ) was commissioned and led by a group of young people called PEAR (Public health, Education, Awareness, Researchers), who were established to advise on public health research in England. PEAR members were based in two large English cities and comprised 20 young people aged between 13 and 20 years. The premise of the study was that PEAR members wanted to commission research into cyber bullying and the effects this has on mental health from the perspectives of young people rather than adult perspectives. This project was innovative as young people commissioned the research and participated as researchers ( Davey, 2011 ) and can be seen to reflect the middle “students as co-researchers” as well as moving toward to right “students as researchers” of Bragg and Fielding (2005) continuum. Although the young people did not carry out the day-to-day work on the project, they were responsible for leading and shaping it. More importantly, the research topic and focus were decided with young people and adults together.

Study three ( O’Brien, 2016 ) involved five self-selecting students from an independent day and boarding school who worked with me to answer this question: What do young people in this independent day and boarding school view as the core issue of bullying in the school and how do they want to address this? These students called themselves R4U (Research for You) with the slogan researching for life without fear . Three cycles of Participatory Action Research (PAR) ensued, where decision making about direction of the research, including methods, analysis and dissemination of findings were made by the research team. As current students of the school, R4U had a unique “insider knowledge” that complemented my position as the “academic researcher.” By working together to generate understanding about bullying at the school, the findings thus reflected this diversity in knowledge. As the project evolved so too did the involvement of the young researchers and my knowledge as the “outsider” (see O’Brien et al., 2018a for further details). Similar to study two, this project is situated between the middle: “students as co-researchers” and the right: “students as researchers” of Bragg and Fielding (2005) continuum.

Study four ( O’Brien, 2017 ) was small-scale and involved interviewing four young people who were receiving support from a charity providing therapeutic and educational support to young people who self-exclude from school due to anxiety, as a result of bullying. Self-exclusion, for the purposes of this study, means that a young person has made a decision not to go to school. It is different from “being excluded” or “truanting” because these young people do not feel safe at school and are therefore too anxious to attend. Little is known about the experiences of young people who self-exclude due to bullying and this study helped to unravel some of these issues. This study reflects the left of Bragg and Fielding (2005) continuum where the young people were involved as “active respondents” in informing adult understanding of the issue.

A variety of research methods were used across the four studies including questionnaires, interviews and focus groups (see Table 1 for more details). In studies two and three, young researchers were fundamental in deciding the types of questions to be asked, where they were asked and who we asked. In study three the young researchers conducted their own peer-led interviews. The diversity of methods used across the studies are a strength for this paper. An over-reliance on one method is not portrayed and the methods used reflected the requirements of the individual studies.

Informed Consent

Voluntary positive agreement to participate in research is referred to as “consent” while “assent,” refers to a person’s compliance to participate ( Coyne, 2010 ). The difference in these terms are normally used to distinguish the “legal competency of children over and under 16 years in relation to research.” ( Coyne, 2010 , 228). In England, children have a legal right to consent so therefore assent is non-applicable ( Coyne, 2010 ). However, there are still tensions surrounding the ability of children and young people under the age of 18 years to consent in research which are related to their vulnerability, age and stage of development ( Lambert and Glacken, 2011 ). The research in the three empirical studies (two, three and four) started from the premise that all young participants were competent to consent to participate and took the approach of Coyne (2010) who argues that parental/carer consent is not always necessary in social research. University Research Ethics Committees (RECs) are nonetheless usually unfamiliar with the theoretical underpinnings that children are viewed as social actors and generally able to consent for themselves ( Lambert and Glacken, 2011 ; Fox, 2013 ; Parsons et al., 2015 ).

In order to ensure the young people in these reported studies were fully informed of the intentions of each project and to adhere to ethical principles, age appropriate participant information sheets were provided to all participants detailing each study’s requirements. Young people were then asked to provide their own consent by signing a consent form, any questions they had about the studies were discussed. Information sheets were made available to parents in studies three and four. In study two, the parents of young people participating in the focus groups were informed of the study through the organizations used to recruit the young people. My full contact details were provided on these sheets so parents/carers could address any queries they had about the project if they wished. When young people participated in the online questionnaire (study two) we did not know who they were so could not provide separate information to parents. Consequently, all participants were given the opportunity to participate in the research without the consent of their parents/carers unless they were deemed incompetent to consent. In this case the onus was on the adult (parent or carer for example) to prove incompetency ( Alderson, 2007 ). Favorable ethical approval, including approval for the above consent procedures, was granted by the Faculty Research Ethics Committee at Anglia Ruskin University.

In the next section I provide a synthesis of the findings across the four studies before discussing how participatory research with young people can offer new understandings of bullying and its impacts on young people.

Although each study was designed to answer specific bullying research questions, the following key themes cut across all four studies 1 :

• Bullying definitions

◦ Behaviors

• Impact of bullying on victim

• Reporting bullying

Bullying Definitions

Young people had various understandings about what they considered bullying to be. Overall, participants agreed that aggressive direct behaviors, mainly focusing on physical aggression, constituted bullying:

“…if someone is physically hurt then that is bullying straight away.” (Female, study 3).

“I think [cyber-bullying is] not as bad because with verbal or physical, you are more likely to come in contact with your attacker regularly, and that can be disturbing. However, with cyber-bullying it is virtual so you can find ways to avoid the person.” (Female, study 2).

Name-calling was an ambiguous concept, young people generally believed that in isolation name-calling might not be bullying behavior or it could be interpreted as “joking” or “banter”:

“I never really see any, a bit of name calling and taking the mick but nothing ever serious.” (Male, study 3).

The concept of “banter” or “joking” was explored in study three as a result of the participatory design. Young people suggested “banter” involves:

“…a personal joke or group banter has no intention to harm another, it is merely playful jokes.” (Female, study 3).

However, underpinning this understanding of “banter” was the importance of intentionality:

“Banter saying things bad as a joke and everyone knows it is a joke.” (Male, study 3).

“Banter” was thus contentious when perception and reception were ambiguous. In some cases, “banter” was considered “normal behavior”:

“…we’ve just been joking about, but it’s never been anything harsh it’s just been like having a joke…” (Male, study 3).

The same view was evident in relation to cyber-bullying. Some participants were rather dismissive of this approach suggesting that it did not exist:

“I don’t really think it exists. If you’re being cyber-“bullied” then there is something wrong with you- it is insanely easy to avoid, by blocking people and so on. Perhaps it consists of people insulting you online?” (Male, study 2).

When young people considered additional factors added to name calling such as the type of name-calling, or aspects of repetition or intention, then a different view was apparent.

“…but it has to be constant it can’t be a single time because that always happens.” (Male, study 3).

Likewise with words used on social media, young people considered intentionality in their consideration of whether particular behaviors were bullying, highlighting important nuances in how bullying is conceptualized:

“Some people they don’t want to sound cruel but because maybe if you don’t put a smiley face on it, it might seem cruel when sometimes you don’t mean it.” (Female, study 2).

Study one also found that young people were more likely to discuss sexist or racist bullying in interviews or focus groups but this information was scarce in the questionnaire data. This is possibly as a result of how the questions were framed and the researchers’ perspectives informing the questions.

Evident across the four studies was the understanding young people had about the effects of continuous name-calling on victims:

“…you can take one comment, you can just like almost brush it off, but if you keep on being bullied and bullied and bullied then you might kind of think, hang on a minute, they’ve taken it a step too far, like it’s actually become more personal, whereas just like a cheeky comment between friends it’s become something that’s more serious and more personal and more annoying or hurtful to someone.” (Female, study 3).

“Cyber-bullying is basically still verbal bullying and is definitely psychological bullying. Any bullying is psychological though, really. And any bullying is going to be harmful.” (Female, study 2).

Aspects of indirect bullying (social exclusion) were features of studies one and three. For the most part, the research reviewed in study one found that as young people got older they were less likely to consider characteristics of social exclusion in their definitions of bullying. In study three, when discussing the school’s anti-bullying policy, study participants raised questions about “ isolating a student from a friendship group .” Some contested this statement as a form of bullying:

“…. there is avoiding, as in, not actively playing a role in trying to be friends which I don’t really see as bullying I see this as just not getting someone to join your friendship group. Whereas if you were actually leaving him out and rejecting him if he tries to be friends then I think I would see that as malicious and bullying.” (Male, study 3).

“Isolating a student from a friendship group – I believe there are various reasons for which a student can be isolated from a group – including by choice.” (Female, study 3).

Cyber-bullying was explored in detail in study two but less so in the other three studies. Most study two participants considered that cyber-bullying was just as harmful, or in some cases worse than, ‘traditional’ bullying due to the use of similar forms of “harassment,” “antagonizing,” “tormenting,” and ‘threatening’ through online platforms. Some young people believed that the physical distance between the victim and the bully is an important aspect of cyber-bullying:

“I think it’s worse because people find it easier to abuse someone when not face to face.” (Male, study 2).

“I think it could be worse, because lots of other people can get involved, whereas when it’s physical bullying it’s normally just between one or two or a smaller group, things could escalate too because especially Facebook, they’ve got potential to escalate.” (Female, study 2).

Other participants in study two spoke about bullying at school which transfers to an online platform highlighting no “escape” for some. In addition, it was made clearer that some young people considered distancing in relation to bullying and how this influences perceptions of severity:

“…when there’s an argument it can continue when you’re not at school or whatever and they can continue it over Facebook and everyone can see it then other people get involved.” (Female, study 2).

“I was cyber-bullied on Facebook, because someone put several hurtful comments in response to my status updates and profile pictures. This actually was extended into school by the bully…” (Male, study 2).

Impact of Bullying on Victim

Although bullying behaviors were a primary consideration of young people’s understanding of bullying, many considered the consequences associated with bullying and in particular, the impact on mental health. In these examples, the specifics of the bullying event were irrelevant to young people and the focus was on how the behavior was received by the recipient.

In study two, young people divulged how cyber-bullying had adversely affected their ability to go to school and to socialize outside school. Indeed some young people reported the affects it had on their confidence and self-esteem:

“I developed anorexia nervosa. Although not the single cause of my illness, bullying greatly contributed to my low self-esteem which led to becoming ill.” (Female, study 2).

“It hurts people’s feelings and can even lead to committing suicide….” (Female, study 2).

Across the studies, young people who had been bullied themselves shared their individual experiences:

“….you feel insecure and it just builds up and builds up and then in the end you have no self-confidence.” (Female, study 2).

“…it was an everyday thing I just couldn’t take it and it was causing me a lot of anxiety.” (Male, study 4).

“I am different to everyone in my class …. I couldn’t take it no more I was upset all the time and it made me feel anxious and I wasn’t sleeping but spent all my time in bed being sad and unhappy.” (Male, study 4).

Young people who had not experienced bullying themselves agreed that the impact it had on a person was a large determiner of whether bullying had happened:

“When your self-confidence is severely affected and you become shy. Also when you start believing what the bullies are saying about you and start to doubt yourself.” (Female, study 3).

“…it makes the victim feel bad about themselves which mostly leads to depression and sadness.” (Male, study 2).

Further evidence around the impact of bullying was apparent in the data in terms of how relational aspects can affect perceived severity. In the case of cyber-bullying, young people suggested a sense of detachment because the bullying takes place online. Consequently, as the relational element is removed bullying becomes easier to execute:

“…because people don’t have to face them over a computer so it’s so much easier. It’s so much quicker as well cos on something like Facebook it’s not just you, you can get everyone on Facebook to help you bully that person.” (Female, study 2).

“Due to technology being cheaper, it is easier for young people to bully people in this way because they don’t believe they can be tracked.” (Male, study 2).

“The effects are the same and often the bullying can be worse as the perpetrator is unknown or can disguise their identity. Away from the eyes of teachers etc., more can be done without anyone knowing.” (Female, study 2).

Relational aspects of bullying were further highlighted with regards to how “banter” was understood, particularly with in-group bullying and how the same example can either be seen as “banter” or bullying depending on the nature of the relationship:

“…we’ve just been joking about, but it’s never been anything harsh it’s just been like having a joke. well, I haven’t done it but I’ve been in a crowd where people do it, so I don’t want to get involved just in case it started an argument.” (Female, study 3).

“But it also depends…who your groups with, for example, if I spoke to my friends from [School]… I wouldn’t like use taboo language with them because to them it may seem inappropriate and probably a bit shocked, but if I was with my friends outside of school we use taboo language, we’ll be ourselves and we’ll be comfortable with it, and if a stranger walked past and heard us obviously they’d be thinking that we’re being bullied ourselves.” (Female, study 3).

Furthermore, how individuals are perceived by others tended to influence whether they were believed or not. In study four for example, participants suggested that who the bullies were within the school might have impacted how complaints were acted upon by school officials:

“When I went to the school about it, the students said I had attacked them – all eight of them! I just realized that no one believes me….” (Female, study 4).

While in study three, a characteristic of bullying was the influence the aggressor has over the victim:

“When the victim starts to feel in danger or start to fear the other person. Consequently he or she tries to avoid the bad guy (or girl!)” (Male, study 3).

These relational and contextual issues also influenced a young person’s ability to report bullying.

Reporting Bullying

Young people were more likely to report bullying when they considered it was ‘serious’ enough. Just under half of participants in study two sought emotional/practical support if they worried about, or were affected by cyber-bullying, with most talking to their parents. In study three, young people were less likely to seek support but when they did, most went to their teachers. In study four, all participants reported bullying in school where they did not feel supported.

Fear of making the bullying worse was captured across the studies as a reason for not reporting it:

“I’m scared that if I tell then the bullying will still go on and they will do more.” (Female, study 3).

“The bully might bully you if he finds out.” (Male, study 3).

Being able to deal with the incident themselves was also a reason for non-reporting:

“…it’s embarrassing and not necessary, my friends help me through it, adults never seem to understand.” (Female, study 2).

“I don’t tend to talk to anyone about it, I just keep it to myself and obviously that’s the worst thing you should ever do, you should never keep it to yourself, because I regret keeping it to myself to be honest….” (Female, study 3).

“…but I think I’d deal with it myself ‘cos. I was quite insecure but now I’m quite secure with myself, so I’ll sort it out myself. I think it’s just over time I’ve just sort of hardened to it.” (Male, study 3).

Most young people seeking support for bullying said they spoke to an adult but the helpfulness of this support varied. This finding is important for understanding relationships between young people and adults. Those who felt supported by their teachers for example, suggested that they took the time to listen and understood what they were telling them. They also reassured young people who in turn believed that the adult they confided in would know what to do:

“So I think the best teacher to talk to is [Miss A] and even though people are scared of her I would recommend it, because she’s a good listener and she can sense when you don’t want to talk about something, whereas the other teachers force it out of you.” (Female, study 3).

“My school has had assemblies about cyber-bullying and ways you can stop it or you can report it anonymously…. you can write your name or you can’t, it’s all up to YOU.” (Male, study 2).

Others however had a negative experience of reporting bullying and a number of reasons were provided as to why. Firstly, young people stated that adults did not believe them which made the bullying worse on some level:

“I went to the teachers a couple of times but, no, I don’t think they could do anything. I did sort of go three times and it still kept on going, so I just had to sort of deal with it and I sort of took it on the cheek….” (Male, study 3).

Secondly, young people suggested that adults did not always listen to their concerns, or in some cases did not take their concerns seriously enough:

“…I had had a really bad day with the girls so I came out and I explained all this to my head of year and how it was affecting me but instead of supporting me he put me straight into isolation.” (Male, study 4).

“I could understand them thinking I maybe got the wrong end of the stick with one incident but this was 18 months of me constantly reporting different incidents.” (Female, study 4).

“If cyber-bullying is brought to our school’s attention, usually, they expect printed proof of the situation and will take it into their own hand depending on its seriousness. However this is usually a couple of detentions. And it’s just not enough.” (Female, study 2).

Finally, some young people suggested that teachers did not always know what to do when bullying concerns were raised and consequently punished those making the complaint:

“I think I would have offered support instead of punishment to someone who was suffering with anxiety. I wouldn’t have seen anxiety as bad behavior I think that’s quite ignorant but they saw it as bad behavior.” (Male, study 4).

It is worth reiterating, that the majority of young people across the studies did not report bullying to anybody , which further underscores the contextual issues underpinning bullying and its role in enabling or disabling bullying behaviors. Some considered it was “pointless” reporting the bullying and others feared the situation would be made worse if they did:

“My school hide and say that bullying doesn’t go on cos they don’t wanna look bad for Ofsted.” (Male, study 2).

“My school is oblivious to anything that happens, many things against school rules happen beneath their eyes but they either refuse to acknowledge it or are just not paying attention so we must suffer.” (Female, study 2).

“That’s why I find that when you get bullied you’re scared of telling because either, in most cases the teacher will – oh yeah, yeah, don’t worry, we’ll sort it out and then they don’t tend to, and then they get bullied more for it.” (Female, study 3).

Young people were concerned that reporting bullying would have a negative impact on their friendship groups. Some were anxious about disrupting the status quo within:

“I think everyone would talk about me behind my back and say I was mean and everyone would hate me.” (Female, study 3).

Others expressed concern about the potential vulnerability they were likely to experience if they raised concerns of bullying:

“I was worried it might affect my other friendships.”(Boy, study 2).

“I’m scared that if I tell, then the bullying will still go on and they will do more.” (Female, study 3).

“….because they might tell off the bullies and then the bullies will like get back at you.” (Female, study 3).

These findings underscore the importance of contextual and relational factors in understanding bullying from the perspectives of young people and how these factors influence a young person’s ability or willingness to report bullying.

Finally one young person who had self-excluded from school due to severe bullying suggested that schools:

“…need to be looking out for their students’ mental wellbeing – not only be there to teach them but to support and mentor them. Keep them safe really… I missed out on about three years of socializing outside of school because I just couldn’t do it. I think it’s important that students are encouraged to stand up for each other.” (Female, study 4).

The studies presented in this paper illustrate the multitude of perceptions underpinning young people’s understandings of what constitutes bullying, both in terms of the behavior and also the impact that this behavior has on an individual. In turn, the ambiguity of what constitutes bullying had an impact on a young person’s ability to seek support. Discrepancies in bullying perceptions within and between young people’s groups are shown, highlighting the fluid and changing roles that occur within a bullying situation. Findings from quantitative studies have demonstrated the differing perceptions of bullying by adults and young people (see for example Smith et al., 2002 ; Vaillancourt et al., 2008 ; Maunder et al., 2010 ; Cuadrado-Gordillo, 2012 ). However, by combining findings from participatory research, new understandings of the relational and contextual factors important to young people come to the fore.

Young people participating in these four studies had unique knowledge and experiences of bullying and the social interactions of other young people in their schools and wider friendship groups. The underpinning participatory design enabled me to work alongside young people to analyze and understand their unique perspectives of bullying in more detail. The research teams were therefore able to construct meaning together, based not entirely on our own assumptions and ideologies, but including the viewpoint of the wider research participant group ( Thomson and Gunter, 2008 ). Together, through the process of co-constructing bullying knowledge, we were able to build on what is already known in this field and contribute to the view that bullying is socially constructed through the experiences of young people and the groups they occupy ( Schott and Sondergaard, 2014 ).

With regards to understanding what bullying is, the findings from these studies corroborate those of the wider literature from both paradigms of inquiry (for example Naylor et al., 2001 ; Canty et al., 2016 ); that being the discrepancies in definitions between adults and young people and also between young people themselves. Yet, findings here suggest that young people’s bullying definitions are contextually and relationally contingent. With the exception of physical bullying, young people did not differentiate between direct or indirect behaviors, instead they tended to agree that other contextual and relational factors played a role in deciding if particular behaviors were bullying (or not). The participatory research design enabled reflection and further investigation of the ideas that were particularly important to young people such as repetition and intentionality. Repetition was generally seen as being indicative of bullying being “serious,” and therefore more likely to be reported, and without repetition, a level of normality was perceived. This finding contradicts some work on bullying definitions, Cuadrado-Gordillo (2012) for example found that regardless of the role played by young people in a bullying episode (victim, aggressor or witness), the criteria of ‘repetition’ was not important in how they defined bullying.

Relational factors underpinning young people’s perception of bullying and indeed it’s “seriousness” were further reflected in their willingness or otherwise to report it. Fear of disrupting the status quo of the wider friendship group, potentially leading to their own exclusion from the group, was raised as a concern by young people. Some were concerned their friends would not support them if they reported bullying, while others feared further retaliation as a result. Friendship groups have been identified as a source of support for those who have experienced bullying and as a protective factor against further bullying ( Allen, 2014 ). Although participants did not suggest their friendship groups are unsupportive it is possible that group dynamics underscore seeking (or not) support for bullying. Other literature has described such practices as evidence of a power imbalance ( Olweus, 1995 ; Cuadrado-Gordillo, 2012 ) but young people in these studies did not describe these unequal relationships in this way and instead focused on the outcomes and impacts of bullying. Indeed Cuadrado-Gordillo (2012) also found that young people in their quantitative study did not consider “power imbalance” in their understanding of bullying and were more likely to consider intention. This paper, however, underscores the relational aspects of definitions of bullying and, how the dynamics of young people’s friendships can shift what is understood as bullying or not. Without such nuances, some behaviors may be overlooked as bullying, whereas other more obvious behaviors draw further attention. This paper also shows that contextual issues such as support structures can shift how young people see bullying. Contextual factors were evident across the four studies through the recognition of bullying being enabled or disabled by institutional factors, including a school’s ability to respond appropriately to bullying concerns. Young people suggested that schools could be influenced by bullies, perceiving them as non-threatening and consequently not dealing appropriately with the situation. Indeed some young people reported that their schools placed the onus on them as victims to change, consequently placing the “blame” on victims instead. These findings raise questions about who young people feel able to confide in about bullying as well as issues around training and teacher preparedness to deal with bullying in schools. Evidenced in these four studies, is that young people feel somewhat disconnected from adults when they have bullying concerns. Those who did report bullying, identified particular individuals they trusted and knew would support them. Novick and Isaacs (2010) identified teachers who young people felt comfortable in approaching to report bullying and described them as “most active, engaged and responsive.” (p. 291). The bullying literature suggests that as young people get older they are more likely to confide in friends than adults ( Moore and Maclean, 2012 ; Allen, 2014 ). However, findings from this paper indicate that although fewer young people reported bullying, those who did confided in an adult. Young people have identified that a variety of supports are required to tackle bullying and that adults need to listen and work with them so nuanced bullying behaviors are not recognized as “normal” behaviors. Within the data presented in this paper, “banter” was portrayed as “normal” behavior. Young people did not specify what behaviors they regarded as “banter,” but suggested that when banter is repeated and intentional the lines are blurred about what is bullying and what is banter.

Exploring bullying nuances in this paper, was enhanced by the involvement of young people in the research process who had a unique “insider” perspective about what it is like to be a young person now and how bullying is currently affecting young people. In studies one and four, young people were “active respondents” ( Bragg and Fielding, 2005 ) and provided adults with their own unique perspectives on bullying. It could be argued that study one did not involve the participation of young people. However, this study informed the basis of the subsequent studies due to the discrepancies noted in the literature about how bullying is understood between adults and young people, as well as the lack of young people’s voice and opportunity to participate in the reviewed research. Accordingly, young people’s data as “active respondents” informed adult understanding and led to future work involving more active research engagement from other young people. Participation in study four provided an opportunity for young people to contribute to future participatory research based on lived experiences as well as informing policy makers of the effects bullying has on the lives of young people ( O’Brien, 2017 ). In studies two and three, young people were involved further along Bragg and Fielding (2005) continuum as “co-researchers” and “students as researchers” with these roles shifting and moving dependent on the context of the project at the time ( O’Brien et al., 2018a ). These young researchers brought unique knowledge to the projects ( Bradbury-Jones et al., 2018 ) that could not be accessed elsewhere. Perspectives offered by the young researchers supported adults in understanding more about traditional and cyber-bullying from their perspectives. Furthermore, this knowledge can be added to other, quantitative studies to further understand why bullying happens alongside bullying prevalence, risk and protective factors, and negative outcomes.

Findings from the four studies offer an alternative perspective to how bullying is understood by young people. Complexities in defining bullying have been further uncovered as understanding is informed by individual factors, as well as wider social and relational contexts ( Horton, 2011 ; Schott and Sondergaard, 2014 ). This has implications for the type of support young people require. This paper highlights how definitions of bullying shift in response to relational and contextual aspects deemed important to young people. Because of this, further nuances were uncovered through the research process itself as the respective studies showed discrepancies in bullying perceptions within and between young people’s groups.

These understandings can act as a starting point for young people and adults to collaborate in research which seeks to understand bullying and the context to which it occurs. Furthermore, such collaborations enable adults to theorize and understand the complexities associated with bullying from the perspective of those at the center. There is a need for additional participatory research projects involving such collaborations where adults and young people can learn from each other as well as combining findings from different methodologies to enable a more comprehensive picture of the issues for young people to emerge. Further research is needed to unravel the complexities of bullying among and between young people, specifically in relation to the contextual and relational factors underscoring perceptions of bullying.

Data Availability

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

Ethics Statement

Ethical approval was granted for all four studies from the Faculty of Health, Education, Medicine and Social Care at the Anglia Ruskin University. The research was conducted on the premise of Gillick competency meaning that young people (in these studies over the age of 12 years) could consent for themselves to participate. Parents/carers were aware the study was happening and received information sheets explaining the process.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

These four studies were conducted at the Anglia Ruskin University. Study one was part of a wider masters degree funded by the Anglia Ruskin University, Study two was funded by a group of young people convened by the National Children’s Bureau with funding from the Wellcome Trust (United Kingdom). Study three was a wider Doctoral study funded by the Anglia Ruskin University and Study four was also funded by the Anglia Ruskin University.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

I would like to thank Dr. Grace Spencer, Ruskin Fellow at the Anglia Ruskin University for providing the critical read of this manuscript and offering constructive feedback. I would also like to thank the two independent reviewers for their feedback on the drafts of this manuscript.

• ^ These findings focus on perceptions and data from the young people in the four studies. For a full discussion on adult perceptions please refer to the individual studies.

Alderson, P. (2007). Competent children? Minors’ consent to health care treatment and research. Soc. Sci. Med. 65, 2272–2283. doi: 10.1016/j.socscimed.2007.08.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Allen, M. (2014). Local Action on Health Inequalities: Building Children and Young People’s Resilience in Schools , London: Public Health England.

Google Scholar

Beresford, P. (2006). Making the connections with direct experience: from the western front to user-controlled research. Educ. Action Res. 14, 161–170.

Black, S., Weinles, D., and Washington, E. (2010). Victim strategies to stop bullying. Youth Violence Juv. Justice 8, 138–147. doi: 10.1177/1541204009349401

CrossRef Full Text | Google Scholar

Bland, D., and Atweh, B. (2007). Students as researchers: engaging students’ voices in PAR. Educ. Action Res. 15, 337–349. doi: 10.1080/09650790701514259

Boulton, M. J., and Flemington, I. (1996). The effects of a short video intervention on secondary school Pupils’ involvement in definitions of and attitudes towards bullying. Sch. Psychol. Int. 17, 331–345. doi: 10.1177/0143034396174003

Bradbury-Jones, C., Isham, L., and Taylor, J. (2018). The complexities and contradictions in participatory research with vulnerable children and young people: a qualitative systematic review. Soc. Sci. Med. 215, 80–91. doi: 10.1016/j.socscimed.2018.08.038

Bragg, S., and Fielding, M. (2005). “It’s an equal thing. It’s about achieving together: student voices and the possibility of a radical collegiality,” in Improving Schools Through Collaborative Enquiry , eds H. Street, and J. Temperley, (London: Continuum), 105–135.

Braun, V., and Clarke, V. (2006). Using thematic analysis in Psychology. Qual. Res. Psychol. 3, 77–101.

Canty, J., Stubbe, M., Steers, D., and Collings, S. (2016). The trouble with bullying–deconstructing the conventional definition of bullying for a child-centred investigation into Children’s use of social media. Child. Soc. 30, 48–58. doi: 10.1111/chso.12103

Coyne, I. (2010). Research with children and young people: the issue of parental (proxy) consent. Child. Soc. 24, 227–237.

Cuadrado-Gordillo, I. (2012). Repetition, power imbalance, and intentionality: do these criteria conform to teenagers’ perception of bullying? A role-based analysis. J. Interpers. Violence 27, 1889–1910. doi: 10.1177/0886260511431436

Davey, C. (2011). Evaluation of the PEAR Project. London: National Children’s Bureau.

deLara, E. W. (2012). Why adolescents Don’t disclose incidents of bullying and harassment. J. Sch. Violence 11, 288–305. doi: 10.1080/15388220.2012.705931

Eriksen, I. M. (2018). The power of the word: students’ and school staff’s use of the established bullying definition. Educ. Res. 60, 157–170. doi: 10.1080/00131881.2018.1454263

Espelage, D. L. (2018). Understanding the complexity of school bully involvement. Chautauqua J. 2:20.

Forsberg, C., Wood, L., Smith, J., Varjas, K., Meyers, J., Jungert, T., et al. (2018). Students’ views of factors affecting their bystander behaviors in response to school bullying: a cross-collaborative conceptual qualitative analysis. Res. Pap. Educ. 33, 127–142. doi: 10.1080/02671522.2016.1271001

Fox, R. (2013). Resisting participation: critiquing participatory research methodologies with young people. J. Youth Stud. 16, 986–999. doi: 10.1080/13676261.2013.815698

Griffin, R. S., and Gross, A. M. (2004). Childhood bullying: current empirical findings and future directions for research. Aggr. Violent Behav. 9, 379–400. doi: 10.1016/s1359-1789(03)00033-8

Guerin, S., and Hennessy, E. (2002). Pupils’ definitions of bullying. Eur. J. Psychol. Educ. 17, 249–261. doi: 10.1007/bf03173535

Horton, P. (2011). School bullying and social and moral orders. Child. Soc. 25, 268–277. doi: 10.1111/j.1099-0860.2011.00377.x

Huang, F. L., and Cornell, D. G. (2015). The impact of definition and question order on the prevalence of bullying victimization using student self-reports. Psychol. Assess. 27:1484. doi: 10.1037/pas0000149

James, A. (2007). Giving voice to children’s voices: practices and problems, pitfalls and potentials. Am. Anthropol. 109, 261–272. doi: 10.1525/aa.2007.109.2.261

Jones, A. (2004). “Involving children and yong people as researchers,” in Doing Research with Children and Young People , eds S. Fraser, V. Lewis, S. Ding, M. Kellett, and C. Robinson, (London: Sage Publications), 113–130.

Kellett, M. (2010). Small shoes, Big Steps! Empowering children as active researchers. Am. J. Commun. Psychol. 46, 195–203. doi: 10.1007/s10464-010-9324-y

La Fontaine, J. (1991). Bullying: The Child’s View – an Analysis of Telephone Calls to ChildLIne about Bullying. London: Calouste Gulbenkian Foundation.

Lambert, V., and Glacken, M. (2011). Engaging with children in research: theoretical and practical implications of negotiating informed consent/assent. Nurs. Ethics 18, 781–801. doi: 10.1177/0969733011401122

Lee, C. (2006). Exploring teachers’ definitions of bullying. Emot. Behav. Diffic. 11, 61–75. doi: 10.1080/13632750500393342

Maunder, R. E., and Crafter, S. (2018). School bullying from a sociocultural perspective. Aggr. Violent Behav. 38, 13–20. doi: 10.1016/j.avb.2017.10.010

Maunder, R. E., Harrop, A., and Tattersall, A. J. (2010). Pupil and staff perceptions of bullying in secondary schools: comparing behavioural definitions and their perceived seriousness. Educ. Res. 52, 263–282. doi: 10.1080/00131881.2010.504062

Moore, S., and Maclean, R. (2012). Victimization, friendship and resilience: crossing the land in-between. Pastor. Care Educ. 30, 147–163. doi: 10.1080/02643944.2012.679956

Naylor, P., Cowie, H., and del Rey, R. (2001). Coping strategies of secondary school children in response to being bullied. Child Psychol. Psychiatry Rev. 6, 114–120. doi: 10.1111/j.1469-7610.2009.02137.x

Novick, R. M., and Isaacs, J. (2010). Telling is compelling: the impact of students reports of bullying on teacher intervention. Educ. Psychol. 30, 283–296. doi: 10.1080/01443410903573123

O’Brien, N. (2009). Secondary school teachers’ and pupils’ definitions of bullying in the UK: a systematic review. Evid. Policy 5, 399–426.

PubMed Abstract | Google Scholar

O’Brien, N. (2014). “I Didn’t Want to be Known as a Snitch”: Using PAR to Explore Bullying in a Private day and Boarding School. Childhood Remixed. Conference Edition. Suffolk: University Campus Suffolk, 86–96.

O’Brien, N. (2016). To ‘Snitch’ or Not to ‘Snitch’? Using PAR to Explore Bullying in a Private Day and Boarding School. Available at: http://arro.anglia.ac.uk/700970/ (accessed September 20, 2018).

