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The Effectiveness of Policy Interventions for School Bullying: A Systematic Review

• William Hall

University of North Carolina at Chapel Hill

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Objective : Bullying threatens the mental and educational well-being of students. Although anti-bullying policies are prevalent, little is known about their effectiveness. This systematic review evaluates the methodological characteristics and summarizes substantive findings of studies examining the effectiveness of school bullying policies. Method : Searches of 11 bibliographic databases yielded 489 studies completed since January 1, 1995. Following duplicate removal and double-independent screening based on a priori inclusion criteria, 21 studies were included for review. Results : Substantially more educators perceive anti-bullying policies to be effective rather than ineffective. Whereas several studies show that the presence or quality of policies is associated with lower rates of bullying among students, other studies found no such associations between policy presence or quality and reductions in bullying. Consistent across studies, this review found that schools with anti-bullying policies that enumerated protections based on sexual orientation and gender identity were associated with better protection of lesbian, gay, bisexual, transgender, and queer (LGBTQ) students. Specifically, LGBTQ students in schools with such policies reported less harassment and more frequent and effective intervention by school personnel. Findings are mixed regarding the relationship between having an anti-bullying policy and educators’ responsiveness to general bullying. Conclusions : Anti-bullying policies might be effective at reducing bullying if their content is based on evidence and sound theory and if they are implemented with a high level of fidelity. More research is needed to improve on limitations among extant studies.

Bullying in schools is a pervasive threat to the well-being and educational success of students. Bullying refers to unwanted aggressive behaviors enacted intentionally over time by an individual or group using some form of power to cause physical and/or psychological harm to another individual or group in a shared social context (Gladden, Vivolo-Kantor, Hamburger, & Lumpkin, 2014 ; Olweus, 2013 ). Bullying is also a widespread phenomenon. A meta-analysis of 82 studies conducted in 22 countries in North America, South America, Europe, Southern Africa, East Asia, and Australia and Oceania found that 53% of youth were involved in bullying as bullies, victims, or both bullies and victims (Cook, Williams, Guerra, & Kim, 2010 ).

Negative Outcomes Connected with Bullying

Involvement in bullying as perpetrators, victims, bully–victims, and bystanders has been linked with deleterious outcomes by both cross-sectional and longitudinal studies. Youths who are bullied can experience immediate negative effects that include physical injury, humiliation, sadness, rejection, and helplessness (Kaiser & Rasminsky, 2009 ). Over time, a number of mental and behavioral health problems can emerge, including low self-esteem, anxiety, depression, suicidal ideation and behavior, conduct problems, psychosomatic problems, psychotic symptoms, and physical illness (Arseneault, Bowes, & Shakoor, 2010 ; Dake, Price, & Telljohann, 2003 ; Gini & Pozzoli, 2009 ; Kim & Leventhal, 2008 ; Klomek, Sourander, & Gould, 2010 ; Reijntjes et al., 2011 ; Reijntjes, Kamphuis, Prinzie, & Telch, 2010 ; Ttofi, Farrington, Lösel, & Loeber, 2011a ). In addition, students who have been bullied may not feel safe at school and may disengage from the school community due to fear and sadness, which may, in turn, contribute to higher rates of absenteeism and lower academic performance (Arseneault et al., 2006 ; Buhs & Ladd, 2001 ; Buhs, Ladd, & Herald, 2006 ; Glew, Fan, Katon, Rivara, & Kernic, 2005 ; Juvonen, Nishina, & Graham, 2000 ; Nakamoto & Schwartz, 2010 ).

Youths who bully also face psychosocial difficulties. These youths often grow up in harsh social environments with few resources (Hong & Espelage, 2012 ), and bullies often lack impulse control and empathy for others (O’Brennan, Bradshaw, & Sawyer, 2009 ; van Noorden, Haselager, Cillessen, & Bukowski, 2015 ). Students who bully are more likely to skip school, perform poorly, and drop out (Jankauskiene, Kardelis, Sukys, & Kardeliene, 2008 ; Ma, Phelps, Lerner, & Lerner, 2009 ). Bullying perpetration also is associated with depressive symptoms, suicidal ideation and behavior, and violent and criminal behavior (e.g., assault, robbery, vandalism, carrying weapons, and rape; Dake et al., 2003 ; Kim & Leventhal, 2008 ; Klomek et al., 2010 ; Ttofi, Farrington, & Lösel, 2012 ; Ttofi, Farrington, Lösel, & Loeber, 2011b ). Compared to nonperpetrators, students who bully have an increased risk of violent and criminal behaviors into adulthood. A meta-analysis of longitudinal studies found that school bullies were 2.5 times more likely to engage in criminal offending over an 11-year follow-up period (Ttofi et al., 2011b ).

Other youths involved in bullying include bully–victims and bystanders. Bully–victims are students who have been bullied but also engage in bullying others. Bully–victims can experience a combination of internalizing and externalizing problems (Cook, Williams, Guerra, Kim, & Sadek, 2010 ). Student bystanders are present in up to 90% of bullying incidents (Atlas & Pepler, 1998 ; Craig & Pepler, 1995 ; Glew et al., 2005 ; Hawkins, Pepler, & Craig, 2001 ). Youths who witness bullying often report emotional distress, including increased heart rate and higher levels of fear, sadness, and anger when recalling bullying incidents (Barhight, Hubbard, & Hyde, 2013 ; Janson & Hazler, 2004 ). Thus, across the literature, bullying is associated with problematic outcomes for perpetrators, victims, bully–victims, and bystanders alike.

Policy as an Intervention for Bullying

Perspectives vary on how to best address bullying in schools. Intervention strategies have included suspending and expelling bullies, training teachers on intervening, teaching empathy and respect to students through classroom lessons, maintaining constant adult supervision throughout school settings, collaborating with parents about student behavior, and enacting school-wide policies about bullying. In the United States, policies addressing bullying emerged in 1999 following the Columbine High School shootings. These policies have spread due to increased awareness and concern about student violence and school safety (Birkland & Lawrence, 2009 ). A policy is a system of principles created by governing bodies or public officials to achieve specific outcomes by guiding action and decision making. Policy is an umbrella term that refers to various regulatory measures, including laws, statutes, policies, regulations, and rules. These terms vary based on the jurisdiction and legal authority of the individual or group who established the policy. In the United States, K–12 education policy, which includes school bullying policy, can be established at the federal, state, and local levels (Mead, 2009 ).

One advantage of policy interventions for bullying is that they can influence student, teacher, and administrator behavior as well as school organizational practices. For example, school bullying policies typically prohibit certain behaviors, such as threatening and harassing other students or retaliating against students who witness and then report bullying incidents. Policies may also require behaviors, such as requiring teachers to report bullying incidents to administrators and requiring administrators to investigate reports of bullying. Further, policies may promote certain behaviors by explicitly stating positive behavioral expectations for students or discourage behaviors by explicitly stating punishments associated with aggressive behaviors. At the school level, policies can guide organizational practices, such as establishing bullying incident reporting procedures and creating school-safety teams tasked with developing and executing school-safety plans. Thus, bullying policies can influence individual and organizational behaviors.