O’Brien, N. (2017). An Exploratory Study of Bullied Young People’s Self-Exclusion from School. Evidence: Presented at Meetings of the All Party Parliamentary Group on Bullying 2011-2016. Project Report. All Party Parliamentary Group on Bullying. Available at: http://arro.anglia.ac.uk/id/eprint/702024 (accessed September 20, 2018).

O’Brien, N., and Moules, T. (2007). So round the spiral again: a reflective participatory research project with children and young people. Educ. Action Res. J. 15, 385–402. doi: 10.1080/09650790701514382

O’Brien, N., and Moules, T. (2010). The Impact of Cyber-Bullying on Young People’s Mental Health. Project Report. Chelmsford: Anglia Ruskin University.

O’Brien, N., and Moules, T. (2013). Not sticks and stones but tweets and texts: findings from a national cyberbullying project. Pastor. Care Educ. 31, 53–65. doi: 10.1080/02643944.2012.747553

O’Brien, N., Moules, T., and Munn-Giddings, C. (2018a). “Negotiating the research space between young people and adults in a PAR study exploring school bullying,” in Reciprocal Relationships and Well-Being: Implications for Social Work and Social Policy , eds M. Torronen, C. Munn-Giddings, and L. Tarkiainen, (Oxon: Routledge), 160–175. doi: 10.4324/9781315628363-11

O’Brien, N., Munn-Giddings, C., and Moules, T. (2018b). The repercussions of reporting bullying: some experiences of students at an independent secondary school. Pastor. Care Educ. 36, 29–43. doi: 10.1080/02643944.2017.1422004

O’Brien, N., Munn-Giddings, C., and Moules, T. (2018c). The Ethics of Involving Young People Directly in the Research Process. Childhood Remixed. Conference Edition , 115–128. Available at: www.uos.ac.uk/content/centre-for-study-children-childhood (accessed May 2018).

Oliver, C., and Candappa, M. (2007). Bullying and the politics of ‘telling’. Oxford Rev. Educ. 33, 71–86. doi: 10.1080/03054980601094594

Olweus, D. (1995). Bullying or peer abuse at school: facts and intervention. Curr. Dir. Psychol. Sci. 4, 196–200. doi: 10.1111/1467-8721.ep10772640

Ozer, E. J., and Wright, D. (2012). Beyond school spirit: the effects of youth-led participatory action research in two urban high schools. J. Res. Adolesc. 22, 267–283. doi: 10.1111/j.1532-7795.2012.00780.x

Parsons, S., Abbott, C., McKnight, L., and Davies, C. (2015). High risk yet invisible: conflicting narratives on social research involving children and young people, and the role of research ethics committees. Br. Educ. Res. J. 41, 709–729. doi: 10.1002/berj.3160

Patton, D. U., Hong, J. S., Patel, S., and Kral, M. J. (2017). A systematic review of research strategies used in qualitative studies on school bullying and victimization. Trauma Violence Abuse 18, 3–16. doi: 10.1177/1524838015588502

Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., et al. (2006). Guidance on the conduct of narrative synthesis in systematic reviews. Eur. Soc. Res. Council Methods Program. doi: 10.13140/2.1.1018.4643

Schott, R. M., and Sondergaard, D. M. (2014). “Introduction: new approaches to school bullying,” in School Bullying: New Theories in Context , eds R. M. Schott, and D. M. Sondergaard, (Massachusetts, MA: Cambridge University Press), 1–17.

Skrzypiec, G., Slee, P., Murray-Harvey, R., and Pereira, B. (2011). School bullying by one or more ways: does it matter and how do students cope? Sch. Psychol. Int. 32, 288–311. doi: 10.1177/0143034311402308

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 Dev. 73, 1119–1133. doi: 10.1111/1467-8624.00461

Spears, B., Taddeo, C., Collin, P., Swist, T., Razzell, M., Borbone, V., et al. (2016). Safe and Well Online: Learnings from Four Social Marketing Campaigns for Youth Wellbeing. Available at: https://researchdirect.westernsydney.edu.au/islandora/object/uws:36405/datastream/PDF/view (accessed July 1, 2019).

Stoudt, B. G. (2009). The role of language & discourse in the investigation of privilege: using participatory action research to discuss theory. Dev. Methodol. Interrupt. Power Urban Rev. 41, 7–28.

Thomson, P., and Gunter, H. (2008). Researching Bullying with students: a lens on everyday life in an ‘innovative school’. Int. J. Inclusive Educ. 12, 185–200. doi: 10.1080/13603110600855713

Tisdall, E. K. M., and Punch, S. (2012). Not so ‘new’? Looking critically at childhood studies. Child. Geogr. 10, 249–264. doi: 10.1080/14733285.2012.693376

United Nations Convention on the Rights of the Child (1989). Available at: http://www.unicef.org.uk/Documents/Publication-pdfs/UNCRC_PRESS2009 10web.pdf (accessed January 19, 2014).

Vaillancourt, T., McDougall, P., Hymel, S., Krygsman, A., Miller, J., Stiver, K., et al. (2008). Bullying: are researchers and children/youth talking about the same thing? Int. J. Behav. Dev. 32, 486–495. doi: 10.1177/0165025408095553

Keywords : bullying, young people, participatory research, social constructionism, young people as researchers, collaboration, bullying supports

Citation: O’Brien N (2019) Understanding Alternative Bullying Perspectives Through Research Engagement With Young People. Front. Psychol. 10:1984. doi: 10.3389/fpsyg.2019.01984

Received: 28 February 2019; Accepted: 13 August 2019; Published: 28 August 2019.

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*Correspondence: Niamh O’Brien, [email protected]

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Bullying at school and mental health problems among adolescents: a repeated cross-sectional study

• Håkan Källmén 1 &
• Mats Hallgren   ORCID: orcid.org/0000-0002-0599-2403 2  

Child and Adolescent Psychiatry and Mental Health volume  15 , Article number:  74 ( 2021 ) Cite this article

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To examine recent trends in bullying and mental health problems among adolescents and the association between them.

A questionnaire measuring mental health problems, bullying at school, socio-economic status, and the school environment was distributed to all secondary school students aged 15 (school-year 9) and 18 (school-year 11) in Stockholm during 2014, 2018, and 2020 (n = 32,722). Associations between bullying and mental health problems were assessed using logistic regression analyses adjusting for relevant demographic, socio-economic, and school-related factors.

The prevalence of bullying remained stable and was highest among girls in year 9; range = 4.9% to 16.9%. Mental health problems increased; range = + 1.2% (year 9 boys) to + 4.6% (year 11 girls) and were consistently higher among girls (17.2% in year 11, 2020). In adjusted models, having been bullied was detrimentally associated with mental health (OR = 2.57 [2.24–2.96]). Reports of mental health problems were four times higher among boys who had been bullied compared to those not bullied. The corresponding figure for girls was 2.4 times higher.

Conclusions

Exposure to bullying at school was associated with higher odds of mental health problems. Boys appear to be more vulnerable to the deleterious effects of bullying than girls.

Introduction

Bullying involves repeated hurtful actions between peers where an imbalance of power exists [ 1 ]. Arseneault et al. [ 2 ] conducted a review of the mental health consequences of bullying for children and adolescents and found that bullying is associated with severe symptoms of mental health problems, including self-harm and suicidality. Bullying was shown to have detrimental effects that persist into late adolescence and contribute independently to mental health problems. Updated reviews have presented evidence indicating that bullying is causative of mental illness in many adolescents [ 3 , 4 ].

There are indications that mental health problems are increasing among adolescents in some Nordic countries. Hagquist et al. [ 5 ] examined trends in mental health among Scandinavian adolescents (n = 116, 531) aged 11–15 years between 1993 and 2014. Mental health problems were operationalized as difficulty concentrating, sleep disorders, headache, stomach pain, feeling tense, sad and/or dizzy. The study revealed increasing rates of adolescent mental health problems in all four counties (Finland, Sweden, Norway, and Denmark), with Sweden experiencing the sharpest increase among older adolescents, particularly girls. Worsening adolescent mental health has also been reported in the United Kingdom. A study of 28,100 school-aged adolescents in England found that two out of five young people scored above thresholds for emotional problems, conduct problems or hyperactivity [ 6 ]. Female gender, deprivation, high needs status (educational/social), ethnic background, and older age were all associated with higher odds of experiencing mental health difficulties.

Bullying is shown to increase the risk of poor mental health and may partly explain these detrimental changes. Le et al. [ 7 ] reported an inverse association between bullying and mental health among 11–16-year-olds in Vietnam. They also found that poor mental health can make some children and adolescents more vulnerable to bullying at school. Bayer et al. [ 8 ] examined links between bullying at school and mental health among 8–9-year-old children in Australia. Those who experienced bullying more than once a week had poorer mental health than children who experienced bullying less frequently. Friendships moderated this association, such that children with more friends experienced fewer mental health problems (protective effect). Hysing et al. [ 9 ] investigated the association between experiences of bullying (as a victim or perpetrator) and mental health, sleep disorders, and school performance among 16–19 year olds from Norway (n = 10,200). Participants were categorized as victims, bullies, or bully-victims (that is, victims who also bullied others). All three categories were associated with worse mental health, school performance, and sleeping difficulties. Those who had been bullied also reported more emotional problems, while those who bullied others reported more conduct disorders [ 9 ].

As most adolescents spend a considerable amount of time at school, the school environment has been a major focus of mental health research [ 10 , 11 ]. In a recent review, Saminathen et al. [ 12 ] concluded that school is a potential protective factor against mental health problems, as it provides a socially supportive context and prepares students for higher education and employment. However, it may also be the primary setting for protracted bullying and stress [ 13 ]. Another factor associated with adolescent mental health is parental socio-economic status (SES) [ 14 ]. A systematic review indicated that lower parental SES is associated with poorer adolescent mental health [ 15 ]. However, no previous studies have examined whether SES modifies or attenuates the association between bullying and mental health. Similarly, it remains unclear whether school related factors, such as school grades and the school environment, influence the relationship between bullying and mental health. This information could help to identify those adolescents most at risk of harm from bullying.

To address these issues, we investigated the prevalence of bullying at school and mental health problems among Swedish adolescents aged 15–18 years between 2014 and 2020 using a population-based school survey. We also examined associations between bullying at school and mental health problems adjusting for relevant demographic, socioeconomic, and school-related factors. We hypothesized that: (1) bullying and adolescent mental health problems have increased over time; (2) There is an association between bullying victimization and mental health, so that mental health problems are more prevalent among those who have been victims of bullying; and (3) that school-related factors would attenuate the association between bullying and mental health.

Participants

The Stockholm school survey is completed every other year by students in lower secondary school (year 9—compulsory) and upper secondary school (year 11). The survey is mandatory for public schools, but voluntary for private schools. The purpose of the survey is to help inform decision making by local authorities that will ultimately improve students’ wellbeing. The questions relate to life circumstances, including SES, schoolwork, bullying, drug use, health, and crime. Non-completers are those who were absent from school when the survey was completed (< 5%). Response rates vary from year to year but are typically around 75%. For the current study data were available for 2014, 2018 and 2020. In 2014; 5235 boys and 5761 girls responded, in 2018; 5017 boys and 5211 girls responded, and in 2020; 5633 boys and 5865 girls responded (total n = 32,722). Data for the exposure variable, bullied at school, were missing for 4159 students, leaving 28,563 participants in the crude model. The fully adjusted model (described below) included 15,985 participants. The mean age in grade 9 was 15.3 years (SD = 0.51) and in grade 11, 17.3 years (SD = 0.61). As the data are completely anonymous, the study was exempt from ethical approval according to an earlier decision from the Ethical Review Board in Stockholm (2010-241 31-5). Details of the survey are available via a website [ 16 ], and are described in a previous paper [ 17 ].

Students completed the questionnaire during a school lesson, placed it in a sealed envelope and handed it to their teacher. Student were permitted the entire lesson (about 40 min) to complete the questionnaire and were informed that participation was voluntary (and that they were free to cancel their participation at any time without consequences). Students were also informed that the Origo Group was responsible for collection of the data on behalf of the City of Stockholm.

Study outcome

Mental health problems were assessed by using a modified version of the Psychosomatic Problem Scale [ 18 ] shown to be appropriate for children and adolescents and invariant across gender and years. The scale was later modified [ 19 ]. In the modified version, items about difficulty concentrating and feeling giddy were deleted and an item about ‘life being great to live’ was added. Seven different symptoms or problems, such as headaches, depression, feeling fear, stomach problems, difficulty sleeping, believing it’s great to live (coded negatively as seldom or rarely) and poor appetite were used. Students who responded (on a 5-point scale) that any of these problems typically occurs ‘at least once a week’ were considered as having indicators of a mental health problem. Cronbach alpha was 0.69 across the whole sample. Adding these problem areas, a total index was created from 0 to 7 mental health symptoms. Those who scored between 0 and 4 points on the total symptoms index were considered to have a low indication of mental health problems (coded as 0); those who scored between 5 and 7 symptoms were considered as likely having mental health problems (coded as 1).

Primary exposure

Experiences of bullying were measured by the following two questions: Have you felt bullied or harassed during the past school year? Have you been involved in bullying or harassing other students during this school year? Alternatives for the first question were: yes or no with several options describing how the bullying had taken place (if yes). Alternatives indicating emotional bullying were feelings of being mocked, ridiculed, socially excluded, or teased. Alternatives indicating physical bullying were being beaten, kicked, forced to do something against their will, robbed, or locked away somewhere. The response alternatives for the second question gave an estimation of how often the respondent had participated in bullying others (from once to several times a week). Combining the answers to these two questions, five different categories of bullying were identified: (1) never been bullied and never bully others; (2) victims of emotional (verbal) bullying who have never bullied others; (3) victims of physical bullying who have never bullied others; (4) victims of bullying who have also bullied others; and (5) perpetrators of bullying, but not victims. As the number of positive cases in the last three categories was low (range = 3–15 cases) bully categories 2–4 were combined into one primary exposure variable: ‘bullied at school’.

Assessment year was operationalized as the year when data was collected: 2014, 2018, and 2020. Age was operationalized as school grade 9 (15–16 years) or 11 (17–18 years). Gender was self-reported (boy or girl). The school situation To assess experiences of the school situation, students responded to 18 statements about well-being in school, participation in important school matters, perceptions of their teachers, and teaching quality. Responses were given on a four-point Likert scale ranging from ‘do not agree at all’ to ‘fully agree’. To reduce the 18-items down to their essential factors, we performed a principal axis factor analysis. Results showed that the 18 statements formed five factors which, according to the Kaiser criterion (eigen values > 1) explained 56% of the covariance in the student’s experience of the school situation. The five factors identified were: (1) Participation in school; (2) Interesting and meaningful work; (3) Feeling well at school; (4) Structured school lessons; and (5) Praise for achievements. For each factor, an index was created that was dichotomised (poor versus good circumstance) using the median-split and dummy coded with ‘good circumstance’ as reference. A description of the items included in each factor is available as Additional file 1 . Socio-economic status (SES) was assessed with three questions about the education level of the student’s mother and father (dichotomized as university degree versus not), and the amount of spending money the student typically received for entertainment each month (> SEK 1000 [approximately $120] versus less). Higher parental education and more spending money were used as reference categories. School grades in Swedish, English, and mathematics were measured separately on a 7-point scale and dichotomized as high (grades A, B, and C) versus low (grades D, E, and F). High school grades were used as the reference category.

Statistical analyses

The prevalence of mental health problems and bullying at school are presented using descriptive statistics, stratified by survey year (2014, 2018, 2020), gender, and school year (9 versus 11). As noted, we reduced the 18-item questionnaire assessing school function down to five essential factors by conducting a principal axis factor analysis (see Additional file 1 ). We then calculated the association between bullying at school (defined above) and mental health problems using multivariable logistic regression. Results are presented as odds ratios (OR) with 95% confidence intervals (Cis). To assess the contribution of SES and school-related factors to this association, three models are presented: Crude, Model 1 adjusted for demographic factors: age, gender, and assessment year; Model 2 adjusted for Model 1 plus SES (parental education and student spending money), and Model 3 adjusted for Model 2 plus school-related factors (school grades and the five factors identified in the principal factor analysis). These covariates were entered into the regression models in three blocks, where the final model represents the fully adjusted analyses. In all models, the category ‘not bullied at school’ was used as the reference. Pseudo R-square was calculated to estimate what proportion of the variance in mental health problems was explained by each model. Unlike the R-square statistic derived from linear regression, the Pseudo R-square statistic derived from logistic regression gives an indicator of the explained variance, as opposed to an exact estimate, and is considered informative in identifying the relative contribution of each model to the outcome [ 20 ]. All analyses were performed using SPSS v. 26.0.

Prevalence of bullying at school and mental health problems

Estimates of the prevalence of bullying at school and mental health problems across the 12 strata of data (3 years × 2 school grades × 2 genders) are shown in Table 1 . The prevalence of bullying at school increased minimally (< 1%) between 2014 and 2020, except among girls in grade 11 (2.5% increase). Mental health problems increased between 2014 and 2020 (range = 1.2% [boys in year 11] to 4.6% [girls in year 11]); were three to four times more prevalent among girls (range = 11.6% to 17.2%) compared to boys (range = 2.6% to 4.9%); and were more prevalent among older adolescents compared to younger adolescents (range = 1% to 3.1% higher). Pooling all data, reports of mental health problems were four times more prevalent among boys who had been victims of bullying compared to those who reported no experiences with bullying. The corresponding figure for girls was two and a half times as prevalent.

Associations between bullying at school and mental health problems

Table 2 shows the association between bullying at school and mental health problems after adjustment for relevant covariates. Demographic factors, including female gender (OR = 3.87; CI 3.48–4.29), older age (OR = 1.38, CI 1.26–1.50), and more recent assessment year (OR = 1.18, CI 1.13–1.25) were associated with higher odds of mental health problems. In Model 2, none of the included SES variables (parental education and student spending money) were associated with mental health problems. In Model 3 (fully adjusted), the following school-related factors were associated with higher odds of mental health problems: lower grades in Swedish (OR = 1.42, CI 1.22–1.67); uninteresting or meaningless schoolwork (OR = 2.44, CI 2.13–2.78); feeling unwell at school (OR = 1.64, CI 1.34–1.85); unstructured school lessons (OR = 1.31, CI = 1.16–1.47); and no praise for achievements (OR = 1.19, CI 1.06–1.34). After adjustment for all covariates, being bullied at school remained associated with higher odds of mental health problems (OR = 2.57; CI 2.24–2.96). Demographic and school-related factors explained 12% and 6% of the variance in mental health problems, respectively (Pseudo R-Square). The inclusion of socioeconomic factors did not alter the variance explained.

Our findings indicate that mental health problems increased among Swedish adolescents between 2014 and 2020, while the prevalence of bullying at school remained stable (< 1% increase), except among girls in year 11, where the prevalence increased by 2.5%. As previously reported [ 5 , 6 ], mental health problems were more common among girls and older adolescents. These findings align with previous studies showing that adolescents who are bullied at school are more likely to experience mental health problems compared to those who are not bullied [ 3 , 4 , 9 ]. This detrimental relationship was observed after adjustment for school-related factors shown to be associated with adolescent mental health [ 10 ].

A novel finding was that boys who had been bullied at school reported a four-times higher prevalence of mental health problems compared to non-bullied boys. The corresponding figure for girls was 2.5 times higher for those who were bullied compared to non-bullied girls, which could indicate that boys are more vulnerable to the deleterious effects of bullying than girls. Alternatively, it may indicate that boys are (on average) bullied more frequently or more intensely than girls, leading to worse mental health. Social support could also play a role; adolescent girls often have stronger social networks than boys and could be more inclined to voice concerns about bullying to significant others, who in turn may offer supports which are protective [ 21 ]. Related studies partly confirm this speculative explanation. An Estonian study involving 2048 children and adolescents aged 10–16 years found that, compared to girls, boys who had been bullied were more likely to report severe distress, measured by poor mental health and feelings of hopelessness [ 22 ].

Other studies suggest that heritable traits, such as the tendency to internalize problems and having low self-esteem are associated with being a bully-victim [ 23 ]. Genetics are understood to explain a large proportion of bullying-related behaviors among adolescents. A study from the Netherlands involving 8215 primary school children found that genetics explained approximately 65% of the risk of being a bully-victim [ 24 ]. This proportion was similar for boys and girls. Higher than average body mass index (BMI) is another recognized risk factor [ 25 ]. A recent Australian trial involving 13 schools and 1087 students (mean age = 13 years) targeted adolescents with high-risk personality traits (hopelessness, anxiety sensitivity, impulsivity, sensation seeking) to reduce bullying at school; both as victims and perpetrators [ 26 ]. There was no significant intervention effect for bullying victimization or perpetration in the total sample. In a secondary analysis, compared to the control schools, intervention school students showed greater reductions in victimization, suicidal ideation, and emotional symptoms. These findings potentially support targeting high-risk personality traits in bullying prevention [ 26 ].

The relative stability of bullying at school between 2014 and 2020 suggests that other factors may better explain the increase in mental health problems seen here. Many factors could be contributing to these changes, including the increasingly competitive labour market, higher demands for education, and the rapid expansion of social media [ 19 , 27 , 28 ]. A recent Swedish study involving 29,199 students aged between 11 and 16 years found that the effects of school stress on psychosomatic symptoms have become stronger over time (1993–2017) and have increased more among girls than among boys [ 10 ]. Research is needed examining possible gender differences in perceived school stress and how these differences moderate associations between bullying and mental health.

Strengths and limitations

Strengths of the current study include the large participant sample from diverse schools; public and private, theoretical and practical orientations. The survey included items measuring diverse aspects of the school environment; factors previously linked to adolescent mental health but rarely included as covariates in studies of bullying and mental health. Some limitations are also acknowledged. These data are cross-sectional which means that the direction of the associations cannot be determined. Moreover, all the variables measured were self-reported. Previous studies indicate that students tend to under-report bullying and mental health problems [ 29 ]; thus, our results may underestimate the prevalence of these behaviors.

In conclusion, consistent with our stated hypotheses, we observed an increase in self-reported mental health problems among Swedish adolescents, and a detrimental association between bullying at school and mental health problems. Although bullying at school does not appear to be the primary explanation for these changes, bullying was detrimentally associated with mental health after adjustment for relevant demographic, socio-economic, and school-related factors, confirming our third hypothesis. The finding that boys are potentially more vulnerable than girls to the deleterious effects of bullying should be replicated in future studies, and the mechanisms investigated. Future studies should examine the longitudinal association between bullying and mental health, including which factors mediate/moderate this relationship. Epigenetic studies are also required to better understand the complex interaction between environmental and biological risk factors for adolescent mental health [ 24 ].

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Olweus D. School bullying: development and some important challenges. Ann Rev Clin Psychol. 2013;9(9):751–80. https://doi.org/10.1146/annurev-clinpsy-050212-185516 .

Article   Google Scholar  

Arseneault L, Bowes L, Shakoor S. Bullying victimization in youths and mental health problems: “Much ado about nothing”? Psychol Med. 2010;40(5):717–29. https://doi.org/10.1017/S0033291709991383 .

Article   CAS   PubMed   Google Scholar  

Arseneault L. The long-term impact of bullying victimization on mental health. World Psychiatry. 2017;16(1):27–8. https://doi.org/10.1002/wps.20399 .

Article   PubMed   PubMed Central   Google Scholar  

Moore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood and adolescence: a systematic review and meta-analysis. World J Psychiatry. 2017;7(1):60–76. https://doi.org/10.5498/wjp.v7.i1.60 .

Hagquist C, Due P, Torsheim T, Valimaa R. Cross-country comparisons of trends in adolescent psychosomatic symptoms—a Rasch analysis of HBSC data from four Nordic countries. Health Qual Life Outcomes. 2019;17(1):27. https://doi.org/10.1186/s12955-019-1097-x .

Deighton J, Lereya ST, Casey P, Patalay P, Humphrey N, Wolpert M. Prevalence of mental health problems in schools: poverty and other risk factors among 28 000 adolescents in England. Br J Psychiatry. 2019;215(3):565–7. https://doi.org/10.1192/bjp.2019.19 .

Article   PubMed Central   Google Scholar  

Le HTH, Tran N, Campbell MA, Gatton ML, Nguyen HT, Dunne MP. Mental health problems both precede and follow bullying among adolescents and the effects differ by gender: a cross-lagged panel analysis of school-based longitudinal data in Vietnam. Int J Ment Health Syst. 2019. https://doi.org/10.1186/s13033-019-0291-x .

Bayer JK, Mundy L, Stokes I, Hearps S, Allen N, Patton G. Bullying, mental health and friendship in Australian primary school children. Child Adolesc Ment Health. 2018;23(4):334–40. https://doi.org/10.1111/camh.12261 .

Article   PubMed   Google Scholar  

Hysing M, Askeland KG, La Greca AM, Solberg ME, Breivik K, Sivertsen B. Bullying involvement in adolescence: implications for sleep, mental health, and academic outcomes. J Interpers Violence. 2019. https://doi.org/10.1177/0886260519853409 .

Hogberg B, Strandh M, Hagquist C. Gender and secular trends in adolescent mental health over 24 years—the role of school-related stress. Soc Sci Med. 2020. https://doi.org/10.1016/j.socscimed.2020.112890 .

Kidger J, Araya R, Donovan J, Gunnell D. The effect of the school environment on the emotional health of adolescents: a systematic review. Pediatrics. 2012;129(5):925–49. https://doi.org/10.1542/peds.2011-2248 .

Saminathen MG, Låftman SB, Modin B. En fungerande skola för alla: skolmiljön som skyddsfaktor för ungas psykiska välbefinnande. [A functioning school for all: the school environment as a protective factor for young people’s mental well-being]. Socialmedicinsk tidskrift [Soc Med]. 2020;97(5–6):804–16.

Google Scholar  

Bibou-Nakou I, Tsiantis J, Assimopoulos H, Chatzilambou P, Giannakopoulou D. School factors related to bullying: a qualitative study of early adolescent students. Soc Psychol Educ. 2012;15(2):125–45. https://doi.org/10.1007/s11218-012-9179-1 .

Vukojevic M, Zovko A, Talic I, Tanovic M, Resic B, Vrdoljak I, Splavski B. Parental socioeconomic status as a predictor of physical and mental health outcomes in children—literature review. Acta Clin Croat. 2017;56(4):742–8. https://doi.org/10.20471/acc.2017.56.04.23 .

Reiss F. Socioeconomic inequalities and mental health problems in children and adolescents: a systematic review. Soc Sci Med. 2013;90:24–31. https://doi.org/10.1016/j.socscimed.2013.04.026 .

Stockholm City. Stockholmsenkät (The Stockholm Student Survey). 2021. https://start.stockholm/aktuellt/nyheter/2020/09/presstraff-stockholmsenkaten-2020/ . Accessed 19 Nov 2021.

Zeebari Z, Lundin A, Dickman PW, Hallgren M. Are changes in alcohol consumption among swedish youth really occurring “in concert”? A new perspective using quantile regression. Alc Alcohol. 2017;52(4):487–95. https://doi.org/10.1093/alcalc/agx020 .

Hagquist C. Psychometric properties of the PsychoSomatic Problems Scale: a Rasch analysis on adolescent data. Social Indicat Res. 2008;86(3):511–23. https://doi.org/10.1007/s11205-007-9186-3 .

Hagquist C. Ungas psykiska hälsa i Sverige–komplexa trender och stora kunskapsluckor [Young people’s mental health in Sweden—complex trends and large knowledge gaps]. Socialmedicinsk tidskrift [Soc Med]. 2013;90(5):671–83.

Wu W, West SG. Detecting misspecification in mean structures for growth curve models: performance of pseudo R(2)s and concordance correlation coefficients. Struct Equ Model. 2013;20(3):455–78. https://doi.org/10.1080/10705511.2013.797829 .

Holt MK, Espelage DL. Perceived social support among bullies, victims, and bully-victims. J Youth Adolscence. 2007;36(8):984–94. https://doi.org/10.1007/s10964-006-9153-3 .

Mark L, Varnik A, Sisask M. Who suffers most from being involved in bullying-bully, victim, or bully-victim? J Sch Health. 2019;89(2):136–44. https://doi.org/10.1111/josh.12720 .

Tsaousis I. The relationship of self-esteem to bullying perpetration and peer victimization among schoolchildren and adolescents: a meta-analytic review. Aggress Violent Behav. 2016;31:186–99. https://doi.org/10.1016/j.avb.2016.09.005 .

Veldkamp SAM, Boomsma DI, de Zeeuw EL, van Beijsterveldt CEM, Bartels M, Dolan CV, van Bergen E. Genetic and environmental influences on different forms of bullying perpetration, bullying victimization, and their co-occurrence. Behav Genet. 2019;49(5):432–43. https://doi.org/10.1007/s10519-019-09968-5 .

Janssen I, Craig WM, Boyce WF, Pickett W. Associations between overweight and obesity with bullying behaviors in school-aged children. Pediatrics. 2004;113(5):1187–94. https://doi.org/10.1542/peds.113.5.1187 .

Kelly EV, Newton NC, Stapinski LA, Conrod PJ, Barrett EL, Champion KE, Teesson M. A novel approach to tackling bullying in schools: personality-targeted intervention for adolescent victims and bullies in Australia. J Am Acad Child Adolesc Psychiatry. 2020;59(4):508. https://doi.org/10.1016/j.jaac.2019.04.010 .

Gunnell D, Kidger J, Elvidge H. Adolescent mental health in crisis. BMJ. 2018. https://doi.org/10.1136/bmj.k2608 .

O’Reilly M, Dogra N, Whiteman N, Hughes J, Eruyar S, Reilly P. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin Child Psychol Psychiatry. 2018;23:601–13.

Unnever JD, Cornell DG. Middle school victims of bullying: who reports being bullied? Aggr Behav. 2004;30(5):373–88. https://doi.org/10.1002/ab.20030 .

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Acknowledgements

Authors are grateful to the Department for Social Affairs, Stockholm, for permission to use data from the Stockholm School Survey.

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HK conceived the study and analyzed the data (with input from MH). HK and MH interpreted the data and jointly wrote the manuscript. All authors read and approved the final manuscript.

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Principal factor analysis description.

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Källmén, H., Hallgren, M. Bullying at school and mental health problems among adolescents: a repeated cross-sectional study. Child Adolesc Psychiatry Ment Health 15 , 74 (2021). https://doi.org/10.1186/s13034-021-00425-y

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The nature of cyberbullying, the impact of cyberbullying on emotional health and well-being, technological solutions, asking adults for help, cyberbullying and its impact on young people's emotional health and well-being.

Published online by Cambridge University Press:  02 January 2018

The upsurge of cyberbullying is a frequent cause of emotional disturbance in children and young people. The situation is complicated by the fact that these interpersonal safety issues are actually generated by the peer group and in contexts that are difficult for adults to control. This article examines the effectiveness of common responses to cyberbullying.

Whatever the value of technological tools for tackling cyberbullying, we cannot avoid the fact that this is an interpersonal problem grounded in a social context.

Practitioners should build on existing knowledge about preventing and reducing face-to-face bullying while taking account of the distinctive nature of cyberbullying. Furthermore, it is essential to take account of the values that young people are learning in society and at school.

Traditional face-to-face bullying has long been identified as a risk factor for the social and emotional adjustment of perpetrators, targets and bully victims during childhood and adolescence; Reference Almeida, Caurcel and Machado 1 - Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 bystanders are also known to be negatively affected. Reference Ahmed, Österman and Björkqvist 7 - Reference Salmivalli 9 The emergence of cyberbullying indicates that perpetrators have turned their attention to technology (including mobile telephones and the internet) as a powerful means of exerting their power and control over others. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 Cyberbullies have the power to reach their targets at any time of the day or night.

Cyberbullying takes a number of forms, to include:

• flaming: electronic transmission of angry or rude messages;

• harassment: repeatedly sending insulting or threatening messages;

• cyberstalking: threats of harm or intimidation;

• denigration: put-downs, spreading cruel rumours;

• masquerading: pretending to be someone else and sharing information to damage a person’s reputation;

• outing: revealing personal information about a person which was shared in confidence;

• exclusion: maliciously leaving a person out of a group online, such as a chat line or a game, ganging up on one individual. Reference Schenk and Fremouw 11

Cyberbullying often occurs in the context of relationship difficulties, such as the break-up of a friendship or romance, envy of a peer’s success, or in the context of prejudiced intolerance of particular groups on the grounds of gender, ethnicity, sexual orientation or disability. Reference Hoff and Mitchell 12

A survey of 23 420 children and young people across Europe found that, although the vast majority were never cyberbullied, 5% were being cyberbullied more than once a week, 4% once or twice a month and 10% less often. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 Many studies indicate a significant overlap between traditional bullying and cyberbullying. Reference Perren, Dooley, Shaw and Cross 5 , Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Kowalski and Limber 14 , Reference Ybarra and Mitchell 15 However, a note of caution is needed when interpreting the frequency and prevalence of cyberbullying. As yet, there is no uniform agreement on its definition and researchers differ in the ways they gather their data, with some, for example, asking participants whether they have ‘ever’ been cyberbullied and others being more specific, for example, ‘in the past 30 days’.

Research consistently identifies the consequences of bullying for the emotional health of children and young people. Victims experience lack of acceptance in their peer groups, which results in loneliness and social isolation. The young person’s consequent social withdrawal is likely to lead to low self-esteem and depression. Bullies too are at risk. They are more likely than non-bullies to engage in a range of maladaptive and antisocial behaviours, and they are at risk of alcohol and drugs dependency; like victims, they have an increased risk of depression and suicidal ideation. Studies among children Reference Escobar, Fernandez-Baen, Miranda, Trianes and Cowie 2 - Reference Kaltiala-Heino, Rimpalä, Rantanen and Rimpalä 4 , Reference Kumpulainen, Rasanen and Henttonen 16 and adolescents Reference Salmivalli, Lappalainen and Lagerspetz 17 , Reference Sourander, Helstela, Helenius and Piha 18 indicate moderate to strong relationships between being nominated by peers as a bully or a victim at different time points, suggesting a process of continuity. The effects of being bullied at school can persist into young adulthood. Reference Isaacs, Hodges and Salmivalli 19 , Reference Lappalainen, Meriläinen, Puhakka and Sinkkonen 20

Studies demonstrate that most young people who are cyberbullied are already being bullied by traditional, face-to-face methods. Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Dooley, Pyzalski and Cross 21 - Reference Riebel, Jaeger and Fischer 23 Cyberbullying can extend into the target’s life at all times of the day and night and there is evidence for additional risks to the targets of cyberbullying, including damage to self-esteem, academic achievement and emotional well-being. For example, Schenk & Fremouw Reference Schenk and Fremouw 11 found that college student victims of cyberbullying scored higher than matched controls on measures of depression, anxiety, phobic anxiety and paranoia. Studies of school-age cyber victims indicate heightened risk of depression, Reference Perren, Dooley, Shaw and Cross 5 , Reference Gradinger, Strohmeier and Spiel 22 , Reference Juvonen and Gross 24 of psychosomatic symptoms such as headaches, abdominal pain and sleeplessness Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 and of behavioural difficulties including alcohol consumption. Reference Mitchell, Ybarra and Finkelhor 25 As found in studies of face-to-face bullying, cyber victims report feeling unsafe and isolated, both at school and at home. Similarly, cyberbullies report a range of social and emotional difficulties, including feeling unsafe at school, perceptions of being unsupported by school staff and a high incidence of headaches. Like traditional bullies, they too are engaged in a range of other antisocial behaviours, conduct disorders, and alcohol and drug misuse. Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Hinduja and Patchin 26

The most fundamental way of dealing with cyberbullying is to attempt to prevent it in the first place, through whole-school e-safety policies Reference Campbell 27 - Reference Stacey 29 and through exposure to the wide range of informative websites that abound (e.g. UK Council for Child Internet Safety (UKCCIS; www.education.gov.uk/ukccis ), ChildLine ( www.childline.org.uk )). Many schools now train pupils in e-safety and ‘netiquette’ to equip them with the critical tools that they will need to understand the complexity of the digital world and become aware of its risks as well as its benefits. Techniques include blocking bullying behaviour online or creating panic buttons for cyber victims to use when under threat. Price & Dalgleish Reference Price and Dalgleish 30 found that blocking was considered as a most helpful online action by cyber victims and a number of other studies have additionally found that deleting nasty messages and stopping use of the internet were effective strategies. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 , Reference Kowalski and Limber 14 , Reference Juvonen and Gross 24 However, recent research by Kumazaki et al Reference Kumazaki, Kanae, Katsura, Akira and Megumi 31 found that training young people in netiquette did not significantly reduce or prevent cyberbullying. Clearly there is a need for further research to evaluate the effectiveness of different types of technological intervention.

Parents play an important role in prevention by banning websites and setting age-appropriate limits of using the computer and internet. Reference Kowalski and Limber 14 Poor parental monitoring is consistently associated with a higher risk for young people to be involved in both traditional and cyberbullying, whether as perpetrator or target. Reference Ybarra and Mitchell 15 However, adults may be less effective in dealing with cyberbullying once it has occurred. Most studies confirm that it is essential to tell someone about the cyberbullying rather than suffer in silence and many students report that they would ask their parents for help in dealing with a cyberbullying incident. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 , Reference Stacey 29 , Reference Aricak, Siyahhan, Uzunhasanoglu, Saribeyoglu, Ciplak and Yilmaz 32 On the other hand, some adolescents recommend not consulting adults because they fear loss of privileges (e.g. having and using mobile telephones and their own internet access), and because they fear that their parents would simply advise them to ignore the situation or that they would not be able to help them as they are not accustomed to cyberspace. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 , Reference Hoff and Mitchell 12 , Reference Kowalski and Limber 14 , Reference Stacey 29 In a web-based survey of 12- to 17-year-olds, of whom most had experienced at least one cyberbullying incident in the past year, Juvonen & Gross Reference Juvonen and Gross 24 found that 90% of the victims did not tell their parents about their experiences and 50% of them justified it with ‘I need to learn to deal with it myself’.

Students also have a rather negative and critical attitude to teachers’ support and a large percentage consider telling a teacher or the school principal as rather ineffective. Reference Aricak, Siyahhan, Uzunhasanoglu, Saribeyoglu, Ciplak and Yilmaz 32 , Reference DiBasilio 33 Although 17% of students reported to a teacher after a cyberbullying incident, in 70% of the cases the school did not react to it. Reference Hoff and Mitchell 12

Involving peers

Young people are more likely to find it helpful to confide in peers. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 , Reference Price and Dalgleish 30 , Reference DiBasilio 33 Additionally, it is essential to take account of the bystanders who usually play a critical role as audience to the cyberbullying in a range of participant roles, and who have the potential to be mobilised to take action against cyberbullying. Reference Salmivalli 9 , Reference Cowie 34 For example, a system of young cyber mentors, trained to monitor websites and offer emotional support to cyber victims, was positively evaluated by adolescents. Reference Banerjee, Robinson and Smalley 35 Similarly, DiBasilio Reference DiBasilio 33 showed that peer leaders in school played a part in prevention of cyberbullying by creating bullying awareness in the school, developing leadership skills among students, establishing bullying intervention practices and team-building initiatives in the student community, and encouraging students to behave proactively as bystanders. This intervention successfully led to a decline in cyberbullying, in that the number of students who participated in electronic bullying decreased, while students’ understanding of bullying widened.

Although recommended strategies for coping with cyberbullying abound, there remains a lack of evidence about what works best and in what circumstances in counteracting its negative effects. However, it would appear that if we are to solve the problem of cyberbullying, we must also understand the networks and social groups where this type of abuse occurs, including the importance that digital worlds play in the emotional lives of young people today, and the disturbing fact that cyber victims can be targeted at any time and wherever they are, so increasing their vulnerability.

There are some implications for professionals working with children and young people. Punitive methods tend on the whole not to be effective in reducing cyberbullying. In fact, as Shariff & Strong-Wilson Reference Shariff, Strong-Wilson and Kincheloe 36 found, zero-tolerance approaches are more likely to criminalise young people and add a burden to the criminal justice system. Interventions that work with peer-group relationships and with young people’s value systems have a greater likelihood of success. Professionals also need to focus on the values that are held within their organisations, in particular with regard to tolerance, acceptance and compassion for those in distress. The ethos of the schools where children and young people spend so much of their time is critical. Engagement with school is strongly linked to the development of positive relationships with adults and peers in an environment where care, respect and support are valued and where there is an emphasis on community. As Batson et al Reference Batson, Ahmad, Lishner, Tsang, Snyder and Lopez 37 argue, empathy-based socialisation practices encourage perspective-taking and enhance prosocial behaviour, leading to more satisfying relationships and greater tolerance of stigmatised outsider groups. This is particularly relevant to the discussion since researchers have consistently found that high-quality friendship is a protective factor against mental health difficulties among bullied children. Reference Skrzypiec, Slee, Askell-Williams and Lawson 38

Finally, research indicates the importance of tackling bullying early before it escalates into something much more serious. This affirms the need for schools to establish a whole-school approach with a range of systems and interventions in place for dealing with all forms of bullying and social exclusion. External controls have their place, but we also need to remember the interpersonal nature of cyberbullying. This suggests that action against cyberbullying should be part of a much wider concern within schools about the creation of an environment where relationships are valued and where conflicts are seen to be resolved in the spirit of justice and fairness.

Acknowledgement

I am grateful to the COST ACTION IS0801 for its support in preparing this article ( https://sites.google.com/site/costis0801 ).

Declaration of interest

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• Volume 37, Issue 5
• Helen Cowie (a1)
• DOI: https://doi.org/10.1192/pb.bp.112.040840

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• Published: 14 December 2023

Online and school bullying roles: are bully-victims more vulnerable in nonsuicidal self-injury and in psychological symptoms than bullies and victims?

• Boglárka Drubina   ORCID: orcid.org/0000-0002-3930-4293 1 , 2 ,
• Gyöngyi Kökönyei   ORCID: orcid.org/0000-0001-6750-2644 2 , 3 , 4 ,
• Dóra Várnai 2 , 5 &
• Melinda Reinhardt   ORCID: orcid.org/0000-0001-7010-5623 2 , 6  

BMC Psychiatry volume  23 , Article number:  945 ( 2023 ) Cite this article

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Bullying leads to adverse mental health outcomes and it has also been linked to nonsuicidal self-injury (NSSI) in community adolescents. It is not clear whether different roles of bullying (bully, victim, bully-victim) are associated with NSSI, furthermore the same associations in cyberbullying are even less investigated.

The aim of the current study was to test whether students involved in school or online bullying differed from their not involved peers and from each other in psychological symptoms (externalizing and internalizing problems) and in NSSI severity (number of episodes, number of methods). Furthermore, mediation models were tested to explore the possible role of externalizing and internalizing problems in the association of school and online bullying roles with NSSI. In our study, 1011 high school students (66.07% girls; n = 668), aged between 14 and 20 years (M age = 16.81; SD = 1.41) participated.

Lifetime prevalence of at least one episode of NSSI was 41.05% (n = 415). Students involved in bullying used more methods of NSSI than not involved adolescents. In general, victim status was associated mostly with internalizing symptoms, while bully role was more strongly associated with externalizing problems. Bully-victims status was associated with both types of psychological problems, but this group did not show a significantly elevated NSSI severity compared to other bullying roles. Externalizing and internalizing problems mediated the relationship between bullying roles and NSSI with different paths at different roles, especially in case of current NSSI that happened in the previous month.

Conclusions

Results highlight that students involved in bullying are more vulnerable to NSSI and to psychological symptoms compared to their peers who are not involved in bullying. It is suggested that bullying roles, especially bully-victim status, need to be identified in school and online settings and thus special attention should be addressed to them to reduce psychological symptoms and NSSI, for example by enhancing adaptive coping skills.

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Introduction

As nonsuicidal self-injury (NSSI) – the intentional, direct destruction (e.g., scratching, bruising, cutting, burning, biting) of one’s own body tissue without suicidal intent [ 1 ] – is a considerable behavioral problem, especially among adolescents [ 2 , 3 ], and has become an even more widespread phenomenon throughout the last decade [ 4 , 5 ], research should focus more on this topic. Some evidence indicates that NSSI can be a stronger predictor of suicide attempts than previous suicidal behavior [ 6 , 7 ]. NSSI typically occurs in early adolescence (between the age of 12–14) [ 5 , 8 ] and peaks in mid-adolescence [ 9 , 10 ]. The prevalence can be remarkably high in community youth samples (between 14.5 and 46.5%) [ 11 , 12 , 13 ], and based on meta-analytical results, females are more at risk for NSSI [ 14 ].

The current study focuses on three phenomena typically occuring during adolescence and thus may also be the cause of difficulties in school settings: along with NSSI , we also focus on bullying , and internalizing and externalizing problems. While previous research mainly focuses on bullying victimization and victims, by differentiating the roles of bullying (bully, victim, bully-victim), the aim of this study is to investigate whether bullies and bully-victims are also vulnerable to mental health issues (NSSI, internalizing and externalizing problems). Furthermore, another intention of this study is to establish and test mediation models that can be taken into consideration when planning NSSI and mental health related interventions in school settings. Our research includes cyberbullying as well, which is less investigated as school-based bullying but more and more a widely spread type of peer aggression.

Bullying and bullying roles

In the current study, bullying is defined as a type of youth violence which includes any unwanted aggressive behavior by another youth or group of youths who are not siblings or current dating partners [ 15 , 16 ]. Bullying can take place in the school or in any online platforms, the latter is cyberbullying [ 17 , 18 ]. In our study, a traditional classification of bullying roles was applied: perpetrator, victim, and bully-victim [ 19 , 20 ]. Bully/perpetrator is the person who commits the bullying and have a perceived dominance or more power than the victim. Victim is a person who suffers from being bullied and perceived as less dominant or having less power. B ully-victim s are those who are both victims and perpetrators [ 19 , 20 , 21 ].

Regarding bullying roles, gender differences can be observed: boys are more likely to engage in bullying others in school and in online settings [ 22 , 23 ], while girls are more likely to be the victims of online bullying [ 24 ]. Based on the data of the HBSC (Health Behavior in School-aged Children) study obtained in 2017/18, the prevalence of bullying perpetration and victimization shows a great variety across the 45 participating countries (the prevalence of perpetration varied from 0.3 to 30% in the 11–15-year-old students, and victimization rates ranged from 0.5 to 32%, respectively) [ 25 ]. However, in many countries there is a decline in bullying perpetration rates. Similar to offline bullying, a significant cross-national variety of prevalence regarding cyberbullying is observable (from 0.6 to 31% in cyberbullying others and from 3 to 29% in being cyberbullied) [ 25 ]. Regarding Hungary, according to the latest HBSC data collection, 28.4% of the 11–17-year-old students reported to have been bullied at least once or twice and 27.1% reported that they have bullied someone in school at least once or twice in the recent three months [ 26 ]. The rates of cyberbullying were lower: 17.8% of students have been cyberbullied and 12.7% of students bullied others online [ 26 ].

The link between bullying roles and externalizing and internalizing problems

Based on different roles in traditional school-based bullying, previous research differentiates connected mental health problems in adolescents. Most of the studies report that bullying perpetrators are more likely to face externalizing problems, while victims are more likely to face internalizing problems [ 23 , 27 , 28 ]. However, the perpetrator-externalizing and victim-internalizing associations can be oversimplifying based on Cook and his colleagues [ 29 ] findings who included online bullying as well. In their meta-analytical results, internalizing problems can be associated with the bully role as well (effect size = 0.12), but the association is stronger for the victims (effect size = 0.25). And similarly, externalizing problems are significantly associated with the victim role (effect size = 0.12), but the association is stronger for bullies, making externalizing problem behaviors the strongest individual predictor of being a bully (effect size = 0.34). Bully-victim role was associated with both externalizing (effect size = 0.33) and internalizing (effect size = 0.22) problems [ 29 ]. Although, previous studies have demonstrated that being a bully-victim in traditional school-based bullying [ 22 , 30 , 31 , 32 ] or in cyberbullying [ 33 , 34 ] might be associated with worse mental health outcomes than either bullies or victims, only a few studies investigated the characteristics and mental health problems of this vulnerable group [ 25 ].

The link between bullying and NSSI

Several risk factors of NSSI have been suggested in previous research (e.g., emotion regulation problems, impulsivity, depressive symptoms), that are mainly individual characteristics [ 35 ]. Less interest has been given to school and peer factors, although the climate of peer relationships or related negative life events can also play an important role in the development of NSSI, furthermore, as it has been recently suggested, academic-related stress and peer bullying is associated with NSSI behaviors [ 36 ], therefore investigating bullying in association with NSSI is essential.

Many cross-sectional studies suggest that adolescents who reported being victims of bullying were at an increased risk for NSSI compared to adolescents who were not victims of bullying or who reported low levels of victimization [ 37 , 38 , 39 ]. Based on two recent studies, involvement in bullying increases the likelihood to engage in NSSI [ 40 , 41 ]. Meta-analytical findings also suggest that bullying is associated with NSSI [ 14 , 42 ]. However, not many studies focused on perpetrators or bully-victims, the associations with NSSI of these roles or the trajectories through which perpetrators or bully-victims are linked to NSSI. Some results show that bullies also engage in NSSI [ 43 ], especially when they had a history of being bullied (which could have made them bully-victims) [ 22 , 44 ]. Additionally, bully-victim girls scored the highest in NSSI [ 44 ] compared to any other role of bullying, and NSSI has also been linked to cyberbullying [ 45 ]. Only a very limited number of research has focused on the association between NSSI and cyberbullying, and these studies mainly investigated cyberbullying victimization. Recent research however shows a higher frequency of NSSI among students involved in cyberbullying and shows that cyberbullying can be in direct association with NSSI [ 12 , 46 , 47 ]. Although online and school-based bullying share common features (e.g., bullying roles, association with mental health problems) remarkable differences occur as well (e.g., higher anonymity, fewer intervention opportunities for affected students, loneliness, role of internet safety features) [ 18 ]. Being alone in online settings might facilitate the appearance of NSSI as feeling lonely is associated with NSSI [ 48 , 49 ] and NSSI happens most often when adolescents are alone [ 50 ]. Furthermore, compared to school-based bullying, online bullying might be more difficult to deal with for the environment (e.g., parents, educators) [ 51 ], making it more difficult for the child to cope with it in the lack of adequate help from significant ones which can also result in a maladaptive coping strategy (e.g., NSSI). A longitudinal study found that cyberbullying can cause harm above and beyond traditional bullying [ 52 ]. Therefore, regarding the remarkable differences between cyberbullying and school-based bullying, it is essential to be able to compare whether bullying in different settings have the same association to NSSI or not. In the current study, online and school-based bullying roles are tested with different mediation models.

Although, most of the previously mentioned findings are cross sectional, some longitudinal cohort and case control studies [ 14 , 44 ] suggest that bullying is not only associated to NSSI but may also predict it.

The link between externalizing and internalizing problems and NSSI

NSSI is often considered as a transdiagnostic element in psychopathology, therefore NSSI-related variables (e.g., suicidality or impulse control difficulties) are best predicted by transdiagnostic variables [ 53 ]. NSSI episodes are prevalent in different psychiatric disorders and psychological symptoms during adolescence (e.g., depression, psychotic symptoms, substance abuse, borderline personality-disorder features, conduct problems, emotional problems) [ 54 , 55 ] and has been linked to externalizing problems (e.g., attention deficit hyperactivity disorder, conduct disorder, oppositional defiant disorder) [ 56 ], and to internalizing problems as well in adolescence [ 57 ]. Some findings suggest that externalizing and internalizing psychopathology are not only associated, but longitudinally predict NSSI [ 58 ].

Externalizing and internalizing problems as possible mediators

Bullying victimization and perpetration can result in interpersonal difficulties or negative interpersonal life events that can cause stress, negative emotions, and mental health problems in adolescence [ 59 , 60 , 61 , 62 , 63 , 64 ]. Subsequently, mental health problems and interpersonal difficulties can trigger and might result in NSSI, thus, they occur comorbidly [ 65 , 66 ]. To cope with negative emotions, NSSI might appear as a dysfunctional emotion regulation [ 3 , 67 , 68 ].

As described in the vulnerability-stress model, suggested by Hankin and Abela [ 69 ], internalizing and externalizing problems are rooted in both individual factors (cognitive vulnerabilities) and in environmental factors (stressors, negative life events, adversities). Environmental factors can be adverse life events that might strengthen the possibility of the development of mental health problems, psychopathology and NSSI in adolescents [ 32 , 70 ]. Bullying might be a significant environmental factor as throughout adolescence the importance of peer relationships and their opinion on oneself can considerably increase the negative influence on the quality of mental health [ 71 , 72 ]. Few mediator models have been established to explain the relationship between bullying and NSSI. Researchers so far have found a partial mediation regarding negative emotions [ 45 ], depressive mood and depressive symptoms [ 22 , 37 ]. In the current study, the possible mediating effect of internalizing and externalizing problems on the relationship between bullying and NSSI among community adolescents was hypothesized and tested.

Aim of the study

The global aim of the current study is to investigate the relationship of school and online bullying, externalizing and internalizing problems, and NSSI.

By establishing different bullying roles, this study seeks answers to the question whether those students who are involved in bullying suffer from greater mental health problems compared to students who are not involved in bullying. The following hypotheses were established:

Adolescents involved in (school or online) bullying show significantly higher internalizing and externalizing scores compared to not involved peers. It is also hypothesized that bully-victims score significantly higher in internalizing and externalizing problems compared to any other peer group (bullies, victims, not involved adolescents).

Adolescents involved in (school or online) bullying show significantly more serious NSSI behavior (in terms of the number of NSSI episodes and the number of NSSI methods) compared to not involved peers. It is also hypothesized that bully-victims show significantly more serious NSSI behavior compared to any other peer group (bullies, victims, not involved adolescents).

Considering that externalizing and internalizing problems are both correlated to bullying [ 23 ] and NSSI [ 65 ], the current study establishes six mediation models (Figs.  1 , 2 , 3 , 4 , 5 and 6 ) to understand the relationship among different school and online bullying roles, externalizing and internalizing problems, and NSSI. Accordingly, the following hypotheses were examined:

The association of school and online bullying roles with NSSI will be partially mediated by both externalizing and internalizing symptoms (Figs.  1 , 2 , 3 and 4 ) given that both bullying perpetration and victimization might be related to internalizing and externalizing problems [ 29 ];

The association of the frequency of school victimization and NSSI will be partially mediated by externalizing and internalizing problems (Figs.  5 and 6 , respectively).

Gender and age are taken into consideration as control variables in the mediation models. As NSSI is predicted by various factors, it was hypothesized that externalizing and internalizing problems would only decrease the direct effect of bullying on NSSI rather than eliminate the association. The study also seeks answer to the question whether bully-victims are in need of intervention due to higher mental health problems (i.e., NSSI, externalizing and internalizing problems) compared to victims and bullies and not involved students.

Hypothesized mediation model 1

Hypothesized mediation model 2

Hypothesized mediation model 3

Hypothesized mediation model 4

Materials and methods

Participants and procedure.

This cross-sectional study involved 14 Hungarian secondary schools from the capital city and from the countryside. Specific schools were asked to participate based on accessibility to the researchers while following the idea to represent different type of secondary schools based on location (e.g., metropolitan area, smaller cities) and on educational profile (e.g., high-school, vocation schools). Data collection started in February 2019 and finished in January 2020. Participants were from all grades (grades 9–12) of secondary schools. During data collection, trained investigators were present, but not any teachers. Students filled out the questionnaires either in their classroom (paper-based questionnaires) or online (on the Qualtrics platform) in computer rooms or on smart phones according to the circumstances of the schools. Online questionnaires were filled out in the classroom, in-person settings as well (e.g., during informatics class) in the presence of trained investigators.

More than one thousand and two hundred students (N = 1232) were requested to take part in the study and a total of 1059 students agreed in participating. 173 students were either absent during data collection or declined to participate. From the 1059 who agreed in the participation, 48 were excluded due to incomplete answers. Thus, the final sample consists of 1011 students, mostly females (n = 668; 66.07% girls), the mean age came to 16.81 (SD = 1.41) years. The youngest participants were 14 years old; the oldest participants were 20 years old. Most of the participants live in cities (n = 450, 44.5%) or in the capital (n = 252; 24.9%), while 309 (30.6%) students live in villages.

All aspects of the study were ethically approved by the Institutional Review Board of ELTE Eötvös Loránd University, Budapest, Hungary. Participation was voluntary and anonymous. Students and one of their parents had to give their written consent to participate in the research, while headmasters of secondary schools were also informed about the details of the study and gave their consent to carry out the research in their institution. The Declaration of Helsinki [ 73 ] was taken into account while carrying out the research. An information sheet about the meaning, characteristics of NSSI and possible sources (online and in person) of help was provided to every participant.

Inventory of Statements About Self-injury

NSSI is often measured with the Inventory of Statements About Self-Injury (ISAS) [ 74 ]. In this study, Hungarian version of the short form was used [ 75 ] (Hungarian version: [ 76 ]). The short form of ISAS has two parts, the first assesses prevalence, types (12 different – plus one free answer – NSSI behaviors, e.g., cutting, biting, carving, severe scratching or hitting self) and characteristics of NSSI (e.g., age of onset, the experience of pain during NSSI, whether NSSI is performed alone or around others). The second part measures 13 functions of NSSI [ 74 ]. In the current study, only frequency, methods of NSSI and time of the last episode were analyzed from the first part of the ISAS. At the beginning of the questionnaire, definition of NSSI was given, underlying the importance of the act being deliberate without suicidal intent.

The Hungarian version of the Revised Olweus Bully/Victim Questionnaire [ 77 ] was used to measure bullying which was used in the Hungarian HBSC Study [ 26 ]. First, a precise definition was given to participants about the meaning of bullying: “We say a student is being bullied when another student, or several other students say mean and hurtful things to him or her; or make fun of him or her; or call him or her mean and hurtful names; or completely ignore or exclude him or her from their group of friends or leave him or her out of things on purpose. When we talk about bullying, these things happen repeatedly, and it is difficult for the student being bullied to defend himself or herself. We also call it bullying, when a student is teased repeatedly in a mean and hurtful way. But we don’t call it bullying when the teasing is done in a friendly and playful way. Also, it is not bullying when two students of about equal strength or power argue or fight.” In the first part, school bullying was measured with two questions: the first measures the frequency of bully perpetration during the past few months, while the second asks about the frequency of bully victimization during the same period of time. Online bullying was measured in the same way. The definition of online bullying was also given before the questions, containing examples as well (e.g., sending offensive messages to someone via SMS, chat programs or e-mail, posting such message on someone’s wall on social media).

Based on the previous questions, four different roles of school and online bullying were differentiated: school bullies were participants who at least once or twice have bullied someone else at school during the previous months, but they have not been bullied at all at school. Online bullies were participants who at least once or twice have bullied someone else online during the previous months, but they have not been bullied online. School victims were those participants who have been bullied at least once or twice during the previous months at school, but they have not bullied others at all in school settings. Online victims were those participants who have been bullied at least once or twice during the previous months on online platforms, but they have not bullied others at all online. School bully-victims are those participants who have bullied others at least once or twice at school in the previous months and who have been bullied as well at least once or twice at school during the previous months. Online bully-victims are those participants who have bullied others at least once or twice online in the previous months and who have been bullied as well at least once or twice online during the previous months. Not involved students have not bullied others and have not been bullied either, neither in school, nor online during the previous months.

In the second part of the questionnaire, seven different types of school bullying victimization (e.g., being excluded from activities or social groups, being ignored, being mocked) were measured. Items 6 (being mocked because of religion) and 7 (experiencing sexual comments) were developed by the Canadian HBSC group [ 78 ].

Participants could give their answers on a 5-point Likert-scale (1 =  never during the past few months , 2 =  once or twice , 3 =  two or three times a month , 4 =  approximately once a week , 5 =  several times a week ).

In the current study, reliability of the second part of the questionnaire (types of school victimization) was good (α = 0.70). Previous studies did not report reliability data concerning the second part of the Revised Olweus Bully/Victim Questionnaire [ 77 ].

Strengths and Difficulties Questionnaire (SDQ)

The self-report version of the Strengths and Difficulties Questionnaire [ 79 ] (Hungarian translation: [ 80 ]) is a brief emotional and behavioral screening questionnaire for children and young people and is a valid and reliable instrument to measure externalizing and internalizing symptoms in adolescence. Respondents use a 3-point scale to indicate how far each item applies to them (1 =  not true , 2 =  somewhat true , 3 =  completely true ). The 25 items are divided between 5 subscales, with five items: emotional symptoms (e.g., “ I am often unhappy, downhearted or tearful. ”), conduct problems (e.g., “ I fight a lot. I can make other people do what I want.” ), hyperactivity-inattention (e.g., “ I am constantly fidgeting or squirming.” ), peer problems (e.g., “ I am usually on my own. I generally play alone or keep to myself.”) , and prosocial behavior (e.g., “I often volunteer to help others (parents, teachers, children).” ). The authors of the questionnaire suggest the use of a three-subscale division of the SDQ [ 81 ] in low-risk or general population samples: internalizing problems (emotional symptoms + peer problems, 10 items), externalizing problems (conduct problems + hyperactivity symptoms, 10 items) and prosocial scale (5 items). In the current study, only internalizing and externalizing subscales were used. Reliability of the subscales were good in our study (internalizing subscale α = 0.75 was better than the original’s α = 0.66; externalizing subscale α = 0.76 was similar to the one in the original study α = 0.76) [ 81 ].

Data analysis

Basic characteristics of the sample, descriptive statistics of the variables and correlations were performed in IBM SPSS 28, the level of significance was taken as 0.05. Mediation analyses were performed in Mplus 8.0 [ 82 ].

NSSI was analyzed via two binary variables in the mediation models: one consists of no history of NSSI and past NSSI (at least one NSSI episode in the past, earlier than a month), while the other consists of no history of NSSI and current NSSI (at least one NSSI episode in the last month). In the ANOVA analysis, NSSI was a categorical variable with three values: no history of NSSI, past NSSI and current NSSI.

Two variables measured the severity of NSSI. Number of NSSI methods was a continuous variable, while number of NSSI episodes was a binary variable (non-repetitive NSSI = 1–9 episodes, repetitive NSSI = 10 or more episodes based on the suggestion of Gratz et al., [ 83 ]).

In the case of bullying, three different bullying variables were analyzed: (1) school bullying roles (1 = victim, 2 = bully, 3 = bully-victim, 4 = not involved in bullying) as a categorical variable; (2) online bullying roles (categories are the same as at school bullying); and (3) frequency of different school victimization types (higher score means more frequent school bullying victimization; used as a continuous variable).

Victim status was measured in two ways: in the first part of the questionnaire, the frequency of bullying victimization was asked in general (“ How often were you bullied during the past months?” ), while in the second part concrete items measure the frequency of different school bullying types.

As suggested in the literature [ 84 ], dummy variables were created for independent categorical variables (school and online bullying roles, see categories in the Measures part) to use in mediation modeling. Externalizing and internalizing problems were continuous variables, higher scores mean stronger internalizing and externalizing problems.

Pearson correlations were performed to measure associations between different variables. Group differences (NSSI, school bullying roles, online bullying roles) regarding externalizing and internalizing problems, and NSSI severity (number of NSSI methods) were assessed with one-way ANOVA. Crosstabulation was performed regarding the number of NSSI episodes (binary variable). In the case of NSSI severity (number of NSSI methods and number of NSSI episodes) normal distribution was violated, therefore the robust version of ANOVA (Welch test) was used. When Levene’s test claimed the violation of the homogeneity of variances, a robust post-hoc test (Games-Howell) was used, otherwise, the results of non-robust post-hoc test (Tukey) are reported. Post-hoc tests are used to compare group differences. To avoid type 1 error, p-values were adjusted during post-hoc analyses in the following way: when analyzing NSSI severity and internalizing and externalizing symptoms in different school and online bullying groups, 4 groups were compared with each other that resulted in 6 comparisons (not involved students vs. bully-victims; not involved students vs. bullies; not involved students vs. victims; bully-victims vs. bullies; bully-victims vs. victims; bullies vs. victims), therefore, p value of 0.05 was divided by 6 which results in p < .0083. Comparing three different NSSI groups in regards with internalizing and externalizing symptoms, three comparisons were made (no NSSI vs. past NSSI; no NSSI vs. current NSSI; past NSSI vs. current NSSI) therefore p value of 0.05 was divided by 3 which results in p < .016. In the Results, only results significant according to the adjusted level are reported. In the comparisons of groups, reference group was the not involved students in case of bullying roles. Regarding NSSI, reference was the no NSSI group (students who have never engaged in NSSI).

Six mediation models were established based on the literature and tested in the current study via Structural Equation Modeling. Mediator variables in every model were both externalizing and internalizing problems. Due to low correlation between the two mediator variables (r = .27), they were tested parallelly within the same models. Gender (1 = boys, 2 = girls) and age (continuous variable) were control variables in each mediation model.

In the models, observed variables were school bullying roles (Model 1, Fig.  1 ), online bullying roles (Model 2, Fig.  2 ) and frequency of school victimization (Model 3, Fig.  3 ). Outcome variables were No NSSI/Past NSSI and No NSSI/Current NSSI. Due to categorical variables, MLR (robust version of maximum likelihood parameter) estimator was used to perform Structural Equation Modeling [ 85 ]. Models are saturated as every possible connection is coded in the models.