Another advantage of bullying policies is that they are upstream interventions that provide a foundation for downstream interventions. In other words, policies are systems-level interventions that typically require more targeted intervention programs, practices, and services at the organizational, group, and individual levels (McKinlay, 1998 ). For example, a bullying policy may be adopted within a state or district; the policy then applies to all schools within the state or district. This policy may require training all school employees on bullying prevention strategies, integrating bullying awareness and education into classroom lessons and curricula, and providing counseling for students involved in bullying. Thus, policy lays the groundwork for an array of more specific and targeted interventions to be deployed in schools by outlining goals and directives in the policy document.

Policy design is important because the content influences a cascade of actions throughout school systems, which may result in positive or negative outcomes. For example, a bullying policy that requires schools to provide counseling services and positive behavioral reinforcement to students who perpetrate bullying is markedly different than a policy that requires schools to suspend or expel students who have carried out multiple acts of bullying. Research shows that overly harsh and punitive policies (e.g., “three strikes and you’re out” policies or “zero-tolerance” policies) are not effective at reducing aggression or improving school safety (American Psychological Association Zero Tolerance Task Force, 2008 ). Thus, bullying policies should be crafted and revised using evidence-based strategies.

Percentage of State Anti-Bullying Laws That Included Key Policy Components Identified by the U.S. Department of Education

Note.  The percentages are based on 46 state bullying laws passed between 1999 and 2011. Source: Stuart-Cassel, Bell, & Springer, 2011 .

Despite the widespread adoption and application of anti-bullying policies within the United States and in other countries, relatively few studies have examined the effectiveness of these interventions. Instead, research has focused on programmatic interventions (e.g., Cool Kids Program, FearNot!, Friendly Schools, KiVa, and Steps to Respect). Numerous systematic or meta-analytic reviews have been completed on the effectiveness of programmatic interventions for school bullying (e.g., Baldry & Farrington, 2007 ; Evans, Fraser, & Cotter, 2014 ; Ferguson, San Miguel, Kilburn, & Sanchez, 2007 ; Lee, Kim, & Kim, 2013 ; Merrell, Gueldner, Ross, & Isava, 2008 ; Ttofi & Farrington, 2011 ). However, a systematic review of the literature on the effectiveness of policy interventions for school bullying has not been completed.

Purpose of the Current Review

Given the proportion of students directly or indirectly involved in bullying, the array of educational and psychological problems associated with bullying, the extensive adoption of anti-bullying policies, and the absence of a review of the research on these policy interventions, the need for a systematic review on this topic is imperative. The following questions drove this review: Are school policies effective in reducing or preventing bullying behavior among students? What is the state or quality of the research on school bullying policy effectiveness? What additional research is needed on school bullying policy effectiveness? Given these questions, the objectives of this review were threefold: to systematically identify, examine, and evaluate the methodological characteristics of studies investigating the effectiveness of school bullying policies; to summarize the substantive findings from these studies; and to provide recommendations for future research.

In preparation of this review, the author adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria (Moher, Liberati, Tetzlaff, & Altman, 2009 ). Before undertaking the search for relevant studies, the author developed protocols for bibliographic database searches, study inclusion and exclusion criteria, and a data extraction tool. In addition, this review was registered with PROSPERO, an international database of systematic reviews regarding health and social well-being.

Search Procedure

A behavioral and social sciences librarian was consulted to assist with developing a search string and identifying relevant computerized bibliographic databases in which to search. The following search string was used to search all databases for studies published between January 1, 1995, and November 8, 2014: school AND bullying AND (law OR policy OR policies OR legislation OR statute) AND (effect OR effects OR effectiveness OR efficacy OR impact OR influence). The search of multiple databases increased the likelihood of identifying all possible studies falling within the scope of the review; thus, the author searched 11 databases, some of which included gray literature sources (e.g., conference papers, government reports, and unpublished papers). Searches were performed in the following databases via EBSCO using terms searched within the abstracts: CINAHL (Cumulative Index to Nursing and Allied Health Literature), Educational Full Text, ERIC (Education Research Information Center), PsycINFO, and Social Work Abstracts. The following databases were searched via ProQuest using terms searched within the titles, abstracts, and subject headings: ASSIA (Applied Social Sciences Index and Abstracts), Dissertations & Theses Full Text, and Social Services Abstracts. In addition, the Conference Proceedings Citations Index was searched using terms searched within titles, abstracts, and keywords. Finally, PubMed was searched using terms searched within titles and abstracts. These more formal bibliographic database searches were supplemented with internet searches using Google Scholar.

Inclusion Criteria

Studies were included in the review if they met the following criteria: (a) collected data and reported results on the effectiveness of policy interventions for bullying in school settings; (b) written in English; and (c) completed since January 1, 1995. Policy interventions for bullying were defined as statutes, policies, regulations, or rules established at the national, state, district, or school levels with the goal of reducing bullying in K–12 schools. Effectiveness referred to the extent to which a policy intervention prevented or reduced student bullying behavior. Given that school bullying policy is a nascent area of empirical inquiry with relatively few empirical investigations and evaluations, the author did not use stringent exclusion criteria in terms of study designs and methods. Only studies written in English were included due to the researchers’ language proficiency. Finally, the time period selected allowed for a comprehensive and contemporary review of the empirical literature completed in this area over the past 20 years.

Study Screening

Flow diagram depicting the identification, screening, and inclusion of studies.

Data Extraction

A data extraction sheet was developed to assist with identifying and collecting relevant information from the 21 included studies. Information extracted included the citation, purpose of the study, study design, sampling strategy and location, response rate, sample size and characteristics, measurement of relevant variables, analyses performed, and results and findings. The author extracted this information and a research assistant then compared the completed extraction sheets with the source documents to assess the accuracy of the extractions. There were only six points of disagreement between the extractor and checker, which they then resolved together by examining the source documents and extractions simultaneously.

Data Synthesis

Initial review of the included studies revealed that a quantitative synthesis, such as a meta-analysis, was not advisable due to the methodological heterogeneity of the studies and differences in approaches to evaluating policy effectiveness. Thus, a narrative thematic synthesis approach was used (Thomas, Harden, & Newman, 2012 ). The substantive findings on policy effectiveness were first categorized based on the outcome evaluated and then synthesized within each category.

A total of 21 studies were included in this review: 9 peer-reviewed journal articles, 6 research reports that were not peer-reviewed, 5 doctoral dissertations, and 1 master’s thesis. A summary of the methodological characteristics of these studies is presented—including a synthesis of the substantive findings regarding the effectiveness of school bullying policies—in Table S1 (available online) .

Methodological Characteristics of the Studies

Of the 21 studies, 12 (57%) used mixed methods, 8 (38%) used quantitative methods, and 1 (5%) used qualitative methods. All studies relied on cross-sectional designs. Most studies (65%) used convenience sampling, whereas the remaining studies used some form of probability sampling. More than half (57%) of studies used national samples, whereas 24% used samples from a single city or local region, 15% used statewide samples, and 5% used samples from areas in multiple countries. Over 80% of studies sampled participants in the United States, with other studies drawing participants from Europe, Australia, East Asia, and Southwest Asia. The most common recruitment sites were schools, followed by listservs, websites, community groups or organizations, professional associations, and personal contacts. Most studies reported participant response rates which varied from 21% to 98%, and the average response rate across studies was 57% ( SD = 29%). Eight studies did not report response rates.

Across studies, sample sizes varied from 6 to 8,584 participants. Only the qualitative study had fewer than 50 participants, and two studies had between 50 and 100 participants. Most studies had relatively large samples with more than 500 respondents. The most commonly used participants were students, followed by teachers. Other respondents included administrators, school psychologists, school counselors, education support professionals, and parents. About one third of studies included multiple participant groups (e.g., students and teachers). Most studies (62%) recruited participants from K–12 settings, whereas other studies recruited participants from a single school level: elementary, middle, or high school. Among adult participants, about 75% were female and 90% were White. These percentages are similar to those reported by the U.S. Department of Education, which show that 76% of teachers are female and 82% are White (Snyder & Dillow, 2013 ).

Samples of students were diverse in terms of race/ethnicity, with most studies consisting of about two-thirds White participants as well as Black, Hispanic/Latino/Latina, Asian, Native American Indian, Middle Eastern, and multiracial students. In addition, student samples were closer to having equal proportions of males and females. Five studies included student participants who were exclusively lesbian, gay, bisexual, transgender, or queer (LGBTQ), whereas 6 studies did not report information about student sexual orientation or gender identity. In addition, studies typically did not measure or report participant national origin, immigrant/citizenship status, religious identity, socioeconomic status, or ability/disability status. Finally, most students were high school students.

Evaluation methods

All studies relied on self-report data to evaluate school bullying policy effectiveness. However, studies varied based on the outcome used in their evaluations: Eight studies examined school members’ perceptions of policy effectiveness, 5 studies examined student bullying perpetration and/or victimization behaviors, 6 studies investigated anti-LGBTQ bullying victimization, and 2 studies considered educator intervention in bullying. The level of policies evaluated also varied: Eleven studies examined school-level policies, 3 studies examined district-level policies, 3 studies examined state laws, 3 studies examined both state laws and school-level policies, and one study examined a national policy.

Studies also varied in terms of the analytic approaches used to evaluate effectiveness: Nine studies used bivariate analyses, 8 studies used descriptive statistics of perceived effectiveness, 3 studies used multivariate analyses, and one study used both bivariate and multivariate analyses. Studies that used a bivariate analytic approach compared measures of teachers’ responsiveness to bullying or measures of student bullying between those in schools with and without anti-bullying policies or between schools with high- versus low-quality anti-bullying policies. In these studies, distinctions between high- and low-quality policies were made by the researchers in each study using content analyses of policy strategies that were theoretically and empirically associated with effectiveness in the bullying literature (e.g., having a definition of bullying, ensuring adult supervision of students, and outlining consequences for bullies; Ordonez, 2006 ; Woods & Wolke, 2003 ). Policy content analysis scores were then used to distinguish between high- and low-quality policies. Descriptive statistical analyses of effectiveness entailed participants responding to a single self-report item about their perceptions of policy effectiveness (e.g., “How effective do you feel that your school’s anti-bullying policy is in reducing bullying?”), with Likert-type response options related to agreement/disagreement or categorical response options (e.g., yes or no). Multivariate analytic approaches primarily used student bullying scores as the dependent variable and either a continuous anti-bullying policy score or a dichotomous variable indicating whether or not the school had an anti-bullying policy as the independent variable. Continuous school bullying policy scores were based on either a set of items about the perceived presence of an anti-bullying policy (e.g., “I think my school clearly set forth anti-bullying policies and rules”) or a content analysis of policy documents to identify the presence of criteria or strategies associated with effectiveness (e.g., having a definition of bullying, establishing procedures and consequences for bullies, having educational events about the school’s bullying guidelines, ensuring adult supervision in school areas prone to bullying, and formulating a school task group to coordinate anti-bullying efforts).

The measures used to assess bullying among students varied; some studies used established scales (e.g., Olweus Bullying Questionnaire), whereas other studies used items developed by the researchers. The number of items used to measure bullying varied from 3 to 23 ( M = 18.2, SD = 6.1). Of the 11 studies that measured bullying, the majority measured bullying victimization ( n = 8). Only 2 studies measured both bullying victimization and perpetration, and one study measured just perpetration. In terms of the types of bullying measured, 5 studies measured physical, verbal, social, electronic, and sexual bullying; 3 studies measured physical, verbal, and social bullying; one study measured physical, verbal, social, and electronic bullying; one study measured physical, verbal, social, and property bullying; and one study measured verbal bullying. In addition to student bullying, educators’ responsiveness to bullying was another outcome variable that was used in 8 studies. Only one study used a scale to measure educator responsiveness, and the remaining 7 studies used one to four items regarding educators responding to student bullying.

Results on Policy Effectiveness

Given that the 21 studies differed on the outcomes used in their evaluations of school bullying policy effectiveness, substantive results are presented by each outcome category: school members’ perceptions of policy effectiveness, student bullying perpetration and/or victimization, anti-LGBTQ bullying victimization, and educator intervention in bullying.

Perceptions of policy effectiveness

Eight studies reported results on participants’ perceptions of policy effectiveness. Results showed that 5% to 88% ( M = 49.4%, SD = 33.4%) of educators perceived school bullying policies to be effective to some degree, 4% to 79% ( M = 24.5%, SD = 23.6%) of educators perceived policies to be ineffective , and 16% to 70% ( M = 51.3%, SD = 30.6%) of educators were uncertain about policy effectiveness (Barnes, 2010 ; Bradshaw, Waasdorp, O’Brennan, & Gulemetova, 2013 ; Hedwall, 2006 ; Isom, 2014 ; Sherer & Nickerson, 2010 ; Terry, 2010 ). Only one study measured students’ perceptions of policy effectiveness, and results showed that they perceived policies to be moderately effective (Ju, 2012 ). In addition, only one of the 21 studies collected multiple waves of data, although different sets of respondents were used at each of the two waves (Samara & Smith, 2008 ). In this study, researchers examined perceived effectiveness before and after the passage of an anti-bullying policy; however, there were no significant changes in perceived effectiveness.

Student bullying perpetration and victimization

Five studies reported findings on the influence of policy on general student bullying outcomes. Two of these 5 studies examined policy content in relation to effectiveness. One study found that students in schools with high-quality bullying policies reported lower rates of verbal and physical bullying victimization than students in schools with low-quality policies; however, no differences were found for social/relational or property bullying victimization (Ordonez, 2006 ). In this study, policy quality was evaluated based on the inclusion of the following elements: a definition of bullying; procedures and consequences for bullies; plans for disseminating the policy to students, school personnel, and parents; programs or practices that encourage acceptance of diversity, empathy for others, respect toward others, peer integration, and responsible use of power; supervision of students in school areas prone to bullying (e.g., playground, cafeteria, and hallways); and socio-emotional skills training for victims and bullies (Ordonez, 2006 ). Similarly, another study found lower rates of verbal, physical, and property bullying victimization among students in schools with high-quality bullying policies, yet higher rates of social/relational bullying perpetration (Woods & Wolke, 2003 ). In this study, policy quality was evaluated based on the inclusion of the following elements: a definition of bullying; recognition of negative outcomes associated with bullying; discussion of locations where bullying can occur; evaluation of the prevalence of bullying; involvement of stakeholders in policy development; supervision of students in school areas; formulation of a school task group to coordinate anti-bullying efforts; classroom rules about bullying; classroom sessions about bullying; discussion of bullying at PTA/PTO meetings; involvement of parents in bullying prevention efforts; and follow-up with victims and bullies after incidents (Woods & Wolke, 2003 ).