Hypothesized mediation model 5

Hypothesized mediation model 6

Characteristics of NSSI

Almost one third (n = 320, 31.7%) of the current sample engaged in NSSI at least once within the last month. Prevalence of past NSSI was 9.4% (n = 95; participants who had engaged in NSSI some point in their life but did not engage within the last month). Among those who have ever engaged in NSSI, the most common methods were banging or hitting oneself (n = 222; 53.1%), interfering with wound healing (n = 218; 52.2%), cutting (n = 170; 40.7%), biting (n = 163; 39%), pinching (n = 162; 38.8%) and severe scratching (n = 144; 34.4%). Mean age of the first NSSI episode was 11.99 years (SD = 3.52). Significantly more girls (n = 293; 43.86%) engaged in NSSI than boys (n = 121; 35.27%; χ 2 (1) = 32.40; p < .001). The highest number of used NSSI methods was 11 from 13. Among those who have engaged in NSSI, 3.80 (SD = 2.69) methods were used in average. Mean score for number of NSSI episodes was 111.91 (SD = 935.96). Lifetime repetitive NSSI (≥ 10 episodes based on the suggestion of Gratz [ 83 ]) was 72.05% (n = 299) of those who have ever engaged in NSSI.

Descriptive statistics and correlation of the variables

Descriptive statistics and correlation of continuous variables, gender and age are shown in Table  1 . Female gender was associated to increased internalizing problems and to number of NSSI methods. Frequency of different school victimization types was slightly associated to lower age, while higher level of internalizing problems was associated to being older. School victimization was almost equally associated to internalizing and externalizing problems. Higher number of used NSSI methods was associated to a higher level of internalizing, externalizing and to more frequent school victimization.

Table  2 shows the crosstabulation of number of participants involved in different roles of school and online bullying. More girls were victims of school bullying (χ 2 (1) = 6.12; p < .01), while significantly more boys were bullies (χ 2 (1) = 20.92; p < .01) and bully-victims (χ 2 (1) = 4.15; p < .05), compared to girls. Similar gender differences emerged in cyberbullying with higher number of female victims (χ 2 (1) = 4.62; p < .05), higher number of male bullies (χ 2 (1) = 10.06; p < .01), and bully-victims (χ 2 (1) = 8.39; p < .01). Most frequent school victimization types were being excluded from activities, social groups or being ignored (n = 344; 33.9%); spreading rumors or fake news (n = 292; 28.8%); being calling names and teasing or made fun of (n = 249; 24.5%) and being the target of sexual comments (n = 120; 11.8%). Those who have ever been or currently are involved in NSSI reported higher frequency of school victimization (M = 9.44; SD = 3.21) compared to peers who are not involved in NSSI (M = 8.24; SD = 2.25; t = 6.53; p < .001). The number of participants who were bullied at school in any form at least once or twice (n = 568) is considerably higher compared to those who reported being bullied at school (n = 91) or online (n = 93) when asking simply how frequently the bullying happened (no concrete types of bullying were given).

Group differences in the severity of NSSI

Welch test revealed that different school-related bullying roles significantly differ in the number of NSSI methods (F (3;139.62) = 13.36; p < .001). Table  3 shows post hoc analysis and group differences. Those who have participated in school bullying in any form (bully, victim, bully-victim) use significantly more NSSI methods compared to their peers who have not participated in bullying at all. Similar results emerged in case of cyberbullying.

A crosstabulation in Table  4 shows the rate of repetitive NSSI and non-repetitive NSSI in different bullying roles.

Group differences in externalizing and internalizing problems

One-way ANOVA revealed that NSSI groups significantly differ in both the level of externalizing problems (F(2,1007) = 26.11; p < .001) and internalizing problems (F(2,1007) = 62.28; p < .001) as well. Table  5 contains post hoc comparisons of different groups. Regarding externalizing symptoms, the mean score of no-NSSI group was significantly lower than the mean score of past NSSI group and current NSSI group. Internalizing problems showed the same pattern.

School and online bullying roles differed significantly in the level of externalizing problems (school bullying: F(3,1001) = 15.93; p < .001; online bullying: F(3,1003) = 17.88; p < .001) and in the level of internalizing problems as well (school bullying: F(3,1001) = 30.38; p < .001; online bullying: (F(3,1003) = 18.65; p < .001). Post hoc test revealed that every school bullying role showed significantly higher level of externalizing problems than those who were not involved in school bullying, with the highest average score of bully-victims and bullies.School bullying roles differed significantly in the level of internalizing problems as well. Compared to those who were not involved in bullying, victims and bully-victims had significantly higher scores of internalizing problems, but bullies did not differ significantly. Victims and bully-victims scored significantly higher on internalizing symptoms compared to bullies, but there was no significant difference between victims and bully-victims. Online bullying roles differed significantly in the level of externalizing problems. Post hoc test revealed that online bullies and online bully-victims scored significantly higher on externalizing problems than those who were not involved in online bullying.

Online bullying roles differed significantly in the level of internalizing problems as well. Post hoc test revealed that online victims and online bully-victims scored significantly higher on internalizing problems than those who were not involved in online bullying; regarding bullies there was no difference compared to the reference group.

Mediation analysis

All standardized regression coefficients and standard errors of total, direct, and indirect effects related to each model are detailed in the Supplementary Materials Table S1 . While Table S2 in the Supplementary Materials contains odds ratios and confidence intervals.

In Model 1, explained variance of past NSSI was 14.8% (Table S1 , Table S2 , Fig.  7 ). Despite significant associations in the model, only two significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path 1.4. victim – internalizing problems – past NSSI (β = 0.052; SE = 0.02; p < .01); path 1.6. bully-victim – internalizing problems – past NSSI (β = 0.036; SE = 0.01; p < .01) (Table S1 , Table S2 ).

Final Model 1 of school bullying and No/Past NSSI showing standardized coefficients

Note: Significant paths are marked with bold numbers and arrows. Sch_victim = school bullying victim; Sch_bully = school bullying perpetrator/bully; Sch_b-v = school bullying bully-victim; *p < .05; **p < .01; ***p < .001

In Model 2, explained variance of current NSSI was 18.9% (Table S1 , Table S2 , Fig.  8 ). Five significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path 2.1. bully – externalizing problems – current NSSI (β = 0.022; SE = 0.01; p < .01); path 2.3. victim – externalizing problems – current NSSI (β = 0.014; SE = 0.01; p < .05); path 2.4. victim – internalizing problems – current NSSI (β = 0.072; SE = 0.01; p < .001); path 2.5. bully-victim – externalizing problems – current NSSI (β = 0.027; SE = 0.01; p < .01); path 2.6. bully-victim – internalizing problems – current NSSI (β = 0.050; SE = 0.01; p < .001) (Table S1 , Table S2 ).

Final Model 2 of school bullying and No/Current NSSI showing standardized coefficients and standard errors

In Model 3, explained variance of past NSSI was 13.6% (Table S1 , Table S2 , Fig.  9 ). Two significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path 3.4. victim – internalizing problems – past NSSI (β = 0.044; SE = 0.01; p < .01); 3.6. bully-victim – internalizing problems – past NSSI (β = 0.032; SE = 0.01; p < .01) (Table S1 , Table S2 ).

Final Model 3 of online bullying and No/Past NSSI showing standardized coefficients and standard errors

Note: Significant paths are marked with bold numbers and arrows. Onl_victim = online bullying victim; Onl_bully = online bullying perpetrator; Onl_b-v = online bullying bully-victim; *p < .05; **p < .01; ***p < .001

In model 4, explained variance of current NSSI was 19.0% (Table S1 , Table S2 , Fig.  10 ). Five significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path 4.1. bully – externalizing problems – current NSSI (β = 0.022; SE = 0.01; p < .01); path 4.3. victim – externalizing problems – current NSSI (β = 0.011; SE = 0.01; p < .05); path 4.4. victim – internalizing problems – current NSSI (β = 0.058; SE = 0.04; p < .001); path 4.5. bully-victim – externalizing problems – current NSSI (β = 0.028; SE = 0.01; p < .01); path 4.6. bully-victim – internalizing problems – current NSSI (β = 0.043; SE = 0.01; p < .001) (Table S1 , Table S2 ).

Final Model 4 of online bullying and No/Current NSSI showing standardized coefficients and standard errors

In Model 5, explained variance of past NSSI was 14.1% (Table S1 , Table S2 , Fig.  11 ). One significant indirect effect size estimate was presented in this mediation model, suggesting a significant mediated pathway: path 5.2. school victimization – internalizing problems – past NSSI (β = 0.062; SE = 0.02; p < .01) (Table S1 , Table S2 ).

Final Model 5 of school victimization and No/Past NSSI showing standardized coefficients and standard errors

Note: Significant paths are marked with bold numbers and arrows. *p < .05; **p < .01; ***p < .001

In Model 6, explained variance of current NSSI was 18.7% (Fig.  12 ). Two significant indirect effect size estimates were presented in this mediation model, suggesting significant mediated pathways: path (6.1) school victimization – externalizing problems – current NSSI (β = 0.044; SE = 0.01; p < .001); path (6.2) school victimization – internalizing problems – current NSSI (β = 0.090; SE = 0.04; p < .001) (Table S1 , Table S2 ).

Final Model 6 of school victimization and No/Current NSSI showing standardized coefficients and standard errors

Our results indicate that mental health problems and NSSI are significantly more relevant for students who are involved in any form of bullying, either in school or online settings, although differences can be detected between various bullying roles. Internalizing and externalizing problems were significant mediators between different bullying roles and current NSSI, although not in the case of NSSI that occurred in the past. School and online bullying did not differ in significant mediation paths.

Our results show a high level of lifetime NSSI (41.17%) in a high-school student sample, which is similar to the latest NSSI research findings [ 12 ].

In our study, we found that more students were involved in traditional bullying than in cyberbullying (23.35% of the students were involved in traditional, school-based bullying, and 16.84% of children were involved in cyberbullying) which is in accordance with similar studies [ 86 ]. The greatest difference in the number of students involved in different roles occurs among online (3.05%) and school (7.98%) bullies. It might be because students do not consider their acts as harmful in the online space and the feedback of the victim is not as direct or visible as in a face-to face situation.

Gender differences we found were in line with previously reported results [ 20 , 22 ]: more girls were victims, more boys were bullies and bully-victims compared to girls, both in school and in online settings. Feijóo and her colleagues [ 87 ] – measuring school victim status in two ways – found that boys suffered more physical violence, were insulted, called names and were threatened, while girls were victims of more relational bullying behaviors (e.g., were excluded or ignored; had rumors spread about them).

Our results show that when measuring concrete types of school victimization, remarkably more students report being victims compared to when only the frequency of being a victim is measured without specific types of bullying listed. It raises attention on the possible phenomenon that high-school students might not be familiar with the terms of assault and victimization. It is also possible that they are not aware of the fact that certain behaviors towards them in school settings are considered as intentional harm doing or aggression. This result is a valuable information for teachers and scientists: it suggests that students might not be aware of the concept of bullying or that different harmful acts should be considered peer violence.

In accordance with meta-analytical findings [ 40 ], victims, bullies and bully-victims were more likely to engage in NSSI than their peers who were not involved in bullying. Students involved in NSSI report more frequent school victimization compared to peers not involved in bullying. Furthermore, those who were not involved in any role of bullying (neither at school or online) reported using significantly less NSSI methods compared to involved participants. This can be interpreted by using the interpersonal theory of NSSI [ 88 ], which considers NSSI as a negative coping strategy, aimed to reduce the stress caused by negative interpersonal events, such as bullying. Bullying is an adverse interpersonal event as a victim, but also as a perpetrator [ 40 ]. Furthermore, the General Strain Theory [ 89 ] suggests that bullying can be experienced as an unjust act that can be resolved with an aggressive behavior. From the victim’s perspective, aggression towards oneself might be the only available option, thus self-harm can be perceived as a temporarily effective way to manage one’s own stress [ 40 ].

Students who are involved in school or online bullying use more NSSI methods than not involved peers, and among them, bully-victims use the most. The number of NSSI episodes (e.g., how often it occurs) in our study did not differentiate between students who are involved and who are not involved in bullying. Although many articles use the frequency of NSSI episodes as an indicator of NSSI severity [ 57 ], it is suggested that the number of NSSI methods predict severity more significantly. NSSI frequency and the number of used methods can also interact, defining a subgroup of individuals seriously at risk [ 90 ]. Robinson and colleagues [ 91 ] found in a community adolescent sample that among adolescents with a lifetime history of NSSI, the number of NSSI methods was strongly associated with reporting suicidal thoughts and behaviors while the number of NSSI episodes was not.

Those who currently engage in NSSI seem to be vulnerable for both externalizing and internalizing problems. Bully-victims reported the highest level of externalizing symptoms in school and online settings, while victims’ level of internalizing problems was the highest both in school and online settings. Higher levels of internalizing problems were present in bully-victims as well. Internalizing symptoms are often conceptualized as significant negative longitudinal outcomes of bullying victimization [ 92 ], however this longitudinal association seem to be bidirectional [ 93 ]. Peer victimization – considered as a significant stressor – can result in internalizing symptoms in adolescents who tend to interpret stressful events in a self-critical manner [ 32 ]. Furthermore, internalizing problems might increase the risk of becoming a target of peer victimization due to individual vulnerabilities (e.g., social withdrawal, avoidance, fearfulness) [ 94 ]. A result that might raise attention on the possible differences of the nature of online and school bullying is that bullies and bully-victims reported higher level of externalizing problems in online settings than in school settings. Online bully-victims also reported significantly higher levels of externalizing symptoms than online victims, a difference, which was not present in school settings. A possible explanation of this might be the online disinhibition phenomenon [ 95 ], that suggests that in online settings users tend to lose their normal capacity of inhibition, partly or completely, as there is no fear of rejection or judgement [ 96 ]. Online bullying might also be a more impulsive act, as the perpetrator has no personal connection with the victim, no facial expression of the victim’s emotions is available and no acquaintance, previous personal contact or physical imbalance is needed [ 86 , 97 ].

In our study we found that, externalizing and internalizing symptoms are more present in students involved in any role of bullying compared to their not involved peers, but different roles seem to be associated differently to symptoms. The differences were not always significant between bullying roles: the results suggest that bully-victims are the most vulnerable group in school and online bullying regarding mental health problems, both in externalizing and internalizing problems. It might be because bully-victims are rejected and isolated by peers and at the same time they are influenced negatively (e.g., to engage in fights) by those adolescents they are friends with [ 29 ]. This suggests that contextual predictors (e.g., peer status and peer influence) can be essential to deal with the bully-victim status. In accordance with Cook’s [ 29 ] meta-analytical findings our results suggest that a bully is possibly an adolescent with significant externalizing behaviors, and also having internalizing symptoms. A victim is an adolescent showing major internalizing symptoms but also engaging in externalizing behaviors to some extent. A bully-victim possibly has comorbid externalizing and internalizing problems which can further worsen his or her mental health.

Models including current NSSI show slightly higher explained variances than those investigating past NSSI. Regarding the mediation models, our main question was whether externalizing and internalizing symptoms mediate the association between different school and online bullying roles and current and past NSSI. Based on indirect effects, results show diverse mediation patterns with specific paths identified regarding different bullying roles.

When NSSI occurred in the past but not currently, both online and school victim and bully-victim roles were significantly associated to NSSI via internalizing problems. The results also underline that school and online victim roles are more strongly associated to internalizing problems and suggest that bully-victims might have comorbid internalizing and externalizing symptoms. Only internalizing symptoms emerged as significant mediators due to the lack of association between externalizing symptoms and past NSSI that happened at least a month before data collection. Based on our mediation analysis and other results, internalizing symptoms are more strongly associated to NSSI (both past and current) than externalizing problems. Emotional and internalizing disorders show clear conceptual overlap with NSSI, as in emotional disorders, negative emotions are often experienced (e.g., fear, anxiety, sadness), which can possibly be maintained by a maladaptive avoidant or coping strategy, like NSSI [ 98 ]. Although, a systematic review suggests that externalizing pathology is also strongly associated to self-injurious behaviors [ 56 ], the study included a wide range of externalizing problems (e.g., attention deficit hyperactivity disorder, oppositional defiant disorder, intermittent explosive disorder) that our study did not, furthermore they included studies which did not differentiate between nonsuicidal and suicidal self-injury. Some studies found a link between externalizing pathology and NSSI happened in the previous year (e.g., [ 57 ], but in our study, past NSSI could occur any time earlier in life, therefore, developmental aspect might play a role in the association of NSSI and externalizing symptoms. Furthermore, the questionnaire asked about externalizing and internalizing symptoms in the previous 6 months, but past NSSI could have occurred earlier than that.

In models with current NSSI, externalizing and internalizing problems seem to be a considerable and significant mediator at most of the bullying roles both in school and online settings. Only bully role was not associated to current NSSI via internalizing problems, which is in accordance with the study’s previous findings, namely that bully role is strongly associated to externalizing problems. Victim and bully-victim status were both associated to current NSSI via externalizing and internalizing symptoms as well, which suggests that not only bully-victims might show comorbid internalizing and externalizing symptoms [ 29 ], but victims as well. Therefore, future research should put special attention on bully-victims and also on victims to specify which leading symptom(s) might be in direct association to the involvement in bullying. Longitudinal studies can reveal the dynamics of the development of being a bully-victim: whether bully-victims were victimized first (i.e., bullied by others) and then started to bully others, or in the opposite way, whether they were initially bullies who then became victims because others took revenge against them [ 44 , 99 ]. A study found that victims in a bullying episode might use aggressive strategies to cope with the situation that tend to perpetuate and escalate the bullying interaction [ 100 ] and therefore might make them a bully-victim. This might be especially true for victims with a relatively high level of externalization [ 86 ] which can also explain our findings that victim status was associated not only to internalizing but to externalizing symptoms as well. In the bully-victim role, guilt might have a special role as well due to the experiences both as a perpetrator who commits the same acts as were done to the person previously [ 101 ].

The results of the models containing school and online bullying, victim role was confirmed by the last two models, containing the frequency of different school victimization types; frequency of school victimization was associated to past NSSI only via internalizing symptoms, while to current NSSI both types of symptoms emerged as significant paths.

The mediation analysis, the settings of bullying (school or online) did not show differences regarding the significant paths via the mediators, which indicates that, in the association of bullying and NSSI, internalizing and externalizing symptoms do not differentiate between school and online settings. The results also suggest that internalizing and externalizing symptoms should be addressed when NSSI occurs currently in a student’s life. As externalizing and internalizing problems only partly mediate the association between different bullying roles and NSSI, to build a complex model, other factors should be considered as well. Some psychological features had been already identified as mediator variables, like social self-efficacy (an individual’s belief that he or she can effectively carry out social tasks) [ 47 , 102 ], negative emotions [ 45 ], depressive mood and depressive symptoms [ 22 , 37 ]. It is also essential to identify factors that can help to cope with stress due to bullying and therefore prevent NSSI as a possible maladaptive coping strategy. Hay and Meldrum [ 45 ] found that the relationship between bullying victimization and NSSI almost disappeared in those adolescents who experienced supportive parenting practices. The need for evidence-based guidelines to prevent and react to NSSI behaviors within schools had already been articulated [ 103 ] and the current study emphasizes its importance by highlighting that school-related factors, like bullying, is associated to NSSI.

Finally, limitations of this study are considered. A possible limitation of the study is its cross-sectional nature, which does not allow any assumptions, whether bullying or externalizing and internalizing problems are predicting NSSI or not. Another limitation might be the validity of the measurement of different bullying roles. In the current study, when asking the frequency of concrete school victimization types, participants reported a remarkably higher occurrence of school victimization than when asking only the frequency in general. As different bullying roles (victim, bully, bully-victim) were established based on the reported frequency (without asking concrete acts), it is possible that participants would have reported a higher and therefore more valid frequency of bully acts based on different types of bully acts given. This limitation however raises attention to the importance of making awareness of the concept and nature of bullying and peer violence in schools. A relatively high prevalence of bullying might be because one single act of bullying (perpetration, victimization, or both) was enough to fulfill a certain category of bullying role. Regarding bullying roles, another limitation should be the possible clustering effect of students from the same class (students from the same class know each other and spend a lot of time together), that was not controlled in the current study. Future studies using more robust analyses (e.g., multilevel structural equation modeling) are suggested to take care of this statistical issue. The unequal number of male and female participants in this study should be considered a limitation, as gender differences are remarkable in NSSI [ 104 ] and in bullying [ 24 ] as well.

In our study, we applied a traditional way of distinguishing different bullying roles (bully, victim, bully-victim) [ 19 , 20 ] however, according to other perspectives, children could fall along a bully-victim continuum and roles demonstrate a significant overlap [ 105 ]. The results should be interpreted with the approach that due to the possible overlap between different bullying roles that were not taken into consideration in the current study, it is possible that students involved in both online and school bullying but in different roles have different psychological needs and difficulties compared to students being involved in one form of bullying, in one single role. Therefore, in future studies, latent cluster or latent profile analyses should be applied to be able to distinguish these, often co-occurring bullying roles.

Although, the sample size of the current study is adequate to make complex statistical analyses, eight subgroups were formed (school victims, school bullies, school bully-victims, not involved participants in school bullying, online victims, online bullies, online bully-victims, not involved participants in online bullying) from which the group of online bullies contains only n = 31 participants.

Current NSSI seems to be more relevant regarding bullying in our study, but a limitation might be that past NSSI could have occurred any time during life, while bullying roles and psychological symptoms (externalizing and internalizing problems) were measured based on the occurrence during the previous few months, or previous six months, respectively. Finally, regarding that our study focused exclusively on the path through which bullying is linked to NSSI via externalizing and internalizing symptoms, future research should focus on other possible mediator and moderator variables.

Based on the results, students involved in bullying are more vulnerable to NSSI and to psychological symptoms compared to their peers who are not involved in bullying. Externalizing and internalizing problems do significantly mediate the association of different bullying roles and NSSI, but to different extent and through different paths. Psychological symptoms seem to play a significant role when NSSI occurs currently throughout the last month. Bully role seems to be associated firstly to externalizing symptoms, but internalizing problems can be present as well. Victim role seems to be slightly associated to externalizing problems, but internalizing symptoms should be addressed in the first place. At bully-victims, comorbid internalizing and externalizing symptoms might occur, however their engagement in NSSI does not seem to be more severe than victims’ or bully’s engagement. Bullying prevention is important because its connection to NSSI is significant. Inconsistencies regarding the self-report of victim role and different types of victimization raises attention on the importance of raising awareness on the phenomenon of bullying and empowering more vulnerable students to be conscious about being maltreated by peers.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Nonsuicidal self-injury

World Health Organization

Health Behaviour in School-aged Children study

Inventory of Statements About Self-Injury

Strengths and Difficulties Questionnaire

Standard deviation

Klonsky ED, Victor SE, Saffer BY. Nonsuicidal self-injury: what we know, and what we need to know. Can J Psychiatry. 2014;59:565–8.

Article   PubMed   PubMed Central   Google Scholar  

Lloyd-Richardson EE, Perrine N, Dierker L, Kelley ML. Characteristics and functions of non-suicidal self-injury in a community sample of adolescents. Psychol Med. 2007;37(8):1183–92.

Nock MK. Self-Injury. Annu Rev Clin Psychol. 2010;6(1):339–63.

Article   PubMed   Google Scholar  

Cybulski L, Ashcroft DM, Carr MJ, Garg S, Chew-Graham CA, Kapur N, et al. Temporal trends in annual incidence rates for psychiatric disorders and self-harm among children and adolescents in the UK, 2003–2018. BMC Psychiatry. 2021;21(1):229.

Gillies D, Christou MA, Dixon AC, Featherston OJ, Rapti I, Garcia-Anguita A, et al. Prevalence and characteristics of self-harm in adolescents: Meta-analyses of community-based studies 1990–2015. J Am Acad Child Adolesc Psychiatry. 2018;57(10):733–41.

Asarnow JR, Porta G, Spirito A, Emslie G, Clarke G, Wagner KD, et al. Suicide attempts and Nonsuicidal Self-Injury in the treatment of resistant depression in adolescents: findings from the TORDIA Study. J Am Acad Child Adolesc Psychiatry. 2011;50(8):772–81.

Ribeiro JD, Franklin JC, Fox KR, Bentley KH, Kleiman EM, Chang BP, et al. Self-injurious thoughts and behaviors as risk factors for future Suicide ideation, attempts, and death: a meta-analysis of longitudinal studies. Psychol Med. 2016;46(2):225–36.

Article   CAS   PubMed   Google Scholar  

Nock MK, Prinstein MJ. A Functional Approach to the Assessment of Self-Mutilative Behavior. J Consult Clin Psychol. 2004;72(5):885–90.

Daukantaitė D, Lundh LG, Wångby-Lundh M, Claréus B, Bjärehed J, Zhou Y, et al. What happens to young adults who have engaged in self-injurious behavior as adolescents? A 10-year follow-up. Eur Child Adolesc Psychiatry. 2021;30(3):475–92.

Plener PL, Schumacher TS, Munz LM, Groschwitz RC. The longitudinal course of non-suicidal self-injury and deliberate self-harm: a systematic review of the literature. Borderline Personal Disord Emot Dysregulation. 2015;2(1):2.

Article   Google Scholar  

Cipriano A, Cella S, Cotrufo P. Nonsuicidal Self-injury: a systematic review. Front Psychol. 2017;(8):1–14.

Faura-Garcia J, Orue I, Calvete E. Cyberbullying victimization and nonsuicidal self-injury in adolescents: the role of maladaptive schemas and dispositional mindfulness. Child Abuse Negl. 2021;118:105135.

Lang J, Yao Y. Prevalence of nonsuicidal self-injury in Chinese middle school and high school students. Med. 2018;97(42):e12916.

Wang YJ, Li X, Ng CH, Xu DW, Hu S, Yuan TF. Risk factors for non-suicidal self-injury (NSSI) in adolescents: a meta-analysis. Clin Med. 2022;46:101350.

Google Scholar  

Olweus D. Bully/victim problems among schoolchildren: Basic facts and effects of a school-based intervention program. In: Pepler DJ, Rubin KH, editors. The development and treatmen of child agression. New York: Psychology Press; 1991. pp. 411–46.

Smith PK, Morita Y, Olweus D, Catalano R, Slee P. The nature of School bullying: a cross-national perspective. 1st ed. London: Routledge; 1999.

Bauman S, Cross D, Walker J. Principles of Cyberbullying Research: definitions, measures, and Methodology. 1st ed. London: Routledge; 2012.

Book   Google Scholar  

Olweus D, Limber SP. Some problems with cyberbullying research. Curr Opin Psychol. 2018;19:139–43.

Olweus D. Bullies on the Playground: the role of victimization. In: Craig HH, editor. Children on playgrounds. Research perspectives and applications. 1st ed. New York: New York Press; 1993.

Solberg ME, Olweus D, Endresen IM. Bullies and victims at school: are they the same pupils? Br J Educ Psychol. 2007;77(2):441–64.

Lin YC, Lin CY. Minor symptoms talk: how children react to encountered bullying. Child Indic Res. 2018;11(6):1755–68.

Claes L, Luyckx K, Baetens I, Van de Ven M, Witteman C. Bullying and victimization, depressive Mood, and Non-suicidal Self-Injury in adolescents: the moderating role of parental support. J Child Fam Stud. 2015;24(11):3363–71.

Luukkonen AH, Riala K, Hakko H, Räsänen P, Workgroup S. 70. Bullying behaviour and substance abuse among Underage Psychiatric Inpatient adolescents. Eur Psychiatry. 2010;25(7):382–9.

Smith PK, López-Castro L, Robinson S, Görzig A. Consistency of gender differences in bullying in cross-cultural surveys. Aggress Violent Behav. 2019;45:33–40.

Inchley JC, Stevens GWJM, Samdal O, Currie DB. Enhancing Understanding of Adolescent Health and Well-Being: the Health Behaviour in School-aged children study. J Adolesc Health. 2020;66 Suppl S3-S5.

Németh Á, Várnai D. Kamaszéletmód Magyarországon [ Adolescent life-style in Hungary ]. 1st ed. Budapest: L’Harmattan; 2019.

Eastman M, Foshee V, Ennett S, Sotres-Alvarez D, Reyes HLM, Faris R, et al. Profiles of internalizing and externalizing symptoms associated with bullying victimization. J Adolesc. 2018;65:101–10.

Kelly EV, Newton NC, Stapinski LA, Slade T, Barrett EL, Conrod PJ, et al. Suicidality, internalizing problems and externalizing problems among adolescent bullies, victims and bully-victims. Prev Med. 2015;73:100–5.

Cook CR, Williams KR, Guerra NG, Kim TE, Sadek S. Predictors of bullying and victimization in childhood and adolescence: a meta-analytic investigation. Sch Psychol Q. 2010;25(2):65–83.

Berkowitz R, Benbenishty R. Perceptions of teachers’ support, safety, and absence from school because of fear among victims, bullies, and bully-victims. Am J Orthopsychiatry. 2012;82(1):67–74.

Klomek AB, Kleinman M, Altschuler E, Marrocco F, Amakawa L, Gould MS. High school bullying as a risk for later depression and suicidality. Suicide Life Threat Behav. 2011;41(5):501–16.

Swearer SM, Hymel S. Understanding the psychology of bullying: moving toward a social-ecological diathesis-stress model. Am Psychol. 2015;70(4):344–53.

Kubiszewski V, Fontaine R, Huré K, Rusch E. Cyber-bullying in adolescents: associated psychosocial problems and comparison with school bullying. L’encephale. 2012;39(2):77–84.

Ybarra ML, Mitchell KJ. Online aggressor/targets, aggressors, and targets: a comparison of associated youth characteristics. J Child Psychol Psychiatr. 2004;45(7):1308–16.

Madjar N, Zalsman G, Ben Mordechai TR, Shoval G. Repetitive vs. occasional non-suicidal self-injury and school-related factors among Israeli high school students. Psychiatry Res. 2017;257:358–60.

Evans R, Hurrell C. The role of schools in children and young people’s self-harm and Suicide: systematic review and meta-ethnography of qualitative research. BMC Psychiatry. 2016;14(16):401.

Baiden P, Stewart SL, Fallon B. The mediating effect of depressive symptoms on the relationship between bullying victimization and non-suicidal self-injury among adolescents: findings from community and inpatient mental health settings in Ontario, Canada. Psychiatry Res. 2017;255:238–47.

Alfonso ML, Kaur R. Self-Injury among Early adolescents: identifying segments protected and at Risk*. J Sch Health. 2012;82(12):537–47.

Li X, Chen F, Lin Y, Jia Z, Tucker W, He J, et al. Research on the relationships between psychological problems and school bullying and Non-suicidal Self-Injury among Rural Primary and Middle School students in Developing Areas of China. Int J Environ Res Public Health. 2020;17(10):3371.

Huang H, Ding Y, Wan X, Liang Y, Zhang Y, Lu G, et al. A meta-analysis of the relationship between bullying and non-suicidal self-injury among children and adolescents. Sci Rep. 2022;12(1):17285.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Esposito C, Bacchini D, Affuso G. Adolescent non-suicidal self-injury and its relationships with school bullying and peer rejection. Psychiatry Res. 2019;274:1–6.

Moore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood and adolescence: a systematic review and meta-analysis. World J Psychiatry. 2017;7(1):60–76.

Serafini G, Aguglia A, Amerio A, Canepa G, Adavastro G, Conigliaro C, et al. The relationship between bullying victimization and perpetration and non-suicidal Self-injury: a systematic review. Child Psychiatry Hum Dev. 2021;54:154–75.

Barker ED, Arseneault L, Brendgen M, Fontaine N, Maughan B. Joint Development of Bullying and victimization in adolescence: relations to Delinquency and Self-Harm. J Am Acad Child Adolesc Psychiatry. 2008;47(9):1030–8.

PubMed   Google Scholar  

Hay C, Meldrum R. Bullying victimization and adolescent Self-Harm: testing hypotheses from general strain theory. J Youth Adolesc. 2010;39(5):446–59.

Wiguna T, Minayati K, Kaligis F, Ismail RI, Wijaya E, Murtani BJ, et al. The effect of cyberbullying, abuse, and screen time on non-suicidal Self-Injury among adolescents during the pandemic: a perspective from the Mediating role of stress. Front Psychiatry. 2021;12:743329.

Zhao H, Gong X, Huebner ES, Yang X, Zhou J. Cyberbullying victimization and nonsuicidal self-injury in adolescents: testing a moderated mediating model of emotion reactivity and dispositional mindfulness. J Affect Disord. 2022;299:256–63.

He N, Xiang Y. Child maltreatment and nonsuicidal self-injury among Chinese adolescents: the mediating effect of psychological resilience and loneliness. Child Youth Serv Rev. 2022;133:106335.

Costa RPDO, Peixoto ALRP, Lucas CCA, Falcão DN, Farias JTDS, Viana LFP, Trindade-Filho EM. Profile of non-suicidal self-injury in adolescents: interface with impulsiveness and loneliness. J Pediatr. 2021;97:184–90.

Reinhardt M, Kökönyei G, Rice KG, Drubina B, Urbán R. Functions of nonsuicidal self-injury in a Hungarian community adolescent sample: a psychometric investigation. BMC Psychiatry. 2021;21(1):1–14.

Kowalski RM, Limber SP, Agatston PW, Cyberbullying. Bullying in the digital age. New Jersey: John Wiley & Sons; 2012.

Englander E, Donnerstein E, Kowalski R, Lin CA, Parti K. Defining cyberbullying. Pediatr. 2017;140(Supplement2):148–S151.

Wang M, Eaton NR. Linking non-suicidal self-injury to psychopathology: the utility of transdiagnostic and DSM-based models. J Affect Disord. 2023;332:55–63.

Muehlenkamp JJ, Brausch AM, Washburn JJ. How much is enough? Examining frequency criteria for NSSI disorder in adolescent inpatients. J Consult Clin Psychol. 2017;85(6):611–9.

Wan YH, Xu SJ, Chen J. Longitudinal effects of psychological symptoms on non-suicidal self-injury: a difference between adolescents and young adults in China. Soc Psychiatry Psychiatr Epidemiol. 2015;50:237–47.

Mészáros G, Horváth LO, Balázs J. Self-injury and externalizing pathology: a systematic literature review. BMC Psychiatry. 2017;17(1):160.

Nock MK, Joiner TE, Gordon KH, Lloyd-Richardson E, Prinstein MJ. Non-suicidal self-injury among adolescents: diagnostic correlates and relation to Suicide attempts. Psychiatry Res. 2006;144(1):65–72.

Fox KR, Franklin JC, Ribeiro JD, Kleiman EM, Bentley KH, Nock MK. Meta-analysis of risk factors for nonsuicidal self-injury. Clin Psychol Rev. 2015;42:156–67.

Lee JY, Kim H, Kim SY, Kim JM, Shin IS, Kim SW. Non-suicidal self-injury is associated with psychotic like experiences, depression, and bullying in Korean adolescents. Early Interv Psychiatry. 2021;15(6):1696–704.

Pengpid S, Peltzer K. Anxiety-induced sleep disturbances among in-school adolescents in the United Arab Emirates: Prevalence and associated factors. Asian J Soc Health Behav. 2022;5:18–23.

Chen JK, Yang B, Wang LC, Chang CW, Lin CY. Is psychological distress a risk factor or an outcome of school Violence and cyberbullying perpetrated by adolescents? A short-term longitudinal panel study. J Interpers Violence. 2023;0(0):1–24.

Chen JK, Chang CW, Lin CY, Wang LC. An investigation of Longitudinal associations between Psychological Distress and Student victimization by teachers. J Interpers Violence. 2023;38(3–4):3279–97.

Chen IH, Gamble JH, Lin CY. Peer victimization’s impact on adolescent school belonging, truancy, and life satisfaction: a cross-cohort international comparison. Curr Psychol. 2023;42(2):1402–19.

Lin YC, Latner JD, Fung XCC, Lin CY. Poor health and experiences of being bullied in adolescents: self-perceived overweight and frustration with appearance matter. Obesity. 2018;26(2):397–404.

Cerutti R, Zuffianò A, Spensieri V. The role of Difficulty in identifying and describing feelings in Non-suicidal Self-Injury Behavior (NSSI): associations with perceived attachment quality, Stressful Life events, and suicidal ideation. Front Psychol. 2018;9:318.

Tang J, Yang W, Ahmed NI, Ma Y, Liu HY, Wang JJ, et al. Stressful life events as a predictor for Nonsuicidal Self-Injury in Southern Chinese Adolescence: a cross-sectional study. Med (Baltim). 2016;95(9):e2637.

Andover MS, Morris BW. Expanding and clarifying the role of emotion regulation in Nonsuicidal Self-Injury. Can J Psychiatry. 2014;59(11):569–75.

Klonsky ED. The functions of deliberate self-injury: a review of the evidence. Clin Psychol Rev. 2007;27(2):226–39.

Hankin BL, Abela JRZ. Depression from Childhood through Adolescence and Adulthood: a developmental vulnerability and stress perspective. In: Hankin BL, Abela JRZ, editors. Development of psychopathology: a vulnerability-stress perspective. 1st ed. Thousand Oaks: Sage Publications Inc; 2005. pp. 245–88.

Chapter   Google Scholar  

Zetterqvist M, Lundh LG, Dahlström Ö, Svedin CG. Prevalence and function of Non-suicidal Self-Injury (NSSI) in a community sample of adolescents, using suggested DSM-5 criteria for a potential NSSI disorder. J Abnorm Child Psychol. 2013;41(5):759–73.

Horváth LO, Győri D, Komáromy D, Mészáros G, Szentiványi D, Balázs J. Nonsuicidal Self-Injury and Suicide: the role of life events in clinical and non-clinical populations of adolescents. Front Psychiatry. 2020;11:370.

Waylen A, Wolke D. Sex ‘n’ Drugs ‘n’ rock ‘n’ roll: the meaning and social consequences of pubertal timing. Eur J Endocrinol. 2004;151 Suppl:U151–9.

World Medical Association. Declaration of Helsinki. Ethical principles for medical research involving human subjects. B World Health Organ. 2003;79(4):373–4.

Klonsky ED, Glenn CR. Assessing the functions of non-suicidal self-injury: psychometric properties of the inventory of statements about self-injury (ISAS). J Psychopathol Behav Assess. 2009;31(3):215–9.

Washburn JJ, Klonsky ED, Styer DM, Gebhardt M, Juzwin KR, Aldridge D et al. Short form of the Inventory of Statements About Self-Injury. Poster presentation at the 7th annual meeting of the International Society for the Study of Self-Injury. Chapel Hill, NC. 2012.

Reinhardt M, Kökönyei G, Drubina B, Urbán R. A nem szuicidális önsértés és motivációjának mérése serdülőknél: Az Állítások Az Önsértés Kapcsán Kérdőív (ISAS-HU) magyar adaptációja. Magy Pszichol Szle. 2022;76(3–4):515–48.

Olweus D. The revised Olweus Bully/Victim questionnaire. Bergen: Research Center Health Promotion; 1996.

Craig W, Harel-Fisch Y, Fogel-Grinvald H, et al. A cross-national profile of bullying and victimization among adolescents in 40 countries. Int J Public Health. 2009;54(Suppl):216–24.

Goodman R, Meltzer H, Bailey V. The strengths and difficulties Questionnaire: a pilot study on the validity of the self-report version. Int Rev Psychiatry. 2003;15(1–2):173–7.

Turi E, Tóth I, Gervai J. A Képességek és Nehézségek Kérdőív (SDQ-Magy) további vizsgálata nem-klinikai mintán, fiatal serdülők körében. [Investigation of the strengths and difficulties Questionnaire in the sample of non-clinical young adolescnets] . Psychiat Hun. 2011;26:415–26.

Goodman A, Lamping DL, Ploubidis GB. When to use broader Internalising and Externalising Subscales instead of the hypothesised five subscales on the strengths and difficulties Questionnaire (SDQ): data from British parents, teachers and children. J Abnorm Child Psychol. 2010;38(8):1179–91.

Muthén LK, Muthén B. Mplus user’s guide: Statistical analysis with latent variables, user’s guide. 8th ed. 2017. https://www.statmodel.com/download/usersguide/MplusUserGuideVer_8.pdf Accessed 18 Apr 2023.

Gratz KL, Dixon-Gordon KL, Chapman AL, Tull MT. Diagnosis and characterization of DSM-5 Nonsuicidal Self-Injury disorder using the Clinician-Administered Nonsuicidal Self-Injury Disorder Index. Assessment. 2015;22(5):527–39.

Bollen KA. Structural equations with latent variables. New York: John Wiley & Sons; 1989.

Muthén LK, Muthén BO, Mplus. Statistical analysis with latent variables user’s guide 6.0. Los Angeles: Muthén & Muthén; 2010.

Smith PK, del Barrio C, Tokunaga R. Definitions of bullying and cyberbullying: how useful are the terms? In: Bauman S, Cross D, Walker J, editors. Principles of cyberbullying research: definition, methods, and measures. New York & London: Routledge; 2012. pp. 64–86.

Feijóo S, O’Higgins-Norman J, Foody M, Pichel R, Braña T, Varela J, et al. Sex differences in adolescent bullying behaviours. Psychosoc Interv. 2021;30(2):95–100.

Hilt LM, Nock MK, Lloyd-Richardson EE, Prinstein MJ. Longitudinal study of Nonsuicidal Self-Injury among Young adolescents: Rates, correlates, and preliminary test of an interpersonal model. J Early Adolesc. 2008;28(3):455–69.

Agnew R. Pressured into crime: an overview of general strain theory. UK: Oxford University Press USA; 2006.

Ammerman BA, Jacobucci R, Turner BJ, Dixon-Gordon KL, McCloskey MS. Quantifying the importance of lifetime frequency versus number of methods in conceptualizing nonsuicidal self-injury severity. Psychol Violence. 2020;10:442–51.

Robinson K, Garisch JA, Wilson MS. Nonsuicidal self-injury thoughts and behavioural characteristics: associations with suicidal thoughts and behaviours among community adolescents. J Affect Disord. 2021;282:1247–54.

Camerini AL, Marciano L, Carrara A, Schulz PJ. Cyberbullying perpetration and victimization among children and adolescents: a systematic review of longitudinal studies. Telemat Inf. 2020;49:10162.

Christina S, Magson NR, Kakar V, Rapee RM. The bidirectional relationships between peer victimization and internalizing problems in school-aged children: an updated systematic review and meta-analysis. Clin Psychol Rev. 2021;85:101979.

Schacter HL, Juvonen J. Depressive symptoms, friend distress, and self-blame: risk factors for adolescent peer victimization. J Appl Dev Psychol. 2017;51:35–43.

Gavidia-Payne S, Denny B, Davis K. Children’s self-concept: parental school engagement and student–teacher relationships in rural and urban Australia. Soc Psychol Educ. 2015;18:121–36.

Lapidot-Lefler N, Barak A. Effects of anonymity, invisibility, and lack of eye-contact on toxic online disinhibition. Comput Hum Behav. 2012;28(2):434–43.

Vandebosch H, Van Cleemput K. Defining cyberbullying: a qualitative research into the perceptions of youngsters. Cyberpsychol Behav. 2008;11(4):499–503.

Bentley KH, Cassiello-Robbins CF, Vittorio L, Sauer-Zavala S, Barlow DH. The association between nonsuicidal self-injury and the emotional disorders: a meta-analytic review. Clin Psychol Rev. 2015;37:72–88.

Nansel TR, Overpeck M, Pilla RS, Ruan WJ, Simons-Morton B, Scheidt P. Bullying behaviors among US youth: prevalence and association with psychosocial adjustment. JAMA. 2001;285(16):2094–100.

Wilton MM, Craig WM, Pepler DJ. Emotional regulation and Display in Classroom victims of bullying: characteristic expressions of Affect, coping styles and relevant Contextual factors. Soc Dev. 2000;9(2):226–45.

Ahmed E, Braithwaite V. What, me Ashamed? Shame management and school bullying. J Res Crime Delinquency. 2004;41(3):269–94.

Smith HM, Betz NE. Development and validation of a scale of Perceived Social Self-Efficacy. J Career Assess. 2000;8(3):283–301.

Toste JR, Heath N. School Response to Non-suicidal Self-Injury. Prev R. 2010;17(1):14–7.

Bresin K, Schoenleber M. Gender differences in the prevalence of nonsuicidal self-injury: a meta-analysis. Clin Psychol Rev. 2015;38:55–64.

Várnai DE, Malinowska-Cieślik M, Madarasová Gecková A, Csémy L, Horváth Z. Do neighbors have more peaceful students? Youth Violence profiles among adolescents in the Czech Republic, Hungary, Poland, and Slovakia. Int J Environ Res Public Health. 2022;19(13):7964.

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Melinda Reinhardt was supported by the National Research, Development, and Innovation Office – NKFIH, Budapest, Hungary under grant number FK 138604. Gyöngyi Kökönyei was supported by the Hungarian National Research, Development, and Innovation Office (FK128614, K143764), the Hungarian Brain Research Program (Grants: 2017 − 1.2.1-NKP-2017-00002), and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022).

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Boglárka Drubina

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Table S1 Standardized regression coefficients and total, direct, and indirect effects related to each model.

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Table S2 Odds ratios and 95% confidence intervals of associations between independent, mediator and outcome variables.

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Drubina, B., Kökönyei, G., Várnai, D. et al. Online and school bullying roles: are bully-victims more vulnerable in nonsuicidal self-injury and in psychological symptoms than bullies and victims?. BMC Psychiatry 23 , 945 (2023). https://doi.org/10.1186/s12888-023-05341-3

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Rates of Incidence

• One out of every five (20.2%) students report being bullied. ( National Center for Educational Statistics, 2019 )
• A higher percentage of male than of female students report being physically bullied (6% vs. 4%), whereas a higher percentage of female than of male students reported being the subjects of rumors (18% vs. 9%) and being excluded from activities on purpose (7% vs. 4%). ( National Center for Educational Statistics, 2019 )
• 41% of students who reported being bullied at school indicated that they think the bullying would happen again. ( National Center for Educational Statistics, 2019 )
• Of those students who reported being bullied, 13% were made fun of, called names, or insulted; 13% were the subject of rumors; 5% were pushed, shoved, tripped, or spit on; and 5% were excluded from activities on purpose. ( National Center for Educational Statistics, 2019 )
• A slightly higher portion of female than of male students report being bullied at school (24% vs. 17%). ( National Center for Educational Statistics, 2019 )
• Bullied students reported that bullying occurred in the following places: the hallway or stairwell at school (43%), inside the classroom (42%), in the cafeteria (27%), outside on school grounds (22%), online or by text (15%), in the bathroom or locker room (12%), and on the school bus (8%). ( National Center for Educational Statistics, 2019 )
• 46% of bullied students report notifying an adult at school about the incident. ( National Center for Educational Statistics, 2019 )
• The reasons for being bullied reported most often by students include physical appearance, race/ethnicity, gender, disability, religion, sexual orientation. ( National Center for Educational Statistics, 2019 )
• The federal government began collecting data on school bullying in 2005, when the prevalence of bullying was around 28 percent. ( U.S. Department of Education, 2015 )
• One in five (20.9%) tweens (9 to 12 years old) has been cyberbullied, cyberbullied others, or seen cyberbullying. ( Patchin & Hinduja, 2020 )
• 49.8% of tweens (9 to 12 years old) said they experienced bullying at school and 14.5% of tweens shared they experienced bullying online. ( Patchin & Hinduja, 2020 )
• 13% of tweens (9 to 12 years old) reported experiencing bullying at school and online, while only 1% reported being bullied solely online. ( Patchin & Hinduja, 2020 )

Effects of Bullying

• Students who experience bullying are at increased risk for depression, anxiety, sleep difficulties, lower academic achievement, and dropping out of school. ( Centers for Disease Control, 2019 )
• Students who are both targets of bullying and engage in bullying behavior are at greater risk for both mental health and behavior problems than students who only bully or are only bullied. ( Centers for Disease Control, 2019 )
• Bullied students indicate that bullying has a negative effect on how they feel about themselves (27%), their relationships with friends and family (19%), their school work (19%), and physical health (14%). ( National Center for Educational Statistics, 2019 )
• Tweens who were cyberbullied shared that it negatively impacted their feelings about themselves (69.1%), their friendships (31.9%), their physical health (13.1%), and their schoolwork (6.5%). ( Patchin & Hinduja, 2020 ).
• Among students ages 12 – 18 who reported being bullied at school, 15% were bullied online or by text ( National Center for Educational Statistics, 2019 )
• Reports of cyberbullying are highest among middle school students, followed by high school students, and then primary school students ( Centers for Disease Control, 2019 )
• The percentages of individuals who have experienced cyberbullying at some point in their lifetimes have more than doubled (18% to 37%) from 2007-2019 ( Patchin & Hinduia, 2019 )
• When students were asked about the specific types of cyberbullying they had experienced, mean and hurtful comments (25%) and rumors spread online (22%) were the most commonly-cited ( Patchin et al., 2019 )
• The type of cyberbullying tends to differ by gender. Girls were more likely to say someone spread rumors about them online while boys were more likely to say that someone threatened to hurt them online ( Patchin et al., 2019 )

Cyberbullying Among Tweens (9-12 Years Old)

• One in five tweens (20.9%) has been cyberbullied, cyberbullied others, or seen cyberbullying
• 49.8% of tweens said they experienced bullying at school and 14.5% of tweens shared they experienced bullying online
• 13% of tweens reported experiencing bullying at school and online, while only 1% reported being bullied solely online
• Nine out of ten tweens use social media or gaming apps (Patchin & Hinduja, 2020)
• Tweens shared they were engaging on the following sites, apps, or games: YouTube, Minecraft, Roblox, Google Classroom, Fortnite, TikTok, YouTube Kids, Snapchat, Facebook Messenger Kids, Instagram, Discord, Facebook, and Twitch
• Tweens who were cyberbullied shared that it negatively impacted their feelings about themselves (69.1%), their friendships (31.9%), their physical health (13.1%), and their schoolwork (6.5%)
• Tweens reported using a variety of strategies to stop the bullying including blocking the person bullying them (60.2%), telling a parent (50.8%), ignoring the person (42.8%), reporting it to the website or app (29.8%), and taking a break from the device (29.6%)
• Two-thirds of tweens are willing to step in to defend, support, or assist those being bullied at school and online when they see it
• Barriers to helping when tweens witness bullying at school or online included being afraid of making things worse, not knowing what to do or say, not knowing how to report it online, being afraid others kids will make fun of them, being afraid to get hurt, and not knowing who to tell

SOURCE: Patchin, J.W., & Hinduja, S. (2020). Tween Cyberbullying in 2020. Cyberbullying Research Center and Cartoon Network. Retrieved from: https://i.cartoonnetwork.com/stop-bullying/pdfs/CN_Stop_Bullying_Cyber_Bullying_Report_9.30.20.pdf.

Bullying of Students with Disabilities

• Students with specific learning disabilities, autism spectrum disorder, emotional and behavior disorders, other health impairments, and speech or language impairments report greater rates of victimization than their peers without disabilities longitudinally and their victimization remains consistent over time ( Rose & Gage, 2016 )

Bullying of Students of Color

• 23% of African-American students, 23% of Caucasian students, 16% of Hispanic students, and 7% of Asian students report being bullied at school ( National Center for Educational Statistics, 2019 )

Bullying of Students Who Identify or Are Perceived as LGBTQ

Bullying and suicide, interventions.

• Tweens reported using a variety of strategies to stop the bullying including blocking the person bullying them (60.2%), telling a parent (50.8%), ignoring the person (42.8%), reporting it to the website or app (29.8%), and taking a break from the device (29.6%) ( Patchin & Hinduja, 2020 ).
• Two-thirds of tweens are willing to step in to defend, support, or assist those being bullied at school and online when they see it ( Patchin & Hinduja, 2020 ).
• Barriers to helping when tweens witness bullying at school or online included being afraid of making things worse, not knowing what to do or say, not knowing how to report it online, being afraid others kids will make fun of them, being afraid to get hurt, and not knowing who to tell ( Patchin & Hinduja, 2020 ).

References:

Centers for Disease Control, National Center for Injury Prevention and Control (2019). Preventing bullying. Retrieved from https://www.cdc.gov/violenceprevention/pdf/yv/bullying-factsheet508.pdf .

National Center for Educational Statistics. (2019). Student reports of bullying: Results from the 2017 School Crime Supplement to the National Victimization Survey. US Department of Education. Retrieved from http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2015056 .

Patchin, J. W., & Hinduja, S. (2019). 2019 Cyberbullying Data. Cyberbullying Research Center. Retrieved from https://cyberbullying.org/2019-cyberbullying-data .

Patchin, J.W., & Hinduja, S. (2020). Tween Cyberbullying in 2020. Cyberbullying Research Center and Cartoon Network. Retrieved from: https://i.cartoonnetwork.com/stop-bullying/pdfs/CN_Stop_Bullying_Cyber_Bullying_Report_9.30.20.pdf .

Rose, C. A., & Gage, N. A. (2016). Exploring the involvement of bullying among students with disabilities over time. Exceptional Children, 83 , 298-314. Retrieved from http://journals.sagepub.com/doi/abs/10.1177/0014402916667587 .

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Bullying Research Paper

This sample bullying research paper features: 4600 words (approx. 15 pages), an outline, and a bibliography with 28 sources. Browse other research paper examples for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our writing service for professional assistance. We offer high-quality assignments for reasonable rates.

Introduction

Bullying defined.

• National Variation
• The Importance of Age

Stability of Bullying Roles

• Gender Differences

The Bully-Victim

The peer group, parenting and home environment, sibling relationships, school factors, internalizing problems, academic performance, delinquency and criminality, impact beyond victims.

• Interventions

Future Directions and Conclusion

• Bibliography

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• Bullying in School Research Paper

Bullying has received worldwide attention in the last 30 years as a form of aggressive behavior that can have a significant negative impact on the physical, emotional, and academic development of victims. The first major contribution to the academic study of bullying was made by Dan Olweus, who wrote the first scholarly book in English to deal with bullying. The book was written in response to the suicide of three bullied boys in Norway and reported a high prevalence of school bullying (20 % of Norwegian children reported having some involvement) as well as discussed the success of the world’s first bullying prevention program (Olweus 1993). Olweus’ work opened the way for an explosion of research on bullying, which expanded from an initial interest in schools to include broader contexts such as the workplace, prisons, and sibling relationships. While much of this work is of interest, showing that bullying has the potential to affect a significant proportion of the population, this review focuses on school bullying, as this is the area that has attracted the most research interest to date.

The international literature is repleted with definitions of school bullying, most of which seem to accept that bullying is any type of negative action intended to cause distress or harm that is repeated and targeted against individuals who cannot defend themselves. When research on bullying started in the 1980s, bullying was perceived to comprise only episodes of physical or verbal aggression where the victim was physically attacked or called names. In recent years, the definition of bullying has broadened to include other forms of aggression that are relational in nature and aim to damage the victim’s peer relationships and their social status such as spreading of malicious gossip and social exclusion. Fighting between people of approximately equal strength, a one-time attack, or a good-natured teasing and play fighting are not counted as bullying.

The advent and widespread use of electronic means of communication such as mobile phones and the Internet has made it easier to bully anonymously, through the use of pseudonyms and temporary accounts, at any time and in any place involving a wide audience. This development has meant that the definition of bullying has had to be expanded to account for what the literature refers to as “cyber-bullying” or “electronic bullying.” A nationally representative survey of 7,508 adolescents in the United States in 2005 found that 8.3 % had bullied others and 9.8 % had been bullied electronically at least once in the last 2 months (Wang et al. 2009). In the same year in England and Wales, a survey of pupils aged 11–16 found that 22 % had been cyber-bullied at least once or twice in the last couple months (Smith et al. 2008). The most common form of cyber-bullying internationally is sending threatening and/or nasty text messages.

Bullying Prevalence and Continuity

National variation in bullying.

There are large variations across countries in the prevalence of bullying perpetration and victimization. In an international survey of health-related symptoms among school-aged children, the percentage of students who reported being frequently bullied during the current term ranged from a low of 5 % to 10 % in some countries to a high of 40 % in others (Due et al. 2005). The prevalence of bullies in primary school ranges, in most countries, between 7 % and 12 % and remains at those levels in secondary school (around 10 %). It is unclear whether these differences in prevalence reflect genuinely different levels of engagement in bullying among countries or, at least partly, result from different meanings of the term “bullying” in different countries and differences in methodologies and samples used.

An example of why valid comparisons between countries are not possible is Portugal where the bullying rate is high compared to other countries. Berger (2007) in her analysis found that one detail of educational policy in Portugal may account, among other things, for this higher rate of bullying. In Portuguese schools, children are asked to repeat sixth grade unless they pass a rigorous test. This practice results in at least 10 % of all sixth graders (more often boys) to be held back 2 years or more, and these older, bigger children are almost twice as likely to bully compared to the class average. This suggests that the difference in prevalence rates between countries may be, at least partly, accounted for by external factors including national differences in school policies and environments but also differences in the methodologies used (self-reports vs. peer and/or teacher reports), students’ differing levels of cognitive ability, cultural differences in reporting, and different meanings of the term “bullying” in different countries.

The Importance of Age in Bullying

Despite variations in prevalence, it is a universal finding that bullying victimization is more frequent among younger children and steadily declines with age. A range of explanations have been put forward to explain these age differences (Smith et al. 1999a, b). Compared to older children, younger children are less likely to have developed the appropriate skills and coping strategies to deal effectively with bullies and avert further victimization. Younger children are also less likely to refrain from bullying others due to socialization pressure. Finally, there is evidence that younger students adopt a more inclusive definition of bullying when responding to prevalence surveys, and this may, at least partly, account for the higher reported frequency of bullying victimization in primary school. For example, younger pupils might find it more difficult to distinguish between bullying and fighting, broadening the use of the term bullying to include aggressive behaviors that involve no imbalance of power. Within the general trend of decreasing bullying victimization over time, researchers have observed an abrupt increase in bullying during the transition from primary to secondary school which may reflect some students’ attempts to establish dominance hierarchies in the new school environment. Relational forms of bullying take precedence over physical modes of attack as children grow older and their social skills improve.

There is some controversy in the literature as to the stability of bullying victimization in primary school. Some studies have reported that bullying victimization is relatively stable over a period of up to 4 years in primary school and often continues in secondary school. Other studies have found that only a relatively small proportion of children (around 4–5 %) are victimized repeatedly over time in primary school.

In secondary school, the stability of both bully and victim roles is considerably higher than in primary school according to teacher, peer, and self-reports. It is estimated that two out of three male bullies remain in their role over a 1-year period. Despite the moderate to high stability of the victim and bully roles in secondary school, prevalence rates are lower than in primary school. This suggests that a small number of victims are targeted consistently and systematically in secondary school.

Stability in bullying victimization has been explained in two ways. Firstly, it has been observed that victims select social environments that reinforce the risk of victimization, for example, they are more likely to have friends who are less accepted by the peer group and often victimized themselves. Secondly, victims often lack the social skills to break through in new environments, and this increases the risk that they are labeled as victims and locked in that role over a long period of time. It is important, therefore, to acknowledge that although for some children bullying victimization will be situational, for others it will develop into a trait.

Gender Differences in Bullying

The view that males are more likely to bully and be bullied than females has been dismissed in recent years following a better understanding about the different forms aggressive behavior such as bullying can take. Although males are more likely to engage in physical forms of bullying such as pushing and hitting, females are, according to some studies, more adept at employing relational forms of aggression (e.g., social exclusion, spreading of nasty rumors) against their victims especially during adolescence. No consistent gender differences have been identified in the use of verbal bullying (e.g., calling names, nasty teasing). This suggests that overall gender differences are not as pronounced as originally thought and that bullying is not a male problem.

Characteristics of Children and Adolescents Involved in Bullying

There is some controversy in the literature about the profile of bullies. Initially, studies described children who bullied others as insecure, anxious individuals who have low self-esteem, are unpopular among their classmates, and use aggressive strategies to resolve conflicts. This stereotype was later disputed by research that suggested bullies are socially competent and have superior theory of mind skills (i.e., awareness of others’ mental functions and states) and good levels of social intelligence, knowing how to attain goals without damaging their reputation. Linked to this, there is also debate concerning whether bullies lack empathic skills. Some research suggests that bullies understand the emotions of others but do not share them. The inconsistencies across studies may be, at least partly, due to different definitions of bully status and different methodologies employed. Studies which have distinguished between “pure” bullies and bully/victims have revealed that “pure” bullies have few conduct problems, perform well at school, are popular among their classmates, and do not suffer from physical and psychosomatic health problems.

There is more consensus on the profile of “pure” victims. Research has identified that “pure” victims exhibit elevated levels of depression and anxiety, low self-esteem, and poor social skills. Hawker and Boulton’s (2000) meta-analysis found that peer victimization is more strongly concurrently associated with depression than with anxiety, loneliness, or self-esteem. Another meta-analysis by Card (2003) found that the strongest correlates of the victimization experience are low self-concept, low physical strength, low school enjoyment, poor social skills, and high internalizing and externalizing problems. It was unclear from these reviews of cross-sectional studies, however, whether internalizing problems lead to victimization or vice versa.

The recent body of longitudinal research on bullying and peer victimization more widely suggests that the relationship between internalizing problems such as depression, anxiety and loneliness, and victimization is more likely to be reciprocal, that is, internalizing problems contribute to victimization and vice versa. A metaanalysis of 18 longitudinal studies examining associations between peer victimization and internalizing problems in children and adolescents concluded that internalizing problems both precede and follow peer victimization experiences (Reijntjes et al. 2011). It is worth noting, however, that the path from psychological maladjustment to victimization has not been replicated in all studies. For instance, Bond et al. (2001) found no support for the hypothesis that emotional maladjustment invites victimization.

Recent work suggests that bullying might arise out of early cognitive deficits, including language problems, imperfect causal understanding, and poor inhibitory control that lead to decreased competence with peers, which over time develops into bullying. Research does not support the assertion that physical appearance (e.g., wearing glasses) is a risk factor for being bullied at school. The only physical characteristic that has been associated with an increased risk of victimization is low physical size and strength. There is less evidence on how equality characteristics influence victimization. There is no consistently robust evidence to suggest that ethnic minority children are more at risk of being bullied at school. Sexual orientation has rarely been investigated in longitudinal studies as a possible risk factor of bullying victimization, but there is some, mainly qualitative, evidence of sexual minorities being targeted in secondary schools. There is stronger evidence that children with disabilities are particularly vulnerable to victimization in mainstream settings, although it might be other characteristics of disabled children that make them more vulnerable to victimization such as lack of friends rather than the disability per se.

Olweus (1993) was the first researcher to identify a small proportion of victims of bullying that he called “provocative victims” or “bully-victims,” who bully other children as well as being bullied by them. Research has identified that bully-victims are the most troubled group among children and adolescents involved in bullying incidents. This group displays the highest levels of internalizing problems, including depression, anxiety, low selfesteem, and loneliness. At the same time, they score high on externalizing problems such as aggression, impulsivity, hyperactivity, and conduct problems. Other research has shown that bully-victims display higher levels of neuroticism and psychoticism than either bullies or victims. Bully-victims use aggressive strategies to cope with stressors at school that increase the risk of further victimization and rejection from peers.

Besides the traditional roles of bully, victim, and bully-victim, research has identified that all students take on a role when bullying episodes emerge. Salmivalli et al. (1996) distinguished between six different roles children can take in bullying situations: the bully (leader), the reinforcer (encourages and provides audience), the assistant (follower/helper, e.g., holds the child down), the defender (helps the victim and/or tells bullies to stop), the outsider (stays away from bullying situations), and the victim. Subsequent research established that the three roles of bully, reinforcer, and assistant are closely correlated with each other and, therefore, cannot usefully discriminate between children. In kindergarten, the three most commonly held roles are those of the bully, the victim, and the defender. Fewer students are defenders by middle school, and the majority becomes witnesses or bystanders when bullying takes place. Such passive behavior, although not directly encouraging of bullying, provides a permissive context for bullies that allows them to continue harassing their victims.

Environmental Influences on Bullying

There is clear evidence that parenting styles are related to bullying behavior. Studies indicate that bullies are more likely to have parents who are authoritarian and punitive, disagree more often, and are less supportive. The parents of bullies are more likely to have been bullies themselves when they were young. Victims, on the other hand, are more likely to have been reared in an overprotective family environment. Bully-victims tend to come from family backgrounds that are exposed to abuse and violence and favor the use of harsh, punitive, and restrictive discipline practices. This group reports little positive warmth in their families and more difficulties in communicating with parents.

Family characteristics are related to bullying victimization in different ways for boys and girls. Boys are more prone to victimization when the father is highly critical or absent in his relationship with his son, thus failing to provide a satisfactory role model. Victimization in boys is also associated with maternal overprotectiveness which may hinder boys’ search for autonomy and independence, whereas victimization in girls is more strongly related to maternal hostility which may lead to anxiety and decreased sense of connectedness in relationships.

Very little research has examined longitudinal associations between early home environment and subsequent bullying behavior. The few studies that exist suggest a link between low emotional support and subsequent bullying behavior at school. Parents who are disagreeable, hostile, cold, or rejecting tend to have children who are at risk of becoming aggressive in the future. In a small longitudinal study, Schwartz et al. (1997) found that bully-victims at 10 years were significantly more likely than the other groups to have had experiences with harsh, disorganized, and potentially abusive home environments 5 years earlier. Mother-child interactions at 5 years were characterized by hostile, restrictive, or overly punitive parenting. They were significantly exposed to higher levels of marital conflicts and more likely to come from marginally lower socioeconomic backgrounds. Bullies were found to be exposed to adult aggression and conflicts, but not victimization by adults, and were from lower socioeconomic backgrounds. These findings need to be replicated in larger samples before any safe conclusions can be drawn.