Other studies examined associations between policy presence and bullying outcomes. Three significant or marginally significant ( p ≤ .095) associations were found: the presence of an anti-bullying policy was inversely related to general bullying victimization, social/relational bullying perpetration, and verbal bullying perpetration (Farrington & Ttofi, 2009 ; Lee, 2007 ). Conversely, eight nonsignificant associations were found between school bullying policy presence and scores of general, physical, verbal, and social/relational bullying perpetration, as well as physical, verbal, and social/relational bullying victimization (Farrington & Ttofi, 2009 ; Khoury-Kassabri, 2011 ; Lee, 2007 ). In addition, having a bullying policy was not associated with increases in general bullying perpetration or victimization (Farrington & Ttofi, 2009 ).

Anti-LGBTQ bullying

Six studies with rather large samples of primarily LGBTQ students consistently found that compared to students in schools without an anti-bullying policy or with an anti-bullying policy that did not explicitly prohibit bullying based on sexual orientation and gender identity, students in schools with comprehensive anti-bullying policies that included protections based on sexual orientation and gender identity reported lower rates of anti-LGBTQ bullying, more school personnel frequently intervening when anti-LGBTQ comments were made in their presence, and more school personnel being effective in their anti-LGBTQ bullying responses (Kosciw & Diaz, 2006 ; Kosciw, Diaz, & Greytak, 2008 ; Kosciw, Greytak, Diaz, & Bartkiewicz, 2010 ; Kosciw, Greytak, Bartkiewicz, Boesen, & Palmer, 2012 ; Kosciw, Greytak, Palmer, & Boesen, 2014 ; Phoenix et al., 2006 ). These differences were consistent in analyses of both local anti-bullying policies and state anti-bullying laws.

Educator intervention in bullying

Educators play a key role in reducing bullying behavior among students. One study found that compared to those in schools without a bullying policy, educators in schools with bullying policies were more likely to enlist the help of parents and colleagues in responding to a bullying incident and were less likely to ignore bullying (Bauman, Rigby, & Hoppa, 2008 ). Conversely, a large, national study of educators found no relationship between having an anti-bullying policy and educators’ comfort intervening in both general and discriminatory bullying (O’Brennan, Waasdorp, & Bradshaw, 2014 ).

The findings are discussed according to the research questions that drove the review.

Are Policies Effective at Reducing Bullying?

Educators were divided in their perceptions of the effectiveness of policies for school bullying; however, on average, about twice as many educators reported that policies were effective to some degree as those who reported that they were not effective. Nonetheless, descriptive summaries of perceptions of effectiveness are typically not viewed as compelling sources of evidence for the effectiveness of an intervention (Petticrew & Roberts, 2003 ). However, educators are considered key informants who know what goes on in schools.

Two studies found lower rates of verbal and physical bullying in schools with high- rather than low-quality policies; however, in terms of social/relational bullying, one study found no difference, and another study found higher rates of social/relational bullying in schools with high-quality policies (Ordonez, 2006 ; Woods & Wolke, 2003 ). This tentative finding suggests that improving the quality of bullying policies may be effective for direct and overt forms of bullying (e.g., hitting and name-calling) but may not effect social/relational bullying. Across the two studies, elements of policy quality associated with decreases in verbal and physical bullying included a comprehensive definition of bullying; school and classroom rules and procedures about bullying; plans for communicating the policy within the school community; supervision of students across school areas; involvement of parents in anti-bullying efforts; involvement of multiple stakeholders in school-wide anti-bullying actions; and working with and educating students around social, emotional, and behavioral issues to prevent bullying. Extant policies may overemphasize traditional notions of what bullying is (i.e., physical and verbal harassment) and underemphasize or neglect to address more recent understandings of social/relational aggression as bullying. In addition, direct and overt forms of bullying may be more amenable to policy interventions because educators can directly observe these behaviors and then proceed with their response, whereas social/relational bullying often occurs away from the direct supervision of educators (Young, Nelson, Hottle, Warburton, & Young, 2013 ). Educators have reported difficulty in responding to bullying incidents that they did not witness (Mishna, Pepler, & Wiener, 2006 ). Similarly, although many educators are aware of cyberbullying, few take steps to address it and many are uncertain about how to confront cyberbullying, which often occurs outside of school (Cassidy, Brown, & Jackson, 2012 ; Stauffer, Heath, Coyne, & Ferrin, 2012 ; Vandebosch, Poels, & Deboutte, 2014 ). Nonetheless, educators can address cyberbullying occurring on or off school grounds if the aggression creates a hostile school environment and substantially disrupts a student’s learning environment (Stuart-Cassel et al., 2011 ).

Findings among the few studies that examined associations between policy presence and student bullying were mixed, although more nonsignificant than significant associations were found. At first glance, one may conclude from these findings that the presence of bullying policies does not influence bullying among students; however, the presence of a policy is necessary but is not sufficient to affect student behavior. Indeed, after a policy has been adopted, it must be put into practice. The mere adoption or presence of a policy does not mean that it will be immediately and consistently put into practice exactly as intended. The implementation of a policy is a complex, dynamic, and ongoing process involving a vast assortment of people, resources, organizational structures, and actions. No study that examined the implementation of school bullying policies found that the policies were being implemented precisely as intended (Hall & Chapman, 2016a , 2016b ; Hedwall, 2006 ; Holmgreen, 2014 ; Jordan, 2014 ; LaRocco, Nestler-Rusack, & Freiberg, 2007 ; MacLeod, 2007 ; Robbins, 2011 ; Schlenoff, 2014 ; Smith-Canty, 2010 ; Terry, 2010 ). Indeed, the extent of faithful implementation in these studies varied considerably by location and policy component. Therefore, fidelity of implementation (i.e., the extent that a policy is put into practice as intended based on the directives expressed in the policy document) may mediate the relationship between policy adoption or presence and the targeted policy outcome of student bullying. However, none of the studies reviewed measured policy implementation fidelity. Thus, one can conclude from this evidence that in some cases, policy presence was associated with decreases in bullying; in other cases, however, there were no such associations. Because data on implementation were not collected in any study, it is not known if the lack of significant associations was related to lack of faithful implementation of policies.