More recently, there has been interest in how sibling relationships affect the development of bullying behavior. There is international evidence that children who are victimized at school are more likely, compared to other groups, to be victimized by their siblings at home. Wolke and Samara (2004) found that more than half of victims of bullying by siblings (50.7 %) were also involved in bullying behavior at school compared to only 12.4 % of those not victimized by siblings, indicating a strong link between intrafamilial and extrafamilial peer relationships. Those who were both victimized at home and at school had the highest behavior problems and were the least prosocial. Similar evidence exists in relation to bullying perpetration, suggesting that those who bully at school tend to exhibit similar behaviors towards their siblings at home.

A number of school factors have also been implicated as correlates of bullying behavior. One of the most consistent findings in the international literature is that the number and quality of friends at school is one of the strongest, if not the strongest, protective factor against bullying victimization. Having friends is not sufficient in itself to protect against victimization. For instance, when at-risk children have friends with internalizing problems, who are physically weak or who themselves are victimized, the relation of children’s behavioral risk to victimization is exacerbated.

More recent work on the role of class structure and climate on bullying has shown that variations in peer structure and dominance hierarchies influence the stability of bullying victimization. For example, victims in primary school classes with a more pronounced hierarchical structure are less likely to escape their victim role compared to those in classes with less clearly marked hierarchies (Sch€afer et al. 2005).

Consequences of Bullying

There has been a growing interest in recent years to investigate the long-term effects of bullying involvement on children’s and adolescents’ social, emotional, behavioral, and academic development using longitudinal samples. The results of these studies suggest that victims and bully-victims manifest more adjustment problems than bullies. Victims and, especially, bully-victims are more likely to show elevated levels of depression, anxiety, and loneliness; perform less well academically; and display conduct problems. The only negative long-term outcome that has consistently been reported in the literature for bullies is their involvement in later offending. There is also some initial evidence that bullying perpetration is a significant risk factor of poor academic performance.

Several cross-sectional studies have demonstrated negative associations between peer victimization and a range of internalizing problems, including loneliness and low self-esteem. A meta-analysis of 23 cross-sectional studies of the association between peer victimization and psychological maladjustment found that peer victimization was more strongly concurrently associated with depression than with anxiety, loneliness, or self-esteem (Hawker and Boulton 2000).

Over the last decade, research on bullying is increasingly reliant on longitudinal methodologies to disentangle whether victimization contributes to internalizing problems or vice versa. It has been argued, for example, that children who display internalizing behaviors (e.g., anxiety or shyness) are more at risk of being targeted by peers due to their inability to cope effectively with provocation. The majority of longitudinal studies investigating associations between peer victimization and psychological maladjustment have found evidence for both directions.

There is some longitudinal evidence that bullying involvement has a negative impact on academic performance, although more studies are needed to reach a definitive conclusion. A US longitudinal study that began in 2002 with a sample of about 1,700 adolescents found that being a bully had a stronger negative effect on self-perceived academic competence over time than being a victim after controlling for demographic background variables and baseline academic competence (Ma et al. 2009). Furthermore, only bully status predicted lower self-reported grades.

Despite showing fewer adjustment problems than victims and bully-victims, bullies are at an increased risk of later delinquency and criminal offending. A recent meta-analysis of studies measuring school bullying and later offending found that school bullies were 2.5 times more likely than noninvolved students to engage in offending over an 11-year follow-up period (Ttofi et al. 2011). The risk was lower when major childhood risk factors were controlled for, but remained statistically significant. The effect of bullying on later offending was especially pronounced when bullying was assessed in older children. The longitudinal association between bullying perpetration and later offending has been replicated in many countries, including Australia, Canada, and Europe.

Finally, there is evidence that bullying and victimization have a negative impact not only on the individual children involved but also on bystanders. Children who witness bullying incidents report increased anxiety, less satisfaction with school, and lower academic achievement. There is also evidence that in school classes where a lot of victimization is taking place, school satisfaction among students is low.

Bullying Interventions

Following the development of the first anti-bullying program by Dan Olweus in Norway in the 1980s, a considerable number of anti-bullying interventions have flourished around the world to reduce bullying behaviors and protect victims. These fall under four broad categories: curriculum interventions generally designed to promote an anti-bullying attitude within the classroom; whole-school programs that intervene on the school, class, and individual level and address bullying as a systemic problem; social and behavioral skills training; and peer support programs including befriending and peer mediation. A systematic review conducted in 2004 evaluated the strength of scientific evidence in support of anti-bullying programs (Vreeman and Carroll 2007). The review concluded that only a small number of anti-bullying programs have been evaluated rigorously enough to permit strong conclusions about their effectiveness.

Whole-school interventions were found to be more effective in reducing victimization and bullying than interventions that focused only on curriculum changes or social and behavioral skills training. Targeting the whole school involves actions to improve the supervision of the playground, having regular meetings between parents and teachers, setting clear guidelines for dealing with bullying, and using role-playing and other techniques to teach students about bullying. The success of whole-school interventions, relative to other stand-alone approaches, supports the view that bullying is a systemic, sociocultural phenomenon derived from factors operating at the individual, class, school, family, and community level. Hence, interventions that target only one level are unlikely to have a significant impact.

A more recent systematic review of school-based anti-bullying programs found that, overall, these programs are effective in reducing bullying perpetration and victimization by an average of 20–23 % and 17–20 %, respectively (Farrington and Ttofi 2009). The interventions that were found to be most effective were those that incorporated parent training/meetings, disciplinary methods, and videos; targeted older children; and were delivered intensively and for longer. There is less robust evidence on the effectiveness of peer support programs that include activities such as befriending, peer counseling, conflict resolution, or mediation, and a systematic review suggested their use may lead to increases in bullying victimization.

More recently, there has been a growing interest in the use of virtual learning environments to reduce bullying at schools. The basic feature of these programs is a computer-based environment that creates a highly believable learning experience for children who find themselves “present” in the situation that causes emotional distress and, as a result, learn experientially how to deal with school problems. An example of such a program is “FearNot,” an intervention that was developed to help victims of bullying explore the success or otherwise of different coping strategies to dealing with bullying victimization through interactions with “virtual” victims of school bullying. The evaluation of this intervention found that the victims that received the intervention were more likely to escape victimization in the short term than victims in control schools who did not interact with the software (Sapouna et al. 2010). These results suggest that the use of virtual environments might be an engaging and useful component of whole-school anti-bullying policies that merits further testing. A key finding that emerged from this research is that interventions are more likely to be successful if they have the support of teachers and other school personnel and there is a strong commitment to reduce bullying in the school community. This is considered to be one of the reasons behind the huge success of the Olweus’ prevention program that has not been replicated to date.

Although an abundance of knowledge has emerged in recent years regarding the correlates of bullying behavior, there is still relatively little known about the causal processes and mechanisms associated with the bully and victim status. Longitudinal studies, which track bullies and victims over time, offer one of the best chances of disentangling the antecedents of bullying perpetration and victimization from its consequences, and these should form a key part of future research in this field. Another approach which shows much promise is the cutting-edge attempt to unravel the causes of bullying behavior made by researchers investigating biological and environmental influences and the way these influences interact.

One of these studies, involving 1,116 families with 10-year-old twins, found that the tendency for children to be bullied was largely explained by genetics (73 % of variance) and less so by environmental factors that were unique to each child (Ball et al. 2008). Another study of 506 six-year-old twins found that variance in victimization was accounted for only by shared and non-shared environmental influences (29 % and 71 %, respectively) and was not related to the child’s genetic predisposition (Brendgen et al. 2008). These discrepancies might be explained by differences in methodologies used, as studies drew on different informants to assess bullying victimization (mothers and peers, respectively). Although results to date have been contradictory, future breakthroughs in this area have the potential to transform radically the study of bullying.

To understand more fully how bullying behaviors develop, future research will also need to investigate in more depth how individual and classroom level factors interact to cause involvement in bullying. It is not currently understood whether the relationship between risk factors and bullying is the same across different school and class environments or the extent to which consequences of bullying and victimization are dependent on class-and school-level factors.

Finally, another area that would benefit from more attention is the investigation of resilience to bullying. Some initial evidence suggests that maternal warmth has an environmental effect in protecting children from negative outcomes associated with victimization (Bowes et al. 2010). However, we still know relatively little about the factors that promote resilience to bullying and victimization among at-risk children, and also what role bullying has to play in increasing resilience. We also know little about the factors that help victims cope better with the effects of victimization.

To conclude, what the recent flurry of research activity has highlighted is how complex the bullying phenomenon is and that, although much has been learned to date, there is clearly a great need to understand how variables describing the family, school, class, and community environment interact with individual characteristics to determine who gets bullied and who bullies others. Research should neither be blind to nor discouraged by these complexities.

Bibliography:

• Ball HA, Arseneault L, Taylor A, Maughan B, Caspi A, Moffitt TE (2008) Genetic and environmental influences on victims, bullies and bully-victims in childhood. J Child Psychol Psychiatry 49(1):104–112
• Berger Stassen K (2007) Update on bullying at school: science forgotten? Dev Rev 21:90–126
• Bond L, Carlin J, Thomas L, Rubin K, Patton G (2001) Does bullying cause emotional problems? A prospective study of young teenagers. Br Med J 323:480–484
• Bowes L, Maughan B, Caspi A, Moffitt TE, Arseneault L (2010) Families promote emotional and behavioral resilience to bullying: evidence of an environmental effect. J Child Psychol Psychiatry 51(7):809–817
• Brendgen M, Boivin M, Vitaro F, Girard A, Dionne G, Perusse D (2008) Dev Psychopathol 20(2):455–471
• Card N (2003) Victims of peer aggression: a meta-analytic review. Presented at the biennial meeting of the society for research on child development, Tampa, 24–27 Apr 2003
• Due P, Holstein BE, Lynch J, Diderichsen F, Gabhain SN, Scheidt P, Currie C, Health Behavior in School-Aged Children Bullying Working Group (2005) Bullying and symptoms among school-aged children: international comparative cross-sectional study in 28 countries. Eur J Pub Health 15:128–132
• Espelage DL, Swearer SM (2003) Bullying in American schools: a social-ecological perspective on prevention and intervention. Lawrence Erlbaum, Mahwah
• Farrington DP, Ttofi MM (2009) School-based programs to reduce bullying and victimization. Campbell Syst Rev 2009:6
• Hawker D, Boulton M (2000) Twenty years’ research on peer victimization and psychosocial maladjustment: a meta-analytic review of cross-sectional studies. J Child Psychol Psy 41:441–455
• Juvonen J, Graham S (2001) Peer harassment in school: the plight of the vulnerable and victimized. Guilford Press, New York
• Ma L, Phelps E, Lerner JV, Lerner RM (2009) The development of academic competence among adolescents who bully and who are bullied. J Appl Dev Psychol 30(5):628–644
• Olweus D (1993) Bullying at school:what we know and what we can do. Blackwell, Cambridge, MA
• Olweus D (1994) Annotation: bullying at school: basic facts and effects of a school-based intervention program. J Child Psychol Psychiatry 35:1171–1190
• Reijntjes A, Kamphuis JH, Prinzie P, Boelen PA, van der Schoot M, Telch MJ (2011) Prospective linkages between peer victimization and externalizing problems in children: a meta-analysis. Aggress Behav 37(3):215–222
• Salmivalli C, Lagerspetz K, Bjorkqvist K, Osterman K, Kaukiainen A (1996) Bullying as a group process: participant roles and their relations to social status within the group. Aggress Behav 22:1–15
• Sapouna M, Wolke D, Vannini N, Watson S, Woods S, Schneider W, Enz S, Hall L, Paiva A, Andre E, Dautenhahn K, Aylett R (2010) Virtual learning intervention to reduce bullying victimization in primary school: a controlled trial. J Child Psychol Psychiatry 51(1):104–112
• Sch€afer M, Korn S, Brodbeck FC, Wolke D, Schulz H (2005) Bullying roles in changing contexts: the stability of victim and bully roles from primary to secondary school. Int J Behav Dev 29:323–335
• Schwartz D, Dodge KA, Pettit GS, Bates JE (1997) The early socialization of aggressive victims of bullying. Child Dev 68(4):665–675
• Smith PK, Madsen K, Moody J (1999a) What causes the age decline in being bullied at school? Towards a developmental analysis of risks of being bullied. Educ Res 41:267–285
• Smith PK, Morita Y, Junger-Tas J, Olweus D, Catalano R, Slee P (eds) (1999b) The nature of school bullying: a cross-national perspective. Routledge, London
• Smith PK, Cowie H, Olafsson R, Liefooghe APD (2002) Definitions of bullying: a comparison of terms used, and age and sex differences, in a 14-country international comparison. Child Dev 73:1119–1133
• Smith PK, Pepler D, Rigby K (2004) Bullying in schools: how successful can interventions be? Cambridge University Press, New York
• Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N (2008) Cyberbullying: its nature and impact in secondary school pupils. J Child Psychol Psychiatry 49(4):376–385
• Ttofi MM, Farrington DP, Losel F, Loeber R (2011) The predictive efficiency of school bullying versus later offending: a systematic/meta-analytic review of longitudinal studies. Crim Beh Ment Health 21:80–89
• Vreeman RC, Carroll AE (2007) A systematic review of school-based interventions to prevent bullying. Arch Pediatr Adolesc Med 161(1):78–88
• Wang J, Ionnotti RJ, Nansel TR (2009) School bullying among adolescents in the United States: physical, verbal, relational, and cyber. J Adolesc Health 45:368–375
• Wolke D, Samara M (2004) Bullied by siblings: association with peer victimization and behavior problems in Israeli lower secondary school children. J Child Psychol Psychiatry 45(5):1015–1029

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Committee on the Biological and Psychosocial Effects of Peer Victimization: Lessons for Bullying Prevention; Board on Children, Youth, and Families; Committee on Law and Justice; Division of Behavioral and Social Sciences and Education; Health and Medicine Division; National Academies of Sciences, Engineering, and Medicine; Rivara F, Le Menestrel S, editors. Preventing Bullying Through Science, Policy, and Practice. Washington (DC): National Academies Press (US); 2016 Sep 14.

Preventing Bullying Through Science, Policy, and Practice.

• Hardcopy Version at National Academies Press

2 The Scope of the Problem

Although attention to bullying has increased markedly among researchers, policy makers, and the media since the late 1990s, bullying and cyberbullying research is underdeveloped and uneven. Despite a growing literature on bullying in the United States, a reliable estimate for the number of children who are bullied in the United States today still eludes the field ( Kowalski et al., 2012 ; Olweus, 2013 ). Estimates of bullying prevalence vary greatly, and there is little consensus on the value and accuracy of existing estimates.

This chapter describes the current state of research focused on estimating rates of bullying and cyberbullying in the United States and based on the findings from four major, federally funded, nationally representative samples. The committee considers overall trends in these prevalence estimates, as well as areas of inconsistencies and potential reasons for these discrepancies across the particular studies. The committee also draws upon other large-scale studies to provide insight into various demographic factors—such as gender, age, and ethnicity—as potential risk or protective factors for youth involvement in bullying. Although perceptions and interpretations of communications may be different in digital communities, the committee decided to address cyberbullying within a shared bullying framework rather than treating cyberbullying and traditional bullying as separate entities because there are shared risk factors, shared negative consequences, and interventions that work on both cyberbullying and traditional bullying. However, there are differences between these behaviors that have been noted in previous research, such as different power differentials, different perceptions of communication, and questions of how best to approach the issue of repetition in an online context. These differences suggest that although the Centers for Disease Control and Prevention (CDC) definition, developed in the context of traditional bullying, may not apply in a blanket fashion to cyberbullying, these two forms are not separate species. This chapter offers insights into the complexities and limitations of current estimates and underscores the challenges faced by policy makers, practitioners, advocates, and researchers. 1 Although exact estimates are challenging to identify and require more comprehensive measurement of bullying that addresses the current prevalence research limitations, it is clear that a sizable portion of youth is exposed to bullying.

Perspectives from the Field

“[Bullying is] emotionally, or mentally, or physically putting down someone and it happens everywhere, it never stops.”

—Young adult in a focus group discussing bullying (See Appendix B for additional highlights from interviews.)

• NATIONALLY REPRESENTATIVE STUDIES OF BULLYING IN THE UNITED STATES

Several national surveys provide insight into the prevalence of bullying and cyberbullying in the United States. In this section, the committee focuses specifically on the School Crime Supplement (SCS) of the National Crime Victimization Survey (NCVS), the National School-Based Youth Risk Behavior Survey (YRBS), the Health Behaviour in School-Aged Children (HBSC) survey, and the National Survey of Children's Exposure to Violence (NatSCEV) because their samples of youth are nationally representative and epidemiologically defined. The committee notes that there are a number of methodological differences in the samples and measurement across the four studies. The prevalence of bullying behavior at school ranged from 17.9 percent to 30.9 percent, whereas the prevalence of cyberbullying ranged from 6.9 percent to 14.8 percent of youth ( Centers for Disease Control and Prevention, 2014b ; Finkelhor et al., 2015 ; Iannotti, 2013 ; U.S. Department of Education, 2015 ; see Table 2-1 for a summary of these nationally representative surveys and Appendix C for detailed results from these surveys). The discussion below considers in greater detail the strengths and weaknesses of the methods employed by each of these surveys, in an effort to elucidate factors that may contribute to the variation in reported prevalence rates.

Comparison of Current National Data Sources on Bullying for School-Aged Children and Adolescents.

School Crime Supplement of the National Crime Victimization Survey

The SCS is a national survey of 4,942 students ages 12 through 18 in U.S. public and private elementary, middle, and high schools as well as home-schooled youth ( U.S. Department of Education, 2015 ). Created as a supplement to the NCVS and co-designed by the Department of Education, National Center for Education Statistics, and Bureau of Justice Statistics, the SCS survey collects information about victimization, crime, and safety at school ( U.S. Department of Education, 2015 ). The survey was designed to assist policy makers as well as academic researchers and practitioners at the federal, state, and local levels so they can make informed decisions concerning crime in schools. NCVS crime data come from surveys administered by field representatives to a representative sample of households in the United States throughout the year in person and over the phone ( U.S. Department of Education, 2015 ). 2 In 2015, the SCS administration tested two different ways of asking about bullying to better align with the CDC definition of bullying.

The SCS asked students a number of key questions about their experiences with and perceptions of crime and violence that occurred inside their school, on school grounds, on a school bus, or on the way to or from school. 3 Additional questions not included in the NCVS were added to the SCS, such as students' self-reports of being bullied and perceived rejection at school. This survey's approach to bullying and cyberbullying is far more intensive than the other national surveys; however, it is limited by its focus exclusively on reports of being bullied (being a target of bullying behavior), with no information on perpetration. Additional information is also available regarding differences in rates of being bullied and cyberbullied by student characteristics such as gender, race and ethnicity, school and grade level, school enrollment, geographic region, eligibility for reduced-price lunch, household income, and student-teacher ratio. Other characteristics of the events assessed include whether or not an adult was notified of the bullying incident, injury, frequency of bullying, form of bullying, and location of the bullying ( U.S. Department of Education, 2015 ). The SCS data showed that in 2013, 21.5 percent of students ages 12-18 were bullied on school property and 6.9 percent of students were cyberbullied anywhere ( U.S. Department of Education, 2015 ; see Appendix C , Tables C-1 through C-3 ). 4

Although the SCS provides the most recent and in-depth assessment of bullying and cyberbullying prevalence in the United States, it has several major limitations. The questions about being bullied or cyberbullied are only included in the SCS, a supplement to the NCVS; therefore, its sample size is only a fraction of that of the larger NCVS. 5 The SCS and NCVS data, similar to the other national datasets, are voluntary self-report surveys. These surveys focused on students ages 12-18 and on their experience being bullied; data are not available from younger children and from children who have bullied others or children who have witnessed bullying instances. The survey also fails to address rates of bullying among various subpopulations of youth, such as groups differentiated by their sexual orientation or gender identity, by weight status, or by religious minorities.

School-Based Youth Risk Behavior Survey

The YRBS is one component of the Youth Risk Behavior Surveillance System (YRBSS), an epidemiological surveillance system developed by the CDC to monitor the prevalence of youth behaviors that most influence health ( Centers for Disease Control and Prevention, 2014b ). The YRBS is conducted biennially and focuses on priority health-risk behavior established during youth (grades 9-12) that result in the most significant mortality, morbidity, disability, and social problems during both youth and adulthood. 6 State and local education and health agencies are permitted to supplement the national survey to meet their individual needs.

National YRBS

Bullying and cyberbullying estimates include responses by student characteristics, such as gender, race and ethnicity, grade level, and urbanicity of the school. 7 , 8 The data showed that 19.6 percent of children ages 14-18 were bullied on school property and 14.8 percent of children ages 14-18 were electronically bullied ( Centers for Disease Control and Prevention, 2014b ; see Appendix C , Table C-4 ). The data captured by the national YRBS reflect self-report surveys from students enrolled in grades 9-12 at public or private schools. As with the other nationally representative samples, it does not identify many subpopulations that are at increased risk for bullying such as lesbian, gay, bisexual, and transgender (LGBT) youth and overweight children. The YRBS gathers information from adolescents approximately ages 14-17; but it offers no nationally representative information on younger children ( Centers for Disease Control and Prevention, 2014b ). The survey gathers information on Hispanic, black, and white students but does not identify other races and ethnicities.

State and Local YRBS

The YRBSS is the only surveillance system designed to monitor a wide range of priority health risk behavior among representative samples of high school students at the state and local levels as well as the national level ( Centers for Disease Control and Prevention, 2014b ). 9 There is a smaller sample of middle school youth that is included in various state YRBS results, but national-level estimates are not available. The 2014 CDC report includes state- and local-level surveys conducted by 42 states and 21 large urban school districts. Of the 42 states that conducted their own YRBS survey, 26 asked questions about bullying and cyberbullying. 10 The state-specific results for bullying prevalence ranged from a high of 26.3 percent in Montana to a low of 15.7 percent in Florida ( Centers for Disease Control and Prevention, 2014b ). Whereas this state-level high is relatively similar to the prevalence of 19.6 percent reported by the national YRBS, the state-level low is less than a third of the national prevalence. For cyberbullying, the state results ranged from a high of 20.6 percent in Maine to a low of 11.9 percent in Mississippi. The national YRBS cyberbullying prevalence of 14.8 percent is about in the middle of these extremes ( Centers for Disease Control and Prevention, 2014b ).

At this time, the available state and local data are highly variable due to major limitations caused by self-reports, variable definitions of bullying, and the limited age range of students, making it difficult to gauge differences in bullying prevalence among states and in comparison to national estimates.

The Health Behaviour in School-Aged Children Survey

The HBSC survey is an international study that generally addresses youth well-being, health behavior, and their social context ( Iannotti, 2013 ). This research is conducted in collaboration with the World Health Organization Regional Office for Europe, and the survey is administered every 4 years in 43 countries and regions across Europe and North America. The HBSC survey collects data on a wide range of health behaviors, health indicators, and factors that may influence them. These factors are primarily characteristics of the children themselves, such as their psychological attributes and personal circumstances, and characteristics of their perceived social environment, including their family relationships, peer-group associations, school climate, and perceived socioeconomic status ( Iannotti, 2013 ).

The most recent survey focused solely on the United States was conducted in the 2009-2010 school year. The 2009-2010 HBSC survey included questions about nutrition; physical activity; violence; bullying; relationships with family and friends; perceptions of school as a supportive environment; and use of alcohol, tobacco, marijuana, and other drugs ( Iannotti, 2013 ). 11 , 12 Regarding bullying and cyberbullying, the HBSC asked questions only about the frequency with which children were bullied in the “past couple of months,” with follow-up questions about the frequency of a certain type of bullying a student experienced (called names or teased, left out of things, kicked or pushed, etc.). The survey found that 30.9 percent of children ages 10-16 were bullied at school and 14.8 percent of children ages 10-16 were bullied using a computer or e-mail ( Iannotti, 2013 ; see Appendix C , Tables C-6 and C-7 ). 13 The survey is the only nationally representative survey that asked students how often they bullied another student and the type of bullying they carried out. It found that 31.8 percent of students bullied others and 14.0 percent of students cyberbullied other children ( Iannotti, 2013 ). It is the only national survey that asked students to report on the reason they thought they were bullied (e.g., how often were you bullied for your race/color?; how often were you bullied for your religion?). (For additional detail, see Appendix C , Tables C-6 and C-7 ). Nevertheless, like the other surveys reviewed here, the HBSC survey is limited by the nature of self-reported and voluntary data from minors, as well as by its decision to limit questions only to frequency of incidents.

National Survey of Children's Exposure to Violence

The National Survey of Children's Exposure to Violence II (NatSCEV II) was designed to obtain up-to-date incidence and prevalence estimates for a wide range of childhood victimizations ( Finkelhor et al., 2015 ). The first such assessment, the National Survey of Children's Exposure to Violence I (NatSCEV I), was conducted in 2008. This updated assessment, conducted in 2011, asked students to report on 54 forms of offenses against them. The offenses include sexual assault, child maltreatment, conventional crime, Internet victimization, peer and sibling victimization, witnessing victimization, and indirect victimization ( Finkelhor et al., 2015 ). 14 While this survey asked questions regarding bullying-type incidents, many of the questions referred to the offenses as “assault” rather than bullying, which typically includes a wider scope of victimization. It addressed these offenses by age and gender of the child who was bullied. NatSCEV II found that 17.9 percent of children ages 1 month to age 17 had experienced an assault by a nonsibling peer, 1.8 percent of children had experienced a bias assault, and 6.0 percent experienced Internet/cell phone harassment ( Finkelhor et al., 2015 ; see Appendix C , Table C-5 ). It is not clear whether Internet or cell phone harassment meets the CDC definition of bullying.

Trends over Time

Although attention to bullying and cyberbullying has increased, the extent to which rates of bullying have changed in recent years is unclear ( Figures 2-1 and 2-2 ) ( Kowalski et al., 2012 ; Limber, 2014 ). As illustrated in Figure 2-1 , data from the SCS-NCVS indicate a sharp reduction in the percentage of 12-18 year olds who reported being bullied at school—from 27.8 percent to 21.5 percent in just 2 years ( U.S. Department of Education, 2015 ).

Trends in bullying over time as reported by national surveys. NOTES: HBSC = Health Behaviour in School-Aged Children; NatSCEV = National Survey of Children's Exposure to Violence, NCVS = National Crime Victimization Survey; SCS = School Crime Supplement (more...)

Trends in cyberbullying over time as reported by national surveys. NOTES: HBSC = Health Behaviour in School-Aged Children; NatSCEV = National Survey of Children's Exposure to Violence, NCVS = National Crime Victimization Survey; SCS = School Crime Supplement (more...)

While the YRBS and NatSCEV mirror this decline, neither found so large a change ( Finkelhor et al., 2015 ; Centers for Disease Control and Prevention, 2014b ; see Figure 2-1 ). Findings from the HBSC survey show an increase in bullying among 11-, 13-, and 15-year-old youth in the United States of about 1 percentage point between 2006 and 2010 ( Iannotti, 2013 ). As illustrated in Figure 2-2 , the trend in cyberbullying over time is even less clear. According to the SCS-NCVS data, the percentage of students ages 12-18 who were cyberbullied doubled between 2001 and 2007 but declined by 2 percentage points between 2011 and 2013 ( U.S. Department of Education, 2015 ). 15 While the HBSC survey and the YRBS also showed a decline in the percentage of students who have been cyberbullied, the NatSCEV showed an increase in the percentage of students who experienced Internet and/or cell phone harassment (see Figure 2-2 ).

Because the available national trend data are limited in the range of years for which data are available and because findings vary somewhat among the major national samples, it is difficult to gauge the extent to which bullying may have increased or decreased in recent years. Additional data points will be necessary to determine national trends in the prevalence rates for children and youth who are bullied.

• EXISTING ESTIMATES OF BULLYING IN THE UNITED STATES BY SUBPOPULATION

In an effort to understand the nature and extent of bullying in the United States, some studies have examined specific subpopulations or subsets of children involved in bullying incidents. Because the major national surveys that include bullying do not uniformly or fully address the bullying experience of subpopulations of interest, 16 in this section the committee also draws upon findings from meta-analyses and independent large-scale research. Although these studies are limited by inconsistent definitions, survey data based on self-reports, differing age ranges, and a lack of questions seeking responses from children who have bullied or have witnessed bullying incidents, they do provide valuable insight into particular risk factors or protective factors for involvement in bullying, insights that are generally not available from the surveys of nationally representative samples. The committee expands on risk and protective factors in Chapter 3 .

Prevalence of Bullying by Age

A majority of bullying research has shown that children's experiences with bullying vary significantly according to their age. Decreases with age in rates of being bullied were reported in the SCS.

As reported by Limber (2014) , a meta-analysis by Cook and colleagues (2010) found that the likelihood of both being bullied and perpetrating bullying behavior peaked in the early adolescent years (ages 12-14) before decreasing slightly in later adolescence ( Limber, 2014 ). Decreases with increasing grade level in rates of being bullied were also reported in the SCS-NCVS.

For example, whereas 27.8 percent of sixth graders reported being bullied at school in 2013, 23.0 percent of ninth graders and 14.1 percent of twelfth graders said they had been bullied ( U.S. Department of Education, 2015 ; see Figure 2-3 ). Although these data suggest that the overall chances of being bullied are particularly likely in middle childhood, children are more or less likely to be involved in specific forms of bullying at different ages, depending on their verbal, cognitive, and social development ( Limber, 2014 ).

Prevalence of bullying and cyberbullying among students, ages 12-18, by grade level, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

Reports of being bullied through an electronic context appear to peak later than reports of being bullied by a more traditional context; the SCS, for example, reported a peak for cyberbullying in tenth grade ( U.S. Department of Education, 2015 ). According to a 2015 overview of teen's social media and technology use, the Pew Research Center found that 68 percent of teens ages 13-14 had access to a smartphone and 84 percent had access to a desktop or laptop computer, whereas 76 percent of teens ages 15-17 had access to a smartphone and 90 percent had access to a desktop or laptop computer ( Lenhart et al., 2015 ). Today's youth are often referred to as “digital natives” due to their upbringing immersed in technological tools including smartphones and social media, while adults are often referred to as “digital immigrants.” This report found that approximately three-fourths of teens ages 13-17 reported access to a cell phone and 94 percent of teens reported going online daily, including 24 percent who said they go online “almost constantly” ( Lenhart et al., 2015 ). Owning a mobile phone allows for ongoing access to the Internet, including social media and other communication tools that may foster opportunities for bullying. Approximately one-quarter of teens surveyed described themselves as “constantly connected” to the Internet ( Lenhart et al., 2015 ). Among teens 13-17 years old, most reported using several forms of social media including Facebook, Instagram, Snapchat, and Twitter (see Figure 2-4 ). A previous study found that older adolescents viewed Facebook as a powerful source of influence through four major processes: connection to others, comparison with peers, building an online identity, and an immersive multimedia experience ( Moreno et al., 2013 ).

Facebook, Instagram, and Snapchat top social media platforms for teens (n = 1,060 teens ages, 13-17). SOURCE: Adapted from Lenhart (2015, p. 2)

This increasing access to and use of technologies with age may help explain rising rates of cyberbullying as adolescents age. An older study of 10-17 year olds found an “online harassment” prevalence of approximately 9 percent ( Wolak et al., 2007 ). However, a more recent study, which focused on middle school adolescents, found a lower prevalence of cyberbullying: 5 percent reported being a perpetrator of cyberbullying, and 6.6 percent reported being a target of cyberbullying ( Rice et al., 2015 ).

Smith and colleagues (2008) found rates of cyberbullying to be lower than rates of traditional bullying, but appreciable, and reported higher cyberbullying prevalence outside of school than inside. It is possible that reported cyberbullying rates are lower than traditional bullying rates because much of technology use occurs outside of school and current approaches to measuring bullying are designed mostly to assess rates of traditional bullying in school ( Smith et al., 2008 ). Previous work has suggested that increased Internet use is associated with increased risk for cyberbullying ( Juvonen and Gross, 2008 ).

Although research has suggested that the prevalence of bullying among older adolescents is lower than that of younger adolescents, researchers have proposed that cyberbullying among older students may represent a continuation of behaviors from previous grades but with a focus on technological tools for more subtle bullying techniques ( Cowie et al., 2013 ).

Prevalence of Bullying by Gender

Research has confirmed that there are gender differences in the frequency with which children and youth are involved in bullying. A recent meta-analysis found that although boys and girls experienced relatively similar rates of being bullied, boys were more likely to bully others, or to bully others and be bullied, than girls were ( Cook et al., 2010 ; Limber, 2014 ). Research has suggested that there are gender differences in the frequency with which children and youth are involved in bullying. The SCS, YRBS, and NatSCEV found that rates for self-reports of being bullied range from 19.5 to 22.8 percent for boys and from 12.8 to 23.7 percent for girls ( Centers for Disease Control and Prevention, 2014b ; Finkelhor et al., 2015 ; U.S. Department of Education, 2015 ). All three of these national surveys found that girls were more likely to report being bullied than were boys (see Figure 2-5 for SCS data).