One area of consistent agreement in the findings relates to the benefits for LGBTQ students who are in schools with anti-bullying policies that explicitly provide protections based on sexual orientation and gender identity. These benefits included lower rates of victimization and higher rates of intervention by educators. Numerous studies have demonstrated that LGBTQ youths experience high rates of bullying victimization (Berlan, Corliss, Field, Goodman, & Austin, 2010 ; Espelage, Aragon, Birkett, & Koenig, 2008 ; Kosciw & Diaz, 2006 ; Kosciw et al., 2008 ; Kosciw et al., 2010 ; Kosciw et al., 2012 ; Kosciw et al., 2014 ; McGuire, Anderson, Toomey, & Russell, 2010 ; Varjas et al., 2008 ). However, only 20 states (40%) have enumerated protections based on sexual orientation and gender identity/expression in their anti-bullying laws (Human Rights Campaign, 2015 ). Given the evidence for the effectiveness of enumerated policies, all policies should prohibit harassment and bullying based on sexual orientation and gender identity.

Aside from the LGBTQ-focused studies, only two other studies examined educators’ responsiveness to bullying. Findings from these studies were somewhat contradictory, as one found a connection between having a bullying policy and responding to a bullying incident, whereas the other study found no relationship between having a policy and educators’ comfort in responding to bullying. However, the study that found no relationship included several other relevant independent variables (i.e., receiving training on how to implement the school’s bullying policy and having resources available in the school to help educators intervene), which were significantly associated with increased comfort in responding to bullying (O’Brennan et al., 2014 ). Thus, the relationship between the presence of a school bullying policy and educators’ responsiveness to bullying incidents may be mediated by training about putting the policy into practice and having resources available for intervention.

Finally, there was no evidence that one level of policy was more effective than another. Across the studies, school, district, and state policies all showed evidence for effectiveness as well as ineffectiveness. Policies do vary in terms of their weight in law. For example, a state statute has more legal force than an informal school policy established by a principal. Nonetheless, a school policy set by a principal is more proximal than a state policy, and therefore, the proximity may facilitate implementation of the policy at the school. Policy level may not be related to effectiveness. What likely matters more in terms of effectiveness are the strategies contained within a policy and the ways they are implemented.

What is the State of the Research on School Bullying Policy Effectiveness?

Systematic reviews summarize what is substantively known about a topic area and also provide a state of the research on a particular topic. Research to date on school bullying policy effectiveness has several strengths. In terms of designs, most studies have used a mixed-methods approach, which is advantageous because it capitalizes on the strengths of both quantitative and qualitative research and offsets weaknesses of using one or the other. Including quantitative methods allows for precise, numerical estimates related to distribution or the strength and direction of relationships, and including qualitative methods allows for rich, in-depth data related to context or complexity. Other strengths are related to sampling: More than one third of the studies used some form of probability sampling, over half of the studies used national samples, and many studies reported high response rates. These sampling strengths are beneficial in terms of generalizing findings. Also, almost all studies had sample sizes greater than 200, and two thirds of studies had large samples (i.e., approximately 500 to 8,500 participants). Larger samples can be more representative of a population and are beneficial in terms of statistical power. A final strength was that many studies collected data from multiple participants groups (e.g., teachers and students). Having multiple participant groups allows for a more comprehensive assessment and the triangulation of data sources, which can be used to compare and contrast findings and may help researchers corroborate findings.

On the other hand, several prominent methodological limitations were identified among the studies reviewed. First, the studies relied on evidence from cross-sectional surveys, which are vulnerable to selection bias and confounding. In addition, cross-sectional studies cannot examine a key criterion of causality: a temporal relationship wherein an anti-bullying policy was adopted and implemented, which then led to decreases in bullying over time. Second, most studies used convenience sampling. Although convenience sampling may be highly feasible and efficient, it can lead to the underrepresentation or overrepresentation of particular groups within a sample. Thus, convenience samples may not be representative of the populations of interest, which undermines the generalizations that can be made from the findings. Third, most of the studies used descriptive statistics or bivariate analyses to evaluate the effectiveness of bullying policies. Such analyses can be oversimplified and leave out relevant explanatory or contextualizing variables. In addition, some of the studies that used bivariate analyses did not report the exact statistical test used (e.g., independent groups t-test and chi-square test) or effect sizes and instead focused on substantive findings. Although these reports seemed to be aimed at a more general, nonscholarly audience, the omission of this information can become problematic in understanding the methods used and drawing conclusions about the results. Fourth, many studies asked participants to report whether their school had an anti-bullying policy. This question might be problematic for student respondents because they might not know about the policies in their schools.

A final limitation involved the measurement of bullying. The main goal of policy interventions for bullying is to prevent and reduce bullying behavior among students. Thus, studies evaluating the effectiveness of these interventions should measure bullying among students as a primary outcome. Nonetheless, only half of the studies directly measured student bullying, and most of these studies did not measure both bullying perpetration and victimization. Policies are aimed at influencing multiple actors involved in the bullying dynamic, which includes bullies, targets, victims, bully–victims, bystanders, parents, and school personnel. Thus, studies that do not measure bullying perpetration and victimization among students are not assessing the two main targeted behavioral outcomes of anti-bullying policies. In addition, bullying behaviors can manifest in many forms, including physical bullying, verbal bullying, social/relational bullying, cyberbullying, property bullying, and sexual bullying (Hall, 2016 ). However, none of the studies in this review measured all of the dimensions of bullying.

What Future Research is Needed on School Bullying Policy Effectiveness?

Undoubtedly, research on the effectiveness of policy interventions for school bullying will continue to expand. In order to build upon and address gaps and limitations in the extant literature, six recommendations are presented for future research on school bullying policy effectiveness. These recommendations are based on the critical analysis of studies in this systematic review.

First, future studies should employ more rigorous designs to evaluate the effectiveness of policy interventions for bullying. The randomized controlled trial (RCT) is the “gold standard” approach for measuring the impact of an intervention; however, RCTs are often infeasible for evaluating public policy interventions due to the political and legal nature of policies, which are implemented across large organizational systems and typically with prescribed timelines (Oliver et al., 2010 ). Thus, researchers may need to rely on other rigorous and feasible designs for evaluating policy effectiveness: pretest/posttest cohort designs, pretest/posttest matched comparison group designs, and interrupted time series designs (Oliver et al., 2010 ; Shadish, Cook, & Campbell, 2002 ). These study designs are superior to cross-sectional studies in determining the effectiveness of interventions (Coalition for Evidence-Based Policy, 2003 ; Petticrew & Roberts, 2003 ; Pilcher & Bedford, 2011 ).

Second, studies should collect data on outcomes and the implementation of policy components. None of the studies assessed implementation fidelity. When bullying policies do not successfully achieve targeted outcomes, we do not know whether those policies were implemented as intended and failed or whether lack of implementation fidelity is to blame. Implementation data, if collected, could be used to ensure that policies are being activated as intended with high levels of fidelity and reported along with outcome evaluation data in the study designs mentioned previously. These data also could be used to examine the predictive relationship between implementation fidelity and outcomes. Theory would suggest an inverse relationship where higher levels of implementation fidelity are associated with lower levels of bullying among students; however, this remains an untested hypothesis. Also, bullying policies are comprised of an array of directives to be put into action. Data on the fidelity of implementation of all components of an anti-bullying policy would allow researchers to examine the relative or combined impact of policy components on outcomes.

Third, analyzing policy content—versus only considering the presence of absence of a bullying policy—is needed for more nuanced understanding of which policies work, for whom, and why. A national review of state anti-bullying laws showed broad inclusion of some policy components (e.g., outlining the consequences for students who bully) and limited inclusion of other components (e.g., providing mental health services to perpetrators or victims of bullying; Stuart-Cassel et al., 2011 ). Theoretically and empirically based guidance about specific actions that can be prescribed in bullying policies is small but growing (Cornell & Limber, 2015 ; Nickerson, Cornell, Smith, & Furlong, 2013 ). Future research should analyze the relationships between policy content and bullying outcomes, which could help identify the most influential policy components. Examining only policy presence or absence is insufficient because a school district may indeed have an anti-bullying policy, but its content may not be evidence-based. Policies can also vary in the way they are written, as some policies are lengthy, vague, and contradictory, whereas other policies are clear, concise, and specific. This area of content could also be analyzed and may relate to educators’ comprehension of policies, which would influence implementation actions by educators, and subsequently, policy outcomes.

Fourth, future studies should use multivariate and multilevel analyses. The effectiveness of policy interventions for bullying are influenced by several variables, including policy content, fidelity of implementation, and school environmental factors. By using more complex statistical methods (e.g., regression modeling, structural equation modeling, propensity score matching, and hierarchical linear modeling), researchers will be able to examine the influence of multiple variables, examine moderating and mediating relationships, control for extraneous variables, match intervention participants with control participants, and account for clustered data (e.g., students or teachers nested within schools). These statistical methods will be essential to execute the recommended study designs and analytic methods described previously. The use of these statistical methods will help ensure the integrity of future findings on policy effectiveness.

Fifth, studies should improve sampling practices. To attain more representative samples, researchers should partner with school districts, state departments of education, and departments of public instruction, and they should employ some form of probability sampling. Many of the studies in this review that used probability sampling involved data collection collaborations with state- and district-level educational agencies. Educational agencies have a vested interest in the implementation and success of bullying policies, especially those codified as law. In addition, future studies should sample from multiple respondent groups—such as administrators, teachers, school mental health professionals, and students—to gain a more comprehensive and multiperspective understanding of the implementation and effectiveness of school bullying policies. Researchers also should sample across the K–12 spectrum because state and district policy guidelines typically apply across these grade levels. Yet, there may be differences in policy effectiveness between elementary, middle, and high school. Certain policy strategies also may need to be tailored based on student developmental differences and differences in school structure across the K–12 system.

Finally, future studies should use scales to measure both bullying perpetration and victimization, and these measures should assess all of the dimensions of bullying: physical, verbal, social/relational, electronic, sexual, and property bullying. Researchers may find that policies are more effective at addressing certain types of bullying than others (e.g., direct vs. indirect bullying). Multifactor scales with a sufficient number of items are needed to measure the full range of bullying behaviors. The Centers for Disease Control and Prevention created a compendium of bullying measures that is available to the public (see Hamburger, Basile, & Vivolo, 2011 ). However, caution should be taken in selecting instruments because some measures have low internal consistency reliability values (i.e., α < .70), low test-retest reliability coefficients (i.e., r < .70), no recall time frames, overly long and complex definitions of bullying, limited evidence of construct validity, limited evidence of criterion validity, and limited evidence regarding respondents’ understanding of the measure’s instructions and items (Hall, 2016 ). In addition, as opposed to questionnaires about bullying behaviors, peer and/or teacher nomination methods to identify students who are bullying victims or perpetrators may be more developmentally appropriate for elementary school-age children.

Strengths and Limitations of the Review

This review used a rigorous approach to identify relevant studies by searching 11 databases using an expert-informed search string. In addition, search records were independently screened by two screeners based on a priori inclusion criteria. Further, research reports and dissertations (forms of gray literature) were included to minimize publication bias. Nonetheless, unpublished research may be underrepresented in this review. Another limitation relates to the variability of studies: Studies varied in the respondents, sample locations, the types of policies examined, and the ways effectiveness was evaluated. This variability presented challenges for combining and comparing results. Another limitation of this review relates to the methodological limitations of some of the included studies. However, by presenting the methodological characteristics and substantive findings by study in Table S1 , readers are able to assess the methodological rigor and trustworthiness of findings accordingly.

Bullying is a widespread problem in which about half of students are directly involved and up to 90% of students are indirectly involved (Atlas & Pepler, 1998 ; Cook, Williams, Guerra, & Kim, 2010 ; Craig & Pepler, 1995 ; Glew et al., 2005 ; Hawkins et al., 2001 ). Policy interventions are an approach to bullying that establishes legal mandates for schools, influences the behavior of students and school personnel, and guides the implementation of other targeted interventions within schools. Findings on the effectiveness of policy interventions for bullying are primarily mixed, and there are limitations in the evaluation methods used. Research on school bullying policy will undoubtedly continue to expand with the growing understanding of the need for evidence-based education policies and as bullying policies continue to be introduced and revised in schools across the globe. Future research must use more rigorous methods and designs and may indeed find that policy interventions play a key role as one of a constellation of intervention strategies for preventing and reducing school bullying.

I would like to thank Mimi Chapman, Natasha Bowen, Barbara Fedders, Mark Fraser, and Kathleen Rounds for their advice and feedback regarding this paper. I also thank Rachele McFarland for her research assistance. The author was supported by the National Research Service Award Postdoctoral Traineeship from the National Institute of Mental Health, sponsored by Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, and the Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine (grant number T32 MH019117).

William Hall , PhD, MSW, is a postdoctoral fellow at the University of North Carolina at Chapel Hill.

Correspondence regarding this article should be directed to William James Hall, 325 Pittsboro Street, CB #3550, Chapel Hill, NC 27599-3550 or via e-mail to [email protected]

* Asterisks indicate studies that were included in the systematic review.

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• Submitted March 30, 2016
• Revised July 07, 2016
• Accepted July 27, 2016
• Published online January 26, 2017
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• Research article
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• Published: 14 December 2021

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 ].

Availability of data and materials

Data requests will be considered on a case-by-case basis; please email the corresponding author.

Code availability

Not applicable.

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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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Håkan Källmén

Epidemiology of Psychiatric Conditions, Substance Use and Social Environment (EPiCSS), Department of Global Public Health, Karolinska Institutet, Level 6, Solnavägen 1e, Solna, Sweden

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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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Correspondence to Mats Hallgren .

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Supplementary Information

Additional file 1..

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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 open science movement has developed out of growing concerns over the scientific standard of published academic research and a perception that science is in crisis (the “replication crisis”). Bullying research sits within this scientific family and without taking a full part in discussions risks falling behind. Open science practices can inform and support a range of research goals while increasing the transparency and trustworthiness of the research process. In this paper, we aim to explain the relevance of open science for bullying research and discuss some of the questionable research practices which challenge the replicability and integrity of research. We also consider how open science practices can be of benefit to research on school bullying. In doing so, we discuss how open science practices, such as pre-registration, can benefit a range of methodologies including quantitative and qualitative research and studies employing a participatory research methods approach. To support researchers in adopting more open practices, we also highlight a range of relevant resources and set out a series of recommendations to the bullying research community.