Prevalence of being bullied among 12-18 year olds by gender, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

Research has suggested similarities and differences, beyond just overall frequency, in how often boys and girls experience different forms of bullying ( Felix and Green, 2010 ). As noted in Chapter 1 , there are two modes of bullying (direct and indirect) as well as different types of bullying (physical, verbal, relational, and damage to property). As illustrated in Figure 2-6 , being made fun of or called names and being the subject of rumors are the two most common forms of bullying experienced by children and youth, and both are much more frequently experienced than physical bullying ( Iannotti, 2013 ; Limber, 2014 ; U.S. Department of Education, 2015 ). For example, the 2013 SCS found that 13.2 percent of youth ages 12-18 reported being the subject of rumors and 13.6 percent said they had been made fun of, called names, or insulted, compared with 6.0 percent who reported being pushed, shoved, tripped, or spit on ( U.S. Department of Education, 2015 ; see Figure 2-6 ). Notions of gendered forms of bullying are common because physical aggression has been regularly associated with boys, whereas relational aggression has been considered to be the domain of girls ( Oppliger, 2013 ). For example, studies have shown that indirect aggression is normative for both genders, while boys are more strongly represented in physical and verbal aggression (see review by Card et. al., 2008). As for differences in different forms of cyberbullying, according to the 2013 SCS, girls experienced a higher prevalence of being bullied in nearly all types, except for receiving unwanted contact while playing online games and facing purposeful exclusion from an online community ( Limber, 2014 ; U.S. Department of Education, 2015 ; see Figure 2-7 ). However, because there is not yet a common definition of cyberbullying, there is no agreement on what forms of online harassment fall under the umbrella term of “cyberbullying.”

Prevalence of different types of bullying among students, ages 12-18, bullied in a school year, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

Prevalence of different types of cyberbullying among students, ages 12-18, bullied in a school year, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

Limber and colleagues (2013) observed that age trends for self-reports of bullying others varied for boys and girls. Among boys, bullying others increased from grades 3 through 12, but among girls, rates of bullying others peaked in eighth grade ( Limber et al., 2013 ). Among older adolescents and college students, cyberbullying may be more common than traditional bullying. Prevalence rates of cyberbullying among young adults and college students have been estimated to be around 10-15 percent ( Kraft and Wang, 2010 ; Schenk and Fremouw, 2012 ; Wensley and Campbell, 2012 ).

Prevalence of Bullying by Race and Ethnicity

There has been only limited research on the roles that race and ethnicity may play in bullying ( Larochette et al., 2010 ; Peskin et al., 2006 ; Spriggs et al., 2007 ). 17 Data from the SCS indicate that the percentage of students who reported being bullied at school in 2013 was highest for white students (23.7%) and lowest for Asian students (9.2%), with rates for black students (20.3%) and Hispanic students (19.2%) falling between (see Figure 2-8 ; data from U.S. Department of Education, 2015 ). Data from the national YRBS were highest for white students (21.8%), next highest for Hispanic students (17.8%), and lowest for black students (12.7%) ( Centers for Disease Control and Prevention, 2014b ). The YRBS data did not include any other ethnicities/races.

Prevalence of being bullied and cyberbullied among students, ages 12-18, by race/ethnicity, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

It is challenging to interpret the percentages of children and youth who are bullied across different racial and ethnic groups, due to the limited information currently available on racial and ethnic differences in definitions of bullying and on whether and how bullying may vary according to the racial/ethnic diversity and density of schools and communities. See Chapter 3 for a discussion of contextual factors, including the school and community contexts, and their modulation of the relations between individual characteristics and prevalence of involvement in and consequences of bullying by race/ethnicity.

• DISPARITIES IN BULLYING PREVALENCE IN THE UNITED STATES AMONG VULNERABLE GROUPS

In addition to exploring standard demographic differences in bullying (i.e., gender, age, race/ethnicity), researchers have identified specific populations that are at increased risk for being bullied. This section reviews the research on groups for which there is consistent epidemiologic evidence of disparities in being the target of bullying, including LGBT youth, overweight/obese youth, and youth with disabilities. The committee also identified groups for which the evidence of increased risk is not currently consistent and which therefore warrant greater research attention ( U.S. Government Accountability Office, 2012 ). In this chapter, we report descriptive data on prevalence rates; see Chapter 3 for a discussion of factors that contribute to these disparities in rates of bullying (e.g., stigma) as well as research evidence on specific forms of bullying (e.g., bias-based bullying) that are more likely to occur among some of the groups covered in this section.

Differences in Bullying by Sexual Orientation and Gender Identity

LGBT youth, youth questioning their sexuality, and youth who do not conform to gender stereotypes frequently face bullying by their peers ( Eisenberg and Aalsma, 2005 ; Espelage et al., 2008 ; Garofalo et al., 1998 ; Rivers, 2001 ; Russell et al., 2014 ). The prevalence of bullying of lesbian, gay, and bisexual (LGB) males and females ranges from 25.6 percent to 43.6 percent ( Berlan et al., 2010 ).

Most research on bullying related to sexual orientation and gender identity comes from nonprobability samples. For example, the 2003 Massachusetts Youth Risk Behavior Survey found that 42.0 percent of sexual-minority youth reported being bullied in the 12 months prior to survey administration ( Hanlon, 2004 ). Similarly, the cross-sectional analysis of the 2001 questionnaire from the Growing Up Today study, a national longitudinal study involving 7,559 youths (ages 14-22) who were children of nurses participating in the Nurses' Health study found that the prevalence of bullying victimization was lowest in heterosexual female respondents (15.9%) and highest in gay male respondents (43.6%) ( Berlan et al., 2010 ). Girls identifying as “mostly heterosexual” and “mostly bisexual” were at increased risk for perpetrating bullying compared to heterosexual girls, while boys identifying as gay were less likely to perpetrate bullying than were heterosexual boys ( Berlan et al., 2010 ).

A growing body of research has aimed to assess the experiences of transgender youth specifically. The existing quantitative research suggests that most transgender youth experience regular bullying and harassment at school ( Grant et al., 2011 ; Kosciw et al., 2012 ; McGuire et al., 2010 ). For instance, in a sample of 5,542 adolescents sampled online, 82 percent of the transgender or gender nonconforming youth reported any bullying experience in the past 12 months, compared to 57 percent among cisgender boys and girls ( Reisner et al., 2015 ). 18

Measures of sexual orientation—including sexual attraction, sexual behavior, and sexual identity—have been recently incorporated into large surveillance systems, such as some state and local versions of the YRBSS, which have provided population-based estimates of bullying among LGB youth. Two of CDC's large surveillance systems—School Health Profiles and the School Health Policies and Practices studies—assess school health policies and practices relevant to LGB students including the prohibition of harassment and bullying ( Centers for Disease Control and Prevention, 2014a ). The results from these sources provide a means to assess sexual-orientation differences in bullying perpetration and victimization among youth by location within the United States ( Centers for Disease Control and Prevention, 2014a ). 19 Recent analyses by Olsen and colleagues (2014) were conducted by creating two datasets: one that combined 2009-2011 YRBS data from 10 states (Connecticut, Delaware, Hawaii, Illinois, Maine, Massachusetts, North Dakota, Rhode Island, Vermont, and Wisconsin) and the other that combined YRBS data from 10 school districts (Boston, Chicago, District of Columbia, Houston, Los Angeles, Milwaukee, New York City, San Diego, San Francisco, and Seattle). Adjusted prevalence rates for being bullied on school property were lowest for both heterosexual boys and girls (18.3% and 19.9%, respectively, based on the state dataset; 11.4% and 11.8%, respectively, based on the district dataset) and highest among gay boys (43.1% and 25.7%, respectively, based on the state and district datasets) and bisexual boys (35.2% and 33.2%, respectively, based on the state and district datasets) ( Olsen et al., 2014 ). Rates of being bullied on school property were intermediate for the lesbian girls (29.5% in the state dataset, and 14.0% in the district dataset) and bisexual girls (35.3% in the state dataset, and 18.8% in the district dataset).

Given the absence of measures of gender identity disaggregated from sex in these large state and local datasets, population-based estimates of the prevalence of bullying among transgender youth are not currently available. However, recent research has conducted cognitive testing to determine the most reliable and valid way of assessing gender identity among both adults ( GenIUSS Group, 2013 ) and youth (e.g., Conron et al., 2008 ). Further, population-based datasets have very recently begun to include measures of gender identity among youth (e.g., the 2013-2014 California Healthy Kids Survey), which will enable researchers to examine gender identity–related disparities in bullying using representative samples of youth.

Using data from the first wave (1994-1995 school year) of the National Longitudinal Study of Adolescent Health, which included 10,587 youth between 13 and 18, Russell and colleagues (2002) examined differences in experiencing, witnessing, and perpetrating violence, depending on the respondent's self-reported category of romantic attraction (same-sex, both-sex, or other-sex), a measure of sexual orientation. Youth who reported same-sex or both-sex attraction were more likely to experience and perpetrate the most dangerous forms of violence (e.g., pulling a gun or knife on someone, shooting or stabbing someone) and to witness violence ( Russell et al., 2002 ). These findings were not disaggregated by sex or gender identity.

Differences in Bullying Among Youth with Disabilities

Much of the existing data suggests that students with disabilities are overrepresented within the bullying dynamic ( McLaughlin et al., 2010 ; Rose, 2015 ; Rose et al., 2010 ), whether as children who have bullied ( Rose et al., 2009 ), children who have been bullied ( Blake et al., 2012 ; Son et al., 2012 ), or children who have both bullied and have been bullied ( Farmer et al., 2012 ). 20 Specifically, national prevalence data suggest that students with disabilities, as a whole, are up to 1.5 times more likely to be bullied than youth without disabilities ( Blake et al., 2012 ); this disproportionate bullying begins in preschool ( Son et al., 2012 ) and continues through adolescence ( Blake et al., 2012 ; Rose, 2015 ).

However, variability exists in reported prevalence rates of involvement for various subgroups of youth with disabilities. For example, Rose and colleagues (2015) conducted a prevalence study of a large sample of youth with and without disabilities in middle and high school ( n = 14,508) and determined that 35.3 percent of students with emotional and behavioral disorders, 33.9 percent of students with autism spectrum disorders, 24.3 percent of students with intellectual disabilities, 20.8 percent of students with another health impairment, and 19.0 percent of students with specific learning disabilities experienced high levels of victimization. In addition, 15.3 percent of youth with emotional and behavioral disorders, 19.4 percent of youth with autism spectrum disorders, 24.1 percent of youth with intellectual disabilities, 16.9 percent of youth with other health impairment, and 14.4 percent of youth with specific learning disabilities perpetrated bullying behavior. These estimates are in contrast to 14.5 percent of youth without disabilities who experienced high rates of being bullied and 13.5 percent who engaged in high rates of perpetration. The authors of this study acknowledge that the study has a number of limitations—mainly self-report, cross-sectional data, and data that were examined at the group level.

This literature on bullying and disabilities has several inconsistencies, which stem from differences in three basic factors: (1) measurement and definition, (2) disability identification, and (3) comparative groups. For instance, separating subclasses of youth with specific typographies of learning disabilities proves difficult, resulting in the general assessment of a combined class of specific learning disabilities ( Rose, 2015 ). This confounding factor leads to conflicting measures of bullying involvement, with some studies suggesting that rates of bullying perpetration are relatively comparable among youth with and without disabilities ( Rose et al., 2015 ), while others found that students with specific learning disabilities were almost six times more likely to engage in bully perpetration than their peers without disabilities ( Twyman et al., 2010 ). These conflicting results suggest further assessment or disaggregation of subgroups of youth with specific learning disabilities may be necessary to better understand bullying involvement among this subpopulation of youth.

Differences in Bullying by Weight Status

Weight status, specifically being overweight or obese, can be a factor in bullying among children and youth ( Puhl and Latner, 2007 ). The CDC defines childhood overweight as a body mass index (BMI) at or above the 85th percentile and below the 95th percentile of a CDC-defined reference population of the same age and sex. It defines childhood obesity as a BMI at or above the 95th percentile of this reference population for the same age and sex ( Centers for Disease Control and Prevention, 2015b ).

In 2012, 31.8 percent of U.S. children and youth 6 to 19 years of age were overweight or obese, using the CDC weight status categories. Eighteen percent of children 6 to 11 and 21 percent of youth 12 to 19 years of age were obese ( Centers for Disease Control and Prevention, 2015a ). Although the 2012 National Health and Nutrition Examination Survey (NHANES) data showed a decrease in obesity rates for children 2 to 5 years of age, the obesity rates for 2-19-year olds between 2003-2004 and 2011-2012 remained unchanged at 31.8 percent ( Ogden et al., 2014 ). Thus, weight-based bullying can affect a substantial number of youth.

In 2007, Puhl and Latner reviewed the growing literature on social marginalization and stigmatization of obesity in children and adolescents, paying attention to the nature and extent of weight bias toward overweight youth and the primary sources of stigma in their lives, including peers. 21 The researchers found that existing studies on weight stigma suggest that experiences with various forms of bullying is a common experience for overweight and obese youth; however, determining specific prevalence rates of bias is difficult because various assessment methods are used across the literature ( Puhl and Latner, 2007 ). For example, Neumark-Sztainer and colleagues (2002) examined the prevalence of weight-based teasing among middle and high school students ( n = 4,746) and found that 63 percent of girls at or above the 95th percentile for BMI and 58 percent of boys at or above the 95th percentile for BMI experienced “weight-based teasing.” However, in a recent longitudinal study of weight-based teasing ( n = 8,210), Griffiths and colleagues (2006) found that 34 percent of girls at or above the 95th percentile for BMI and 36 percent of boys at or above the 95th percentile for BMI reported being victims of “weight-based teasing and various forms of bullying” ( Griffiths et al., 2006 ). Griffiths and colleagues (2006) found that obese boys and girls were more likely to be victims of overt bullying one year later.

Janssen and colleagues (2004) found that among 5,749 children, ages 11-16, girls with a higher BMI were more likely to be targets of bullying behavior than their average-weight peers. They found that the likelihood of these girls being targeted in verbal, physical, and relational bullying incidents only increased as BMI rose. Among boys, however, the researchers found no significant associations between BMI and physical victimization. When they looked at the older portion of the sample, they found that among 15-16-year-old boys and girls, BMI was positively associated with being the perpetrator of bullying behavior compared with BMI among average-weight children ( Puhl and Latner, 2007 ). In this sample of 15 and 16 year olds, girls still faced an increased likelihood of both being bullied and being a perpetrator of bullying ( Puhl and Latner, 2007 ).

In their review of the literature on peer victimization and pediatric obesity, Gray and colleagues (2009) summarized evidence since 1960 on stigmatization, marginalization, and peer victimization of obese children. They concluded that obesity in children and youth places them at risk for harmful physical, emotional, and psychosocial effects of bullying and similar types of peer mistreatment. They also noted that “over time, a cyclical relationship may emerge between obese individuals and victimization such that children who are victimized are less likely to be active, which in turn leads to increased weight gain and a greater likelihood of experiencing weight-based victimization” ( Gray et al., 2009 , p. 722).

In summary, although numerous studies indicate that overweight and obese youth are at increased risk of being bullied, it can be difficult to attribute weight-based bullying to a single physical attribute, given that being overweight or obese often co-exists with other factors (see also the subsection below on “Youth with Intersectional Identities”). Additional research is needed to identify the relative importance of weight as a reason for being bullied or being a perpetrator of bullying among children and youth.

Other Disparity Groups Requiring More Research

Although most research on groups that are at disproportionate risk for bullying has focused on LGBT youth, overweight/obese youth, or youth with disabilities, some recent research has begun to identify other groups that may be at heightened risk. 22 Because this research is in its early stages, the evidence is not yet compelling on whether these groups do experience disparities in perpetrating or being targeted by bullying behavior. Consequently, the committee highlights the following groups as warranting further study to establish their risk status.

Socioeconomic Status

The literature on socioeconomic status and bullying contains conflicting results. Higher socioeconomic status has been associated with higher levels of perpetration ( Barboza et al., 2009 ; Shetgiri et al., 2012 ) but so has lower socioeconomic status ( Christie-Mizell et al., 2011 ; Garner and Hinton, 2010 ; Glew et al., 2005 ; Jansen et al., 2011 , 2012 ; Nordhagen et al., 2005 ; Pereira et al., 2004 ; Schwartz et al., 1997 ). Other studies found that socioeconomic status was not associated with perpetration ( Flouri and Buchanan, 2003 ; Zimmerman et al., 2005 ).

The evidence for an association between socioeconomic status and being bullied is similarly inconsistent. Specifically, some studies found that neither economic deprivation ( Wilson et al., 2012 ), family income ( Garner and Hinton, 2010 ), nor general socioeconomic status ( Magklara et al., 2012 ) predicted greater risk of being targeted by bullying behavior. Other studies found that insufficient parental income ( Lemstra et al., 2012 ) and low social class ( Pereira et al., 2004 ) predicted increased rates of being the target in bullying incidents. These conflicting results may be due in part to different measures and conceptualizations of socioeconomic status. In addition, other environmental or social–ecological factors that are often not included in evaluative models may account for the differences in these findings. For example, Barboza and colleagues (2009) argued that perpetration emerges as a function of social climate deficits, where social supports may mediate perpetration regardless of demographic characteristics, including socioeconomic status. Thus, further research is warranted on the mediating and moderating variables in the association between socioeconomic status and either bullying perpetration or being targeted for bullying. (See Chapter 3 for a more detailed discussion of moderation.)

Immigration Status

The results to date from research on the association between immigration status and bullying involvement are inconsistent. For example, Lim and Hoot (2015) investigated the bullying involvement of third and sixth grade students who were immigrants, refugees, or native born. The majority of these students who were refugees or immigrants came from Burma, Burundi, Iraq, Somalia, Thailand, and Yemen. The refugees and immigrants did not report higher levels of being bullied than the native-born American students. However, qualitative data suggested that youth with refugee status responded as “nonpassive victims,” meaning they would try to defend themselves when physically attacked, whereas immigrants and native-born youth who were bullied responded to bullying more passively. The inconsistencies in the results may be associated with age of the respondents, total sample size, nationality of the immigrants/refugees, or other environmental or social–ecological factors ( Hong et al., 2014 ), all of which require greater attention in future studies.

Minority Religious Affiliations

Few studies have specifically investigated the bullying involvement of youth from minority religious groups. However, evidence from other areas of violence suggests that youth from religious minorities may experience higher rates of being bullied than those who identify as Christians. For instance, the percentage of hate crimes in the United States that are grounded in religious affiliation has increased from 10 percent in 2004 to 28 percent in 2012 ( Wilson, 2014 ). Since schools are reflective of society as a whole, and bullying involvement is grounded in a social–ecological context that includes community and societal factors ( Hong and Espelage, 2012 ), this targeting of religious minorities may carry over into the school environment. However, this hypothesis requires empirical documentation.

Youth with Intersectional Identities

As noted in the earlier discussion of weight status as a factor in bullying, “intersectionality” refers to individuals with multiple stigmatized statuses (e.g., black lesbian youth). The majority of studies on bullying perpetration and targeting have examined identity groups in isolation, but there is increasing acknowledgement that multiple intersecting identities can exacerbate or attenuate health outcomes (e.g., Bowleg, 2008 ; McCall, 2005 ). An exception is the study by Garnett and colleagues (2014) , which analyzed the intersectionality of weight-related bullying with bullying for other reasons. Among 965 Boston youth sampled in the 2006 Boston Youth Survey, participants had been discriminated against or bullied (or assaulted) for any of four attributes (race or ethnicity, immigration status, perceived sexual orientation, and weight). Participants who were bullied for their race and weight had higher rates of being targeted for bullying behavior, compared with students who had two or more of the other characteristics ( Garnett et al., 2014 ). As discussed earlier, the extent to which intersecting identities affect the prevalence of bullying perpetration and targeting remains largely unknown and therefore represents an important area for future study.

Children and adolescents have mostly stated that the differences in their physical appearance contribute to the possibility of their being bullied ( Lunde et al., 2007 ). There is concern that students with characteristics, such as obesity, disabilities, food allergies, and gender issues could put them directly in the path of being more likely to be bullied ( Schuster and Bogart, 2013 ). These categories may intersect at the micro level of individual experience to reflect multiple interlocking systems of privilege and oppression at the macro, social-structural level ( Bowleg, 2012 ).

Is bullying more prevalent in urban schools than in suburban or rural schools? Because large-city urban schools are often located in inner-city areas of concentrated poverty and exposure to violence, theories of social disorganization suggest that bullying might be more common in such contexts ( Bradshaw et al., 2009 ). However, there is not much research in support of this hypothesis. Rural students have self-reported at least as much bullying in their schools as did urban youth ( Dulmus et al., 2004 ; Stockdale et al., 2002 ). Moreover, data from large national studies in the United States indicate that students in rural schools report somewhat more bullying than those in urban and suburban schools ( Nansel et al., 2001 ; Robers et al., 2013 ). In particular Robers and colleagues (2013) found, using 2011 National Center for Education Statistics data, that 25 percent of students in urban schools reported some bullying, compared with 29 percent in suburban schools and 30 percent in rural schools. One reason that has been suggested for this difference is that smaller rural schools, some of which have fewer school transitions (e.g., lacking a separate middle school between elementary and high school grades), may typically consolidate social reputations and provide fewer opportunities for targeted youth to redefine how they are perceived by peers ( Farmer et al., 2011 ).

What may differ by urbanicity of schools are the reasons for targeting certain individuals in a pattern of bullying behavior. For example, Goldweber and colleagues (2013) documented that urban African American youth were more likely to report race-based bullying by peers than were rural or suburban youth. As noted above in the section on “Prevalence of Bullying by Race and Ethnicity,” the connection between experiences of peer bullying and racial discrimination merits further study.

• ISSUES IN DEVELOPING ESTIMATES OF BULLYING IN THE UNITED STATES

Current efforts to estimate prevalence of bullying and cyberbullying behavior are characterized by disagreement and confusion. This chapter has pointed out the major challenges associated with generating accurate and reliable estimates of bullying and cyberbullying rates in the United States. The issues to be addressed are summarized here in terms of definitional issues and issues of measurement and sampling.

Definitional Issues

As attention to bullying behavior has grown in recent years, concerns have been raised that efforts to characterize bullying vary considerably and that a lack of a consistent definition “hinders our ability to understand the true magnitude, scope, and impact of bullying and track trends over time” ( Gladden et al., 2014 , p. 1). One such approach to measuring bullying includes providing an explicit definition or explanation of what is meant by bullying to study participants. In contrast, some approaches simply use the word “bullying” but do not define it, whereas others list specific behaviors that constitute bullying without using the term “bullying” ( Gladden et al., 2014 ; Sawyer et al., 2008 ). Even if the definition is provided, researchers must assume that respondents (who are often children) fully understand the broad and difficult concept of bullying—including its elements of hostile intent, repetition, and power imbalance and its various forms—when answering. However, research has shown that this level of comprehension might not be uniformly present for children of all age groups and cultures ( Monks and Smith, 2006 ; Smith et al., 2002 ; Strohmeier and Toda, 2008 ; Vaillancourt et al., 2008 ). For instance, 8-year-old children consider fewer negative behavior options to be bullying than do 14-year-old adolescents ( Smith et al., 2002 ). Furthermore, children hold very different definitions of bullying from those held by researchers. Bullying may also be understood and defined differently in different languages and cultures ( Arora, 1996 ). Smith and colleagues (2002) showed that terms used in different cultures differed remarkably in their meanings. For example, some terms captured verbal aggression, while others were connected instead with physically aggressive acts or with social exclusion. These definitional issues are also relevant to cyberbullying, as there is no uniform definition used across studies.

There is still a lot of variability when it comes to defining bullying: Parents, children, and schools or medical professionals can mean a wide range of different things when they use the term “bullying.” Bullying varies in different developmental stages, and we should acknowledge that it is not always obvious. Even so, bullying can be characterized as the kind of behavior that would actually be considered harassment if the people involved were over age 18. However you look at it, a standardized definition would help us more precisely target bullying behavior and consequences while avoiding misunderstandings.

—Summary of themes from service providers/community-based providers focus group (See Appendix B for additional highlights from interviews.)

Measurement and Sampling Issues

Measuring bullying and cyberbullying is very difficult. The variability in prevalence rates reflects a number of measurement and sampling issues. First, studies reporting prevalence rates of bullying problems may rely on different data sources, such as peer versus teacher nominations or ratings, observations by researchers, or self-report questionnaires. Particularly with children, the self-report strategy poses a unique problem in regard to possible underreporting or overreporting ( Solberg, 2003 ). Some children who bully other students will choose not to respond honestly on the relevant questionnaire items for fear of retribution from adults. To date, a majority of information is gathered via self-reports, which have limitations; however, the committee does not believe that official reports are necessarily a better or more reliable source of information. The committee also acknowledges that for studies examining the prevalence of bullying by a certain demographic category, such as obesity or sexual orientation, it is not possible to say who is the “most bullied” by comparing students with one set of demographic characteristics with other students with different demographic characteristics.

Second, research suggests that the approach to measuring bullying does affect the pattern of responses and in turn may influence the prevalence rates. For example, a study of over 24,000 elementary, middle, and high school age youth found significantly higher prevalence rates for bullying when it was assessed using a behavior-based approach (i.e., asking about the experience of specific forms and acts of bullying) than when it was measured using a definition-based approach ( Sawyer et al., 2008 ). A similar pattern occurs for cyberbullying, For example, one study used a definition that read “repeatedly [trying] to hurt you or make you feel bad by e-mailing/e-messaging you or posting a blog about you on the Internet (MySpace).” This study found the prevalence of cybervictimization to be 9 percent ( Selkie et al., 2015 ). Another study asked about “the use of the Internet, cell phones and other technologies to bully, harass, threaten or embarrass someone” and found cybervictimization prevalence to be 31 percent ( Pergolizzi et al., 2011 ).

Third, studies may differ with regard to the reference period used in measuring bullying. For example, a question may refer to a whole school year or one school term, the past couple of months, or over a lifetime. Response and rating categories may vary in both number and specificity as well. Such categories may consist of a simple yes or no dichotomy; of various applicability categories such as “does not apply at all” and “applies perfectly”; or of relatively vague frequency alternatives ranging from “seldom” to “very often” or from “not at all in the past couple of months” to “several times a week.”

Fourth, some studies use different criteria for differentiating students who have been bullied and students who have not, as well as students who have and have not bullied others. This variation in identification makes prevalence rates difficult to compare ( Solberg, 2003 ). A majority of studies do not ask questions about children who have bullied or children who have been bystanders, instead focusing on children who have been bullied. Taken together, these findings suggest that researchers need to be cautious about interpreting their findings in light of their measurement approach.

Estimates of bullying inform an evidence-based understanding about the extent of the problem and bring attention to the need to address the problem and allocate the funding to do so. Prevalence estimates provide information for policy makers, identify where education is needed, identify vulnerable populations, and help direct assistance and resources. As this chapter has explained, generating reliable estimates for the number of children who have bullied and the number who have been bullied is not an easy task. In some cases, the task is extraordinarily difficult. For example, existing research suggests disparities in rates of bullying by a variety of characteristics, including sexual orientation, disability, and obesity, mostly due to the lack of nationally representative data on these and other vulnerable groups. Bullying must be understood as a social problem characterized by numerous challenges to estimating its prevalence and the conditions associated with it. In summary, based on its review of the available evidence, the committee maintains that, despite the current imperfect estimates, bullying and cyberbullying in the United States is clearly prevalent and therefore worthy of attention.

• FINDINGS AND CONCLUSIONS

Finding 2.1: Estimates of bullying and cyberbullying prevalence reported by national surveys vary greatly, ranging from 17.9 percent to 30.9 percent of school-age children for the prevalence of bullying behavior at school and from 6.9 percent to 14.8 percent for the prevalence of cyberbullying. The prevalence of bullying among some groups of youth is even higher. For instance, the prevalence of bullying of lesbian, gay, bisexual, and transgender youth is approximately double that of heterosexual and cisgender youth. Finding 2.2: The extent to which rates of bullying and cyberbullying have changed in recent years is unclear. Finding 2.3: The four major national surveys that include bullying do not uniformly address all age groups and school levels. Finding 2.4: A majority of prevalence data collection is done through self-reports or observation. Finding 2.5: A majority of national studies do not ask questions about children who have bullied or children who have been bystanders. Finding 2.6: Many studies differ with regard to the reference period used in measuring bullying behavior (e.g., last month versus last 12 months). Finding 2.7: Studies use different definitional criteria for differentiating students who have been bullied and cyberbullied and students who have not, as well as students who bully and cyberbully and students who do not. Finding 2.8: Existing research suggests that there are disparities in rates of bullying by a variety of characteristics, including sexual orientation, disability, and obesity. However, there is a lack of nationally representative data on these and other vulnerable groups. Future research is therefore needed to generate representative estimates of bullying, including bias-based and discriminatory bullying, to accurately identify disparity groups.

Conclusions

Conclusion 2.1: Definitional and measurement inconsistencies lead to a variation in estimates of bullying prevalence, especially across disparate samples of youth. Although there is a variation in numbers, the national surveys show bullying behavior is a real problem that affects a large number of youth. Conclusion 2.2: The national datasets on the prevalence of bullying focus predominantly on the children who are bullied. Considerably less is known about perpetrators, and nothing is known about bystanders in that national data. Conclusion 2.3: Cyberbullying should be considered within the context of bullying rather than as a separate entity. The Centers for Disease Control and Prevention definition should be evaluated for its application to cyberbullying. Although cyberbullying may already be included, it is not perceived that way by the public or by the youth population. Conclusion 2.4: Different types of bullying behaviors—physical, relational, cyber—may emerge or be more salient at different stages of the developmental life course. Conclusion 2.5: The online context where cyberbullying takes place is nearly universally accessed by adolescents. Social media sites are used by the majority of teens and are an influential and immersive medium in which cyberbullying occurs.