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Bullying in school is a common experience for many children and adolescents. Such experiences relate to a range of adverse outcomes, including poor mental health, poorer academic achievement, and anti-social behaviour (Gini et al., 2018 ; Nakamoto & Schwartz, 2010 ; Valdebenito et al., 2017 ). Bullying research has increased substantially over the past 60 years, with over 5000 articles published between 2010 and 2016 alone (Volk et al., 2017 ). Much of this research focuses on the prevalence and antecedents of bullying, correlates of bullying, and the development and evaluation of anti-bullying interventions (Volk et al., 2017 ). The outcomes of this work for children and young people can therefore be life changing, and researchers should strive to ensure that their work is trustworthy, reliable, and accessible to a wide range of stakeholders both inside and outside of academia.

In recent years, the replication crisis has led to growing concern regarding the standard of research practices in the social sciences (Munafò et al., 2017 ). To address this, open science practices, such as openly sharing publications and data, conducting replication studies, and the pre-registration of research protocols, have provided the opportunity to increase the transparency and trustworthiness of the research process. In this paper, we aim to discuss the replication crisis and highlight the risks that questionable research practices pose for bullying research. We also aim to summarise open science practices and outline how these can benefit the broad spectrum of bullying research as well as to researchers themselves. Specifically, we aim to highlight how such practices can benefit both quantitative and qualitative research and studies employing a participatory research methods approach.

The Replication Crisis

In 2015, the Open Science Collaboration (Open Science Collaboration, 2015 ) conducted a large-scale replication of 100 published studies from three journals. The results questioned the replicability of research findings in psychology. In the original 100 studies, 97 reported a significant effect compared to only 35 of the replications. Furthermore, the effect sizes reported in the original studies were typically much larger than those found in the replications. The findings of the Open Science Collaboration received significant academic and mainstream media attention, which concluded that psychological research is in crisis (Wiggins & Chrisopherson, 2019 ). While these findings are based on the analysis of psychological research, challenges in replicating research findings have been reported in a range of disciplines including sociology (Freese & Peterson, 2017 ) and education studies (Makel & Pluker, 2014 ). Shrout and Rodgers ( 2018 ) suggest that the notion that science is in crisis is further supported by (1) the number of serious cases of academic misconduct such as that of Diederick Stapel (Nelson et al., 2018 ) and (2) the prevalence of questionable research practices and misuse of inferential statistics and hypothesis testing (see Ioannidis, 2005 ). The replication crisis has called into question the degree to which research across the social sciences accurately describes the world that we live in or whether this literature is overwhelmingly populated by misleading claims based on weak and error-strewn findings.

The trustworthiness of research reflects the quality of the method, rigour of the design, and the extent to which results are reliable and valid (Cook et al., 2018 ). Research on school bullying has grown exponentially in recent years (Smith & Berkkun, 2020 ) and typically focuses on understanding the nature, prevalence, and consequences of bullying to inform prevention and intervention efforts. If our research is not trustworthy, this can impede theory development and call into question the reliability of our research and meta-analytic findings (Friese & Frankenbach, 2020 ). Ultimately, if our research findings are untrustworthy, this undermines our efforts to prevent bullying and help and support young people. Bullying research exists within a broader academic research culture, which facilitates and incentivises the ways that research is undertaken and shared. As such, the issues that have been identified have direct relevance to those working in bullying.

The Incentive Culture in Academia

“The relentless drive for research excellence has created a culture in modern science that cares exclusively about what is achieved and not about how it is achieved.”

Jeremy Farrar, Director of the Wellcome Trust (Farrar, 2019 ).

In academia, career progression is closely tied to publication record. As such, academics feel under considerable pressure to publish frequently in high-quality journals to advance their careers (Grimes et al., 2018 ; Munafò et al., 2017 ). Yet, the publication process itself is biased toward accepting novel or statistically significant findings for publication (Renkewitz & Heene, 2019 ). This bias fuels a perception that non-significant results will not be published (the “file drawer problem”: Rosenthal, 1979 ). This can result in researchers employing a range of questionable research practices to achieve a statistically significant finding in order to increase the likelihood that a study will be accepted for publication. Taken together, this can lead to a perverse “scientific process” where achieving statistical significance is more important than the quality of the research itself (Frankenhuis & Nettle, 2018 ).

Questionable Research Practices

Questionable research practices (QRPs) can occur at all stages of the research process (Munafò et al., 2017 ). These practices differ from research misconduct in that they do not typically involve the deliberate intent to deceive or engage in fraudulent research practices (Stricker & Günther, 2019 ). Instead, QRPs are characterised by misrepresentation, inaccuracy, and bias (Steneck, 2006 ). All are of direct relevance to the work of scholars in the bullying field since each weakens our ability to achieve meaningful change for children and young people. QRPs emerge directly from “researcher degrees of freedom” that occur at all stages of the research process and which simply reflect the many decisions that researchers make with regard to their hypotheses, methodological design, data analyses, and reporting of results (see Wicherts et al., 2016 for an extensive list of researcher degrees of freedom). These decisions pose fundamental threats to how robust a study is as each compromises the likelihood that findings accurately model a psychological or social process (Munafò et al., 2017 ). QRPs include p -hacking; hypothesising after the result is known (HARKing); conducting studies with low statistical power; and the misuse of p values (Chambers et al., 2014 ). Such QRPs may reflect a misunderstanding of inferential statistics (Sijtsma, 2016 ). A misunderstanding of statistical theory can also lead to a lack of awareness regarding the nature and impact of QRPs (Sijtsma, 2016 ). This includes the prevailing approach to quantitative data analysis, Null Hypothesis Significance Testing (NHST) (Lyu et al., 2018 ; Travers et al., 2017 ), which is overwhelmingly the approach used in the bullying field. QRPs can fundamentally threaten the degree to which research in bullying can be trusted, replicated, and effective in efforts to implement successful and impactful intervention or prevention programs.

P -hacking (or data-dredging) reflects methods of re-analysing data in different ways to find a significant result (Raj et al., 2018 ). Such methods can include the selective deletion of outliers, selectively controlling for variables, recoding variables in different ways, or selectively reporting the results of structural equation models (Simonsohn et al., 2014 ). While there are various methods of p -hacking, the end goal is the same: to find a significant result in a data set, often when initial analyses fail to do so (Friese & Frankenbach, 2020 ).

There are no available data on the degree to which p -hacking is a problem in bullying research per se, but the nature of the methods commonly used mean it is a clear and present danger. For example, the inclusion of multiple outcome measures (allowing those with the “best” results to be cherry-picked for publication), measures of involvement in bullying that can be scored or analysed in multiple ways (e.g. as a continuous measure or as a method to categorise participants as involved or not), and the presence of a diverse selection of demographic variables (which can be selectively included or excluded from analyses) all provide researchers with an array of possible analytic approaches. Such options pose a risk for p -hacking as decisions can be made on the results of statistical fishing (i.e. hunting to find significant effects) rather than on any underpinning theoretical rationale.