• Arora CM. Defining bullying towards a clearer general understanding and more effective intervention strategies. School Psychology International. 1996; 17 (4):317–329.
• Barboza GE, Schiamberg LB, Oehmke J, Korzeniewski SJ, Post LA, Heraux CG. Individual characteristics and the multiple contexts of adolescent bullying: An ecological perspective. Journal of Youth and Adolescence. 2009; 38 (1):101–121. [ PubMed : 19636795 ]
• Berlan ED, Corliss HL, Field AE, Goodman E, Austin SB. Sexual orientation and bullying among adolescents in the Growing Up Today study. Journal of Adolescent Health. 2010; 46 (4):366–371. [ PMC free article : PMC2844864 ] [ PubMed : 20307826 ]
• Blake JJ, Lund EM, Zhou Q, Kwok OM, Benz MR. National prevalence rates of bully victimization among students with disabilities in the United States. School Psychology Quarterly. 2012; 27 (4):210. [ PubMed : 23294235 ]
• Bowleg L. When black + lesbian + woman ≠ black lesbian woman: The methodological challenges of qualitative and quantitative intersectionality research. Sex Roles. 2008; 59 (5-6):312–325.
• Bowleg L. The problem with the phrase women and minorities: Intersectionality—an important theoretical framework for public health. American Journal of Public Health. 2012; 102 (7):1267–1273. [ PMC free article : PMC3477987 ] [ PubMed : 22594719 ]
• Bradshaw CP, Sawyer AL, O'Brennan LM. A social disorganization perspective on bullying-related attitudes and behaviors: The influence of school context. American Journal of Community Psychology. 2009; 43 (3-4):204–220. [ PubMed : 19333749 ]
• Card NA, Stucky BD, Sawalani GM, Little TD. Direct and indirect aggression during childhood and adolescence: A meta-analytic review of gender differences, intercorrelations, and relations to maladjustment. Child Development. 2008; 79 (5):1185–1229. [ PubMed : 18826521 ]
• Centers for Disease Control and Prevention. Youth Risk Behavior Surveillance—United States, 2009. Morbidity and Mortality Weekly Report. 2010; 59 (SS-5):1–142. [ PubMed : 20520591 ]
• Centers for Disease Control and Prevention. Youth Risk Behavior Surveillance—United States, 2009. Morbidity and Mortality Weekly Report. 2012; 61 (4):1–162. [ PubMed : 22673000 ]
• Centers for Disease Control and Prevention. LGBTQ Youth Programs at a Glance. 2014a. [December 2015]. http://www ​.cdc.gov/lgbthealth ​/youth-programs.htm .
• Centers for Disease Control and Prevention. Youth Risk Behavior Surveillance—United States. Morbidity and Mortality Weekly Report. 2014b; 63 (4):1–169. [ PubMed : 24918634 ]
• Centers for Disease Control and Prevention. Childhood Obesity Facts. 2015a. [November 2015]. http://www ​.cdc.gov/healthyschools ​/obesity/facts.htm .
• Centers for Disease Control and Prevention. Defining Childhood Obesity. 2015b. [November 2015]. http://www ​.cdc.gov/obesity ​/childhood/defining.html .
• Christie-Mizell CA, Keil JM, Laske MT, Stewart J. Bullying behavior, parents' work hours, and early adolescents' perceptions of time spent with parents. Youth & Society. 2011; 43 (4):1570–1595.
• Conron KJ, Scout N, Austin SB. “Everyone has a right to, like, check their box”: Findings on a measure of gender identity from a cognitive testing study with adolescents. Journal of LGBT Health Research. 2008; 4 (1):1–9. [ PubMed : 19860014 ] [ CrossRef ]
• Cook CR, Williams KR, Guerra NG, Kim T. Handbook of Bullying in Schools: An International Perspective. Jimerson SR, Swearer SM, Espelage DL, editors. New York: Routledge; 2010. pp. 347–362. (Variability in the prevalence of bullying and victimization).
• Cowie H, Bauman S, Coyne I, Myers C, Pörhölä M, Almeida A. Cyberbullying Through the New Media: Findings from an International Network. Smither PK, Steffgen G, editors. London, UK: Psychology Press; 2013. pp. 165–177. (Cyberbullying amongst university students).
• DeVoe JF, Bauer L. Student Victimization in U.S. Schools: Results from the 2007 School Crime Supplement to the National Crime Victimization Survey. Washington, DC: U.S. Government Printing Office; 2010. (NCES 2010-319).
• DeVoe JF, Bauer L. Student Victimization in U.S. Schools: Results from the 2009 School Crime Supplement to the National Crime Victimization Survey. Washington, DC: U.S. Government Printing Office; 2011.
• Dulmus CN, Theriot MT, Sowers KM, Blackburn JA. Student reports of peer bullying victimization in a rural school. Stress, Trauma, and Crisis. 2004; 7 (1):1–16.
• Eisenberg ME, Aalsma MC. Bullying and peer victimization: Position paper of the Society for Adolescent Medicine. Journal of Adolescent Health. 2005; 36 (1):88–91. [ PubMed : 15661606 ]
• Espelage DL, Aragon SR, Birkett M, Koenig BW. Homophobic teasing, psychological outcomes, and sexual orientation among high school students: What influence do parents and schools have? School Psychology Review. 2008; 37 (2):202–216.
• Farmer TW, Hamm JV, Leung MC, Lambert K, Gravelle M. Early adolescent peer ecologies in rural communities: Bullying in schools that do and do not have a transition during the middle grades. Journal of Youth and Adolescence. 2011; 40 (9):1106–1117. [ PubMed : 21667294 ]
• Farmer TW, Petrin R, Brooks DS, Hamm JV, Lambert K, Gravelle M. Bullying involvement and the school adjustment of rural students with and without disabilities. Journal of Emotional and Behavioral Disorders. 2012; 20 (1):19–37.
• Felix ED, Green JG. Handbook of Bullying in Schools. An International Perspective. Jimerson SR, Swearer SM, Espelage DL, editors. New York: Routledge; 2010. pp. 173–185. (Popular girls and brawny boys).
• Finkelhor D, Turner H, Ormrod R, Hamby S, Kracke K. Children's Exposure to Violence: A Comprehensive Survey. U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention; 2012. [June 2016]. https://www ​.ncjrs.gov ​/pdffiles1/ojjdp/grants/248444.pdf .
• Finkelhor D, Turner HA, Shattuck A, Hamby SL. Violence, Crime, and Abuse Exposure in a National Sample of Children and Youth: An Update. U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention; 2015. [June 2016]. http://www ​.ojjdp.gov/pubs/248547.pdf .
• Flouri E, Buchanan A. The role of mother involvement and father involvement in adolescent bullying behavior. Journal of Interpersonal Violence. 2003; 18 (6):634–644.
• Garner PW, Hinton TS. Emotional display rules and emotion self-regulation: Associations with bullying and victimization in community-based after school programs. Journal of Community & Applied Social Psychology. 2010; 20 (6):480–496.
• Garnett BR, Masyn KE, Austin SB, Miller M, Williams DR, Viswanath K. The intersectionality of discrimination attributes and bullying among youth: An applied latent class analysis. Journal of Youth and Adolescence. 2014; 43 (8):1225–1239. [ PubMed : 24318776 ]
• Garofalo R, Wolf RC, Kessel S, Palfrey J, DuRant RH. The association between health risk behaviors and sexual orientation among a school-based sample of adolescents. Pediatrics. 1998; 101 (5):895–902. [ PubMed : 9565422 ]
• GenIUSS Group. Gender-Related Measures Overview. The Williams Institute; 2013. [April 2016]. http: ​//williamsinstitute ​.law.ucla.edu/wp-content ​/uploads/GenIUSS-Gender-relatedQuestion-Overview.pdf .
• Gladden RM, Vivolo-Kantor AM, Hamburger ME, Lumpkin CD. Bullying Surveillance among Youths: Uniform Definitions for Public Health and Recommended Data Elements, Version 1.0. Atlanta, GA: Centers for Disease Control and Prevention and U.S. Department of Education; 2014.
• Glew GM, Fan MY, Katon W, Rivara FP, Kernic MA. Bullying, psychosocial adjustment, and academic performance in elementary school. Archives of Pediatrics & Adolescent Medicine. 2005; 159 (11):1026–1031. [ PubMed : 16275791 ]
• Goldweber A, Waasdorp TE, Bradshaw CP. Examining associations between race, urbanicity, and patterns of bullying involvement. Journal of Youth and Adolescence. 2013; 42 (2):206–219. [ PubMed : 23095907 ]
• Grant JM, Mottet L, Tanis JE, Harrison J, Herman J, Keisling M. Injustice at Every Turn: A Report of the National Transgender Discrimination Survey. National Center for Transgender Equality; 2011. [October 2015]. http://www ​.thetaskforce ​.org/static_html/downloads ​/reports/reports/ntds_full.pdf .
• Gray WN, Kahhan NA, Janicke DM. Peer victimization and pediatric obesity: A review of the literature. Psychology in the Schools. 2009; 46 (8):720–727.
• Griffiths LJ, Wolke D, Page AS, Horwood J. Obesity and bullying: Different effects for boys and girls. Archives of Disease in Childhood. 2006; 91 (2):121–125. [ PMC free article : PMC2082670 ] [ PubMed : 16174642 ]
• Hanlon BM. 2003 Massachusetts Youth Risk Behavior Survey Results. Malden: Massachusetts Department of Education; 2004.
• Hong JS, Espelage DL. A review of research on bullying and peer victimization in school: An ecological system analysis. Aggression and Violent Behavior. 2012; 17 (4):311–322.
• Hong JS, Peguero AA, Choi S, Lanesskog D, Espelage DL, Lee NY. Social ecology of bullying and peer victimization of Latino and Asian youth in the United States: A review of the literature. Journal of School Violence. 2014; 13 (3):315–338.
• Iannotti RJ. Health Behavior in School-Aged Children (HBSC), 2005-2006. Ann Arbor, MI: Inter-university Consortium for Political and Social Research; 2012.
• Iannotti RJ. Health Behavior in School-Aged Children (HBSC), 2009-2010. Ann Arbor, MI: Inter-university Consortium for Political and Social Research; 2013.
• Jansen DE, Veenstra R, Ormel J, Verhulst FC, Reijneveld SA. Early risk factors for being a bully, victim, or bully/victim in late elementary and early secondary education. The Longitudinal Trails Study. BMC Public Health. 2011; 11 (1):1–7. [ PMC free article : PMC3128024 ] [ PubMed : 21645403 ]
• Jansen PW, Verlinden M, Dommisse-van Berkel A, Mieloo C, van der Ende J, Veenstra R, Verhulst FC, Jansen W, Tiemeier H. Prevalence of bullying and victimization among children in early elementary school: Do family and school neighbourhood socioeconomic status matter? BMC Public Health. 2012; 12 (1):1–10. [ PMC free article : PMC3575320 ] [ PubMed : 22747880 ]
• Janssen I, Craig WM, Boyce WF, Pickett W. Associations between overweight and obesity with bullying behaviors in school-aged children. Pediatrics. 2004; 113 (5):1187–1194. [ PubMed : 15121928 ]
• Juvonen J, Gross EF. Extending the school grounds?—Bullying experiences in cyberspace. Journal of School Health. 2008; 78 (9):496–505. [ PubMed : 18786042 ]
• Kosciw JG, Greytak EA, Bartkiewicz MJ, Boesen MJ, Palmer NA. The 2011 National School Climate Survey: The Experiences of Lesbian, Gay, Bisexual, and Transgender Youth in Our Nation's School. Washington, DC: Education Resources Information Center, Institute of Education Sciences, U.S. Department of Education; 2012.
• Kowalski RM, Limber SP, Agatston PW. Cyberbullying: Bullying in the Digital Age. West Sussex, UK: Wiley-Blackwell; 2012.
• Kraft E, Wang J. An exploratory study of the cyberbullying and cyberstalking experiences and factors related to victimization of students at a public liberal arts college. International Journal of Technoethics. 2010; 1 (4):74–91.
• Larochette AC, Murphy AN, Craig WM. Racial bullying and victimization in Canadian school-age children: Individual and school level effects. School Psychology International. 2010; 31 (4):389–408.
• Lemstra ME, Nielsen G, Rogers MR, Thompson AT, Moraros JS. Risk indicators and outcomes associated with bullying in youth aged 9-15 years. Canadian Journal of Public Health/Revue Canadienne de Santé Publique. 2012; 103 (1):9–13. [ PMC free article : PMC6973964 ] [ PubMed : 22338321 ]
• Lenhart A, Duggan M, Perrin A, Stepler R, Rainie H, Parker K. Teens, Social Media & Technology Overview 2015. 2015. [September 2015]. http://www ​.pewinternet ​.org/files/2015/04 ​/PI_TeensandTech_Update2015_0409151.pdf .
• Lim SJJ, Hoot JL. Bullying in an increasingly diverse school population: A socioecological model analysis. School Psychology International. 2015
• Limber SB. Bullying Among Children and Youth. Department of Psychology and Institute on Family and Neighborhood Life, Clemson University, SC; 2014. (Unpublished manuscript of a study commissioned by the Planning Committee on Increasing Capacity for Reducing Bullying and Its Impact on the Lifecourse of Youth Involved).
• Limber SB, Olweus D, Luxenberg H. Bullying in U.S. Schools 2012 Status Report. Center City, MN: Hazelden Foundation; 2013.
• Lunde C, Frisén A, Hwang CP. Ten-year-old girls' and boys' body composition and peer victimization experiences: Prospective associations with body satisfaction. Body Image. 2007; 4 (1):11–28. [ PubMed : 18089248 ]
• Magklara K, Skapinakis P, Gkatsa T, Bellos S, Araya R, Stylianidis S, Mavreas V. Bullying behaviour in schools, socioeconomic position, and psychiatric morbidity: A cross-sectional study in late adolescents in Greece. Child and Adolescent Psychiatry and Mental Health. 2012; 6 (8):2–13. [ PMC free article : PMC3298787 ] [ PubMed : 22325708 ]
• McCall L. The complexity of intersectionality. Signs: Journal of Women in Culture and Society. 2005; 30 (3):1771–1800.
• McGuire JK, Anderson CR, Toomey RB, Russell ST. School climate for transgender youth: A mixed-method investigation of student experiences and school responses. Journal of Youth and Adolescence. 2010; 39 (10):1175–1188. [ PubMed : 20428933 ]
• McLaughlin C, Byers R, Vaughn R. Responding to Bullying among Children with Special Educational Needs and/or Disabilities. London, UK: Anti-Bullying Alliance; 2010.
• Monks CP, Smith PK. Definitions of bullying: Age differences in understanding of the term and the role of experience. British Journal of Developmental Psychology. 2006; 24 (4):801–821.
• Moreno MA, Kota R, Schoohs S, Whitehill JM. The Facebook influence model: A concept mapping approach. Cyberpsychology, Behavior, and Social Networking. 2013; 16 (7):504–511. [ PMC free article : PMC3715786 ] [ PubMed : 23621717 ]
• Nansel TR, Overpeck M, Pilla RS, Ruan WJ, Simons-Morton B, Scheidt P. Bullying behaviors among U.S. youth: Prevalence and association with psychosocial adjustment. Journal of the American Medical Association. 2001; 285 (16):2094–2100. [ PMC free article : PMC2435211 ] [ PubMed : 11311098 ]
• Neumark-Sztainer D, Falner N, Story M, Perry C, Hannan PJ, Mulert S. Weight-teasing among adolescents: Correlations with weight status and disordered eating behaviors. International Journal of Obesity and Related Metabolic Disorders. 2002; 26 (1):123–131. [ PubMed : 11791157 ]
• Nordhagen R, Nielsen A, Stigum H, Köhler L. Parental reported bullying among Nordic children: A population-based study. Child: Care, Health and Development. 2005; 31 (6):693–701. [ PubMed : 16207227 ]
• Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. Journal of the American Medical Association. 2014; 311 (8):806–814. [ PMC free article : PMC4770258 ] [ PubMed : 24570244 ]
• Olsen EOM, Kann L, Vivolo-Kantor A, Kinchen S, McManus T. School violence and bullying among sexual minority high school students, 2009-2011. Journal of Adolescent Health. 2014; 55 (3):432–438. [ PubMed : 24768163 ]
• Olweus D. School bullying: Development and some important challenges. Annual Review of Clinical Psychology. 2013; 9 :751–780. [ PubMed : 23297789 ]
• Oppliger PA. Bullies and Mean Girls in Popular Culture. Jefferson, NC: McFarland & Company; 2013.
• Pereira B, Mendonca D, Neto C, Valente L, Smith PK. Bullying in Portuguese schools. School Psychology International. 2004; 25 (2):241–254.
• Pergolizzi F, Pergolizzi J, Gan Z, Macario S, Pergolizzi JV, Ewin T, Gan TJ. Bullying in middle school: Results from a 2008 survey. International Journal of Adolescent Medicine and Health. 2011; 23 (1):11–18. [ PubMed : 21721358 ]
• Peskin M, Tortolero SR, Markham CM. Bullying and victimization among black and Hispanic adolescents. Adolescence. 2006; 41 :467, 484. [ PubMed : 17225662 ]
• Puhl RM, Latner JD. Stigma, obesity, and the health of the nation's children. Psychological Bulletin. 2007; 133 (4):557. [ PubMed : 17592956 ]
• Reisner SL, Greytak EA, Parsons JT, Ybarra ML. Gender minority social stress in adolescence: Disparities in adolescent bullying and substance use by gender identity. Journal of Sex Research. 2015; 52 (3):243–256. [ PMC free article : PMC4201643 ] [ PubMed : 24742006 ]
• Rice E, Petering R, Rhoades H, Winetrobe H, Goldbach J, Plant A, Montoya J, Kordic T. Cyberbullying perpetration and victimization among middle school students. American Journal of Public Health. 2015; 105 (3):e66–e72. [ PMC free article : PMC4330864 ] [ PubMed : 25602905 ]
• Rivers I. The bullying of sexual minorities at school: Its nature and long-term correlates. Educational and Child Psychology. 2001; 18 (1):32–46.
• Robers S, Kemp J, Truman J. Indicators of School Crime and Safety: 2012. Washington, DC: National Center for Education Statistics, U.S. Department of Education; 2013. (NCES 2013-036/NCJ 241446).
• Rose CA. Bullying and Vulnerable Populations: Exploring Predictive and Protective Factors Associated with Disproportionate Representation. Department of Special Education, University of Missouri; Columbia: 2015. (Unpublished manuscript of a study commissioned by the Committee on the Biological and Psychosocial Effects of Peer Victimization: Lessons for Bullying Prevention).
• Rose CA, Espelage DL, Monda-Amaya LE. Bullying and victimisation rates among students in general and special education: A comparative analysis. Educational Psychology. 2009; 29 (7):761–776.
• Rose CA, Monda-Amaya LE, Espelage DL. Bullying perpetration and victimization in special education: A review of the literature. Remedial and Special Education. 2010; 32 :114–130.
• Rose CA, Simpson CG, Moss A. The bullying dynamic: Prevalence of involvement among a large-scale sample of middle and high school youth with and without disabilities. Psychology in the Schools. 2015; 52 (5):515–531.
• Russell ST, Driscoll AK, Truong N. Adolescent same-sex romantic attractions and relationships: Implications for substance use and abuse. American Journal of Public Health. 2002; 92 (2):198–202. [ PMC free article : PMC1447042 ] [ PubMed : 11818291 ]
• Russell ST, Everett BG, Rosario M, Birkett M. Indicators of victimization and sexual orientation among adolescents: Analyses from Youth Risk Behavior Surveys. American Journal of Public Health. 2014; 104 (2):255–261. [ PMC free article : PMC3935664 ] [ PubMed : 24328633 ]
• Sawyer AL, Bradshaw CP, O'Brennan LM. Examining ethnic, gender, and developmental differences in the way children report being a victim of “bullying” on self-report measures. Journal of Adolescent Health. 2008; 43 :106–114. [ PubMed : 18639783 ] [ CrossRef ]
• Schenk AM, Fremouw WJ. Prevalence, psychological impact, and coping of cyberbully victims among college students. Journal of School Violence. 2012; 11 (1):21–37.
• Schuster MA, Bogart LM. Did the ugly duckling have PTSD? Bullying, its effects, and the role of pediatricians. Pediatrics. 2013; 131 (1):e288–e291. [ PMC free article : PMC3529957 ] [ PubMed : 23266921 ]
• Schwartz D, Dodge KA, Pettit GS, Bates JE. The early socialization of aggressive victims of bullying. Child Development. 1997; 68 (4):665–675. [ PubMed : 9306645 ]
• Selkie EM, Fales JL, Moreno MA. Cyberbullying prevalence among U.S. middle and high school age adolescents: A systematic review and quality assessment. Journal of Adolescent Health. 2015; 58 (2):125–133. [ PMC free article : PMC4724486 ] [ PubMed : 26576821 ]
• Shetgiri R, Lin H, Flores G. Identifying children at risk for being bullies in the United States. Academic Pediatrics. 2012; 12 (6):509–522. [ PMC free article : PMC3501605 ] [ PubMed : 22989731 ]
• Smith PK, Cowie H, Olafsson RF, Liefooghe AP, Almeida A, Araki H, del Barrio C, Costabile A, Dekleva B, Houndoumadi A. Definitions of bullying: A comparison of terms used, and age and gender differences, in a fourteen-country international comparison. Child Development. 2002; 73 (4):1119–1133. [ PubMed : 12146737 ]
• Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N. Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry. 2008; 49 (4):376–385. [ PubMed : 18363945 ]
• Solberg ME. Prevalence estimation of school bullying with the Olweus bully/victim questionnaire. Aggressive Behavior. 2003; 29 (3):239–268.
• Son E, Parish SL, Peterson NA. National prevalence of peer victimization among young children with disabilities in the United States. Children and Youth Services Review. 2012; 34 (8):1540–1545.
• Spriggs AL, Iannotti RJ, Nansel TR, Haynie DL. Adolescent bullying involvement and perceived family, peer, and school relations: Commonalities and differences across race/ethnicity. Journal of Adolescent Health. 2007; 41 (3):283–293. [ PMC free article : PMC1989108 ] [ PubMed : 17707299 ]
• Stockdale MS, Hangaduambo S, Duys D, Larson K, Sarvela PD. Rural elementary students', parents', and teachers' perceptions of bullying. American Journal of Health Behavior. 2002; 26 (4):266–277. [ PubMed : 12081359 ]
• Strohmeier D, Toda Y. Cross-National Similarities and Differences. The Extent to Which Bullying and Victimisation Are Similar, or Show Differences, Across Cultures, Especially Western and Eastern Cultures. Wurzburg, Germany: Jul, 2008. (Paper presented at the preconference “Victimisation in Children and Youth,” 20th Biennial Meeting of the International Society for the Study of Behavioural Development).
• Twyman KA, Saylor CF, Saia D, Macias MM, Taylor LA, Spratt E. Bullying and ostracism experiences in children with special health care needs. Journal of Developmental & Behavioral Pediatrics. 2010; 31 (1):1–8. [ PubMed : 20081430 ]
• U.S. Department of Education. Student Reports of Bullying and Cyber-Bullying: Results from the 2011 School Crime Supplement to the National Crime Victimization Survey. Washington DC: Author; 2013.
• U.S. Department of Education. Student Reports of Bullying and Cyber-Bullying: Results from the 2013 School Crime Supplement to the National Crime Victimization Survey. Washington DC: Author; 2015. [April 2015]. http://nces ​.ed.gov/pubs2015/2015056.pdf .
• U.S. Department of Health and Human Services; Health Resources and Services Administration. Maternal and Child Health Bureau. Health Behavior in School-Aged Children (HBSC), 2001-2002. Ann Arbor, MI: Inter-university Consortium for Political and Social Research; 2008.
• U.S. Government Accountability Office. School Bullying-Extent of Legal Protections for Vulnerable Groups Needs to Be More Fully Assessed. Washington, DC: Author; 2012.
• Vaillancourt T, McDougall P, Hymel S, Krygsman A, Miller J, Stiver K, Davis C. Bullying: Are researchers and children/youth talking about the same thing? International Journal of Behavioral Development. 2008; 32 (6):486–495.
• Wensley K, Campbell M. Heterosexual and nonheterosexual young university students' involvement in traditional and cyber forms of bullying. Cyberpsychology, Behavior, and Social Networking. 2012; 15 (12):649–654. [ PubMed : 23078337 ]
• Wilson ML, Bovet P, Viswanathan B, Suris JC. Bullying among adolescents in a sub-Saharan middle-income setting. Journal of Adolescent Health. 2012; 51 (1):96–98. [ PubMed : 22727084 ]
• Wilson MM. Hate Crime Victimization 2004-2012 Statistics. Washington, DC: U.S. Department of Justice; 2014. [November 2015]. http://www ​.bjs.gov/content ​/pub/pdf/hcv0412st.pdf .
• Wolak J, Mitchell KJ, Finkelhor D. Does online harassment constitute bullying? An exploration of online harassment by known peers and online-only contacts. Journal of Adolescent Health. 2007; 41 (6):S51–S58. [ PubMed : 18047945 ]
• World Health Organization. Health Behavior in School-Aged Children (HBSC), 1997-1998. Ann Arbor, MI: Inter-university Consortium for Political and Social Research; 2003.
• Zimmerman FJ, Glew GM, Christakis DA, Katon W. Early cognitive stimulation, emotional support, and television watching as predictors of subsequent bullying among grade-school children. Archives of Pediatrics & Adolescent Medicine. 2005; 159 (4):384–388. [ PubMed : 15809395 ]

Additional information about strategies for overcoming these limitations can be found in Chapter 7 .

Households are selected through a stratified, multistage, cluster sampling process. Households in the sample are designed to be representative of all households as well as noninstitutionalized individuals ages 12 or older.

For the SCS, being “bullied” includes students being made fun of, called names, or insulted; being the subject of rumors; being threatened with harm; being pushed, shoved, tripped, or spit on; being pressured into doing things they did not want to do; being excluded from activities on purpose; and having property destroyed on purpose. “At school” includes the school building, school property, school bus, or going to and from school. Missing data are not shown for household income.

In 1995 and 1999, “at school” was defined for respondents as in the school building, on the school grounds, or on a school bus. In 2001, the definition for “at school” was changed to mean in the school building, on school property, on a school bus, or going to and from school.

The NCVS has a nationally representative sample of about 90,000 households comprising nearly 160,000 persons, whereas the sample size of the SCS is just 4,942 students.

The YRBS uses a cluster sampling design to produce a nationally representative sample of the students in grades 9-12 of all public and private school students in the 50 states and the District of Columbia.

The 2014 YRBS does not clarify whether this includes school events held off campus or the children's journey to and from school.

Electronically bullied includes being bullied through e-mail, chat rooms, instant messaging, Websites, or texting.

Each state-based and local-school-based YRBS employs a two-stage, cluster sample design to produce representative samples of students in grades 9-12 in the survey's jurisdiction.

States and cities could modify the national YRBS questionnaire for their own surveys to meet their needs.

The student survey was administered in a regular classroom setting to participating students by a school representative (e.g., teacher, nurse, guidance counselor, etc.).

Three versions of the self-report questionnaire were administered: one for fifth and sixth graders; one for students in seventh, eighth, and ninth grade; and one for students in tenth grade. The tenth grade questionnaire contained the complete set of questions asked.

This is the highest prevalence rate for both bullying and cyberbullying reports among the four national surveys.

For NatSCEV II, data were collected by telephone interview on 4,503 children and youth ages 1 month to 17 years. If the respondent was between the ages of 10-17, the main telephone interview was conducted with the child. If the respondent was younger than age 10, the interview was conducted with the child's primary caregiver.

The statistical standard for referring to “trends” is at least three data points in the same direction. In the SCS, the decrease from 2011 to 2013 is one data point, and conclusions should not be drawn at this point in time.

The committee's Statement of Task (see Box 1-1 ) requested “a particular focus on children who are most at risk of peer victimization—i.e., those with high-risk factors in combination with few protective factors . . .” At-risk subpopulations specifically named in the Statement of Task were “children with disabilities,” poly-victims, LGBT youth, and children living in poverty . . .”

The committee expands on this topic in Chapter 3 .

Reisner and colleagues (2015, p. 1) define cisgender youth as youth “whose gender identity or expression matches one's sex assigned at birth.”

The National YRBS data available at the time of publication did not include questions about sexual identity and sex of sexual contacts, but these topics are included in the YRBS report released in June 2016.

This section is adapted from a study ( Rose, 2015 ) commissioned by the committee for this report.

In this review, weight stigma included “verbal teasing (e.g., name calling, derogatory remarks, being made fun of), physical bullying (e.g., hitting, kicking, pushing, shoving), and relational victimization (e.g., social exclusion, being ignored or avoided, the target of rumors”) ( Puhl and Latner, 2007 , p. 558).

• Cite this Page Committee on the Biological and Psychosocial Effects of Peer Victimization: Lessons for Bullying Prevention; Board on Children, Youth, and Families; Committee on Law and Justice; Division of Behavioral and Social Sciences and Education; Health and Medicine Division; National Academies of Sciences, Engineering, and Medicine; Rivara F, Le Menestrel S, editors. Preventing Bullying Through Science, Policy, and Practice. Washington (DC): National Academies Press (US); 2016 Sep 14. 2, The Scope of the Problem.
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Modular, scalable hardware architecture for a quantum computer

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Quantum computers hold the promise of being able to quickly solve extremely complex problems that might take the world’s most powerful supercomputer decades to crack.

But achieving that performance involves building a system with millions of interconnected building blocks called qubits. Making and controlling so many qubits in a hardware architecture is an enormous challenge that scientists around the world are striving to meet.

Toward this goal, researchers at MIT and MITRE have demonstrated a scalable, modular hardware platform that integrates thousands of interconnected qubits onto a customized integrated circuit. This “quantum-system-on-chip” (QSoC) architecture enables the researchers to precisely tune and control a dense array of qubits. Multiple chips could be connected using optical networking to create a large-scale quantum communication network.

By tuning qubits across 11 frequency channels, this QSoC architecture allows for a new proposed protocol of “entanglement multiplexing” for large-scale quantum computing.

The team spent years perfecting an intricate process for manufacturing two-dimensional arrays of atom-sized qubit microchiplets and transferring thousands of them onto a carefully prepared complementary metal-oxide semiconductor (CMOS) chip. This transfer can be performed in a single step.

“We will need a large number of qubits, and great control over them, to really leverage the power of a quantum system and make it useful. We are proposing a brand new architecture and a fabrication technology that can support the scalability requirements of a hardware system for a quantum computer,” says Linsen Li, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this architecture.

Li’s co-authors include Ruonan Han, an associate professor in EECS, leader of the Terahertz Integrated Electronics Group, and member of the Research Laboratory of Electronics (RLE); senior author Dirk Englund, professor of EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE; as well as others at MIT, Cornell University, the Delft Institute of Technology, the U.S. Army Research Laboratory, and the MITRE Corporation. The paper appears today in Nature .

Diamond microchiplets

While there are many types of qubits, the researchers chose to use diamond color centers because of their scalability advantages. They previously used such qubits to produce integrated quantum chips with photonic circuitry.

Qubits made from diamond color centers are “artificial atoms” that carry quantum information. Because diamond color centers are solid-state systems, the qubit manufacturing is compatible with modern semiconductor fabrication processes. They are also compact and have relatively long coherence times, which refers to the amount of time a qubit’s state remains stable, due to the clean environment provided by the diamond material.

In addition, diamond color centers have photonic interfaces which allows them to be remotely entangled, or connected, with other qubits that aren’t adjacent to them.

“The conventional assumption in the field is that the inhomogeneity of the diamond color center is a drawback compared to identical quantum memory like ions and neutral atoms. However, we turn this challenge into an advantage by embracing the diversity of the artificial atoms: Each atom has its own spectral frequency. This allows us to communicate with individual atoms by voltage tuning them into resonance with a laser, much like tuning the dial on a tiny radio,” says Englund.

This is especially difficult because the researchers must achieve this at a large scale to compensate for the qubit inhomogeneity in a large system.

To communicate across qubits, they need to have multiple such “quantum radios” dialed into the same channel. Achieving this condition becomes near-certain when scaling to thousands of qubits. To this end, the researchers surmounted that challenge by integrating a large array of diamond color center qubits onto a CMOS chip which provides the control dials. The chip can be incorporated with built-in digital logic that rapidly and automatically reconfigures the voltages, enabling the qubits to reach full connectivity.

“This compensates for the in-homogenous nature of the system. With the CMOS platform, we can quickly and dynamically tune all the qubit frequencies,” Li explains.

Lock-and-release fabrication

To build this QSoC, the researchers developed a fabrication process to transfer diamond color center “microchiplets” onto a CMOS backplane at a large scale.

They started by fabricating an array of diamond color center microchiplets from a solid block of diamond. They also designed and fabricated nanoscale optical antennas that enable more efficient collection of the photons emitted by these color center qubits in free space.

Then, they designed and mapped out the chip from the semiconductor foundry. Working in the MIT.nano cleanroom, they post-processed a CMOS chip to add microscale sockets that match up with the diamond microchiplet array.

They built an in-house transfer setup in the lab and applied a lock-and-release process to integrate the two layers by locking the diamond microchiplets into the sockets on the CMOS chip. Since the diamond microchiplets are weakly bonded to the diamond surface, when they release the bulk diamond horizontally, the microchiplets stay in the sockets.

“Because we can control the fabrication of both the diamond and the CMOS chip, we can make a complementary pattern. In this way, we can transfer thousands of diamond chiplets into their corresponding sockets all at the same time,” Li says.

The researchers demonstrated a 500-micron by 500-micron area transfer for an array with 1,024 diamond nanoantennas, but they could use larger diamond arrays and a larger CMOS chip to further scale up the system. In fact, they found that with more qubits, tuning the frequencies actually requires less voltage for this architecture.

“In this case, if you have more qubits, our architecture will work even better,” Li says.

The team tested many nanostructures before they determined the ideal microchiplet array for the lock-and-release process. However, making quantum microchiplets is no easy task, and the process took years to perfect.

“We have iterated and developed the recipe to fabricate these diamond nanostructures in MIT cleanroom, but it is a very complicated process. It took 19 steps of nanofabrication to get the diamond quantum microchiplets, and the steps were not straightforward,” he adds.

Alongside their QSoC, the researchers developed an approach to characterize the system and measure its performance on a large scale. To do this, they built a custom cryo-optical metrology setup.

Using this technique, they demonstrated an entire chip with over 4,000 qubits that could be tuned to the same frequency while maintaining their spin and optical properties. They also built a digital twin simulation that connects the experiment with digitized modeling, which helps them understand the root causes of the observed phenomenon and determine how to efficiently implement the architecture.

In the future, the researchers could boost the performance of their system by refining the materials they used to make qubits or developing more precise control processes. They could also apply this architecture to other solid-state quantum systems.

This work was supported by the MITRE Corporation Quantum Moonshot Program, the U.S. National Science Foundation, the U.S. Army Research Office, the Center for Quantum Networks, and the European Union’s Horizon 2020 Research and Innovation Program.

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Related links.

• Quantum Photonics and AI Laboratory
• Terahertz Integrated Electronics Group
• Research Laboratory of Electronics
• Microsystems Technology Laboratories
• Department of Electrical Engineering and Computer Science

Related Topics

• Computer science and technology
• Quantum computing
• Electronics
• Semiconductors
• Electrical Engineering & Computer Science (eecs)
• National Science Foundation (NSF)

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