P -hacking need not be driven by a desire to deceive; rather, it can be used by well-meaning researchers and their wish to honestly identify useful or interesting findings (Wicherts et al., 2016 ). Sadly, even in this case, the impact of p- hacking remains profoundly problematic for the field. The p- hacking process biases the literature towards erroneous significant results and inflated effect sizes, impacting on our understanding of any issue that we seek to understand better, and biasing effect size estimates reported in meta-analyses (Friese & Frankenbach, 2020 ). While such effects may seem remote or of only academic interest, they compromise all that we in the bullying field seek to accomplish because they make it much less likely that effective, impactful, and meaningful intervention and prevention strategies can be identified and implemented.

Typically, quantitative research follows the hypothetico-deductive model (Popper, 1959 ). From this perspective, hypotheses are formulated based on appropriate theory and previous research (Rubin, 2017 ). Once written, the study is designed, and data are collected and analysed (Rubin, 2017 ). Hypothesising after the result is known, or HARKing (Kerr, 1998 ), occurs when researchers amend their hypotheses to reflect their completed data analysis (Kerr, 1998 ). HARKing results in confusion between confirmatory and exploratory data analysis (Shrout & Rodgers, 2018 ), creating a literature where hypotheses are always confirmed and never falsified. This inhibits theory development (Rubin, 2017 ) in part because “progress” is, in fact, the accumulation of type 1 errors.

Low Statistical Power

Statistical power reflects the power in a statistical test to find an effect if there is one to find (Cohen, 2013 ). There are concerns regarding the sample sizes used in bullying research, as experiences of bullying are typically of a low frequency and positively skewed (Vessey et al., 2014 ; Volk et al., 2017 ). Low statistical power is problematic in two ways. First, it increases the type II error rate (the probability of falsely rejecting the null hypothesis), meaning that researchers may fail to report important and meaningful effects. Statistically significant effects can still be found under the conditions of low statistical power; however, the size of these effects is likely to be exaggerated due to a lower positive predictive value (the probability of a statistically significant result being genuine) (Button et al., 2013 ). In this case, researchers may find significant effects even in small samples, but those effects are at risk of being inflated.

QRPs in Qualitative Research

Apart from the previously discussed issues, there are also QRPs in qualitative work. Mainly, these involve issues pertaining to trustworthiness such as credibility, transferability, dependability, and confirmability (See Shenton, 2004 ). One factor that can influence perceptions about qualitative work is the possibility of subjectivity or different interpretations of the same data (Haven & Van Grootel, 2019 ). Additionally, the idea that the researcher will be biased and that their experiences, beliefs, and personal history will all influence how they both collect and interpret data has also been discussed (Berger, 2015 ). Clearly stating the positionality of the researcher and how their experiences informed their current research (the process of reflexivity) can help others better understand their interpretation of the data (Berger, 2015 ). Finally, one decision that qualitative researchers should consider when thinking about their designs is their stopping criteria. This might imply code or meaning saturation (see Hennink et al., 2017 , for more detail on how these two types are different from one another). Thus, making it clear in the conceptualisation process when and how the data collection will stop is important to assure transparency and high-quality research. This is not a complete list of QRPs in qualitative research, but these seem to be the most urgent when it comes to bullying research when thinking about open science.

The Prevalence and Impact of QRPs

Identifying the prevalence of QRPs and academic misconduct is challenging as this is reliant on self-reports. In their survey of 2155 psychologists, John et al. ( 2012 ) identified that 78% of participants had not reported all dependent measures, 72% had collected more data after finding their statistical effects were not statistically significant, 67% reported selective reporting of studies that “worked” (yielded a significant effect), and 9% reported falsifying data. Such problematic practices have serious implications for the reliability of effects reported in the research literature (John et al., 2012 ), which can impact interventions and treatments such evidence may inform. Furthermore, De Vries et al. ( 2018 ) have highlighted how biases in the publication process threaten the validity of treatment results reported in the literature. Although focused on the treatment of depression, their work has clear lessons for the bullying research community. They demonstrate how the bias towards reporting more positive, significant effects, distorts a literature in favour of treatments that appear efficacious but are much less so in practice (Box 1 ).

Box 1 The Replication Crisis

Munafò et al. ( 2017 ) outline a manifesto for reproducible research, highlighting problems with current research practices.

Shrout and Rodgers  ( 2018 ) provide an overview of the replication crisis and questionable research practices.

Steneck ( 2006 ) provides a detailed overview of definitions of academic misconduct, questionable research practices, and academic integrity.

Open Science

Confronting these challenges can be daunting, but open science offers several strategies that researchers in the bullying field can use to increase the transparency, reproducibility, and openness of their research. The most common practices include openly sharing publications and data, encouraging replication, pre-registration, and open peer-review. Below, we provide an overview of open science practices, with a particular focus on pre-registration and replication studies. We recommend that researchers begin by using those practices that they can most easily integrate into their work, building their repertoire of open science actions over time. We provide a series of recommendations for the school bullying research community alongside summaries of useful supporting resources (Box 2 ).

Box 2 Key Reading on Open Science

Banks et al. ( 2019 ) discuss frequently asked questions about open science providing a good overview of open science practices and contemporary debates.

Crüwell et al. ( 2019 ) provide an annotated reading list on important papers in open science.

Gehlbach and Robinson ( 2021 ) in their introduction to a special edition of the journal Educational Psychologist they discuss the adoption of open science practices in the context of what they term “old school” research practices.

Lindsay ( 2020 ) outlines a series of steps researchers can take to integrate open science practices into their research.

Open Publication, Open Data, and Reporting Standards

Open publication.

Ensuring research publications are openly available by providing access to pre-print versions of papers or paying for publishers to make articles openly available is now a widely adopted practice (Concannon et al., 2019 ; McKiernan et al., 2016 ). Articles can be hosted on websites such as ResearchGate and/or on institutional repositories, allowing a wider pool of potential stakeholders to access relevant bullying research and increasing the impact of research (Concannon et al., 2019 ). This process also supports access for the research and practice communities in low- and middle-income countries where even Universities may be unable to pay journal subscriptions. The authors can also share pre-print versions of their papers for comment and review before submitting them to a journal for review using an online digital repository, such as PsyArXiv. Sharing publications in this way can encourage both early feedback on articles and the faster dissemination of research findings (Chiarelli et al., 2019 ).

Making data and data analysis scripts openly available is also encouraged, can enable further data analysis (e.g. meta-analysis), and facilitates replication (Munafò et al., 2017 ; Nosek & Bar-Anan, 2012 ). It also enables the collation of larger data sets, and secondary data analyses to test different hypotheses. Several publications on bullying in school are based on the secondary analysis of openly shared data (e.g. Dantchev & Wolke, 2019 ; Przybylski & Bowes, 2017 ) and highlight the benefits of such analyses. Furthermore, although limited in number, examples of papers on school bullying where data, research materials, and data analysis scripts are openly shared are emerging (e.g. Przybylski, 2019 ).

Bullying data often includes detailed personal accounts of experiences and the impact of bullying. Such data are highly sensitive, and there may be a risk that individuals can be identified. To address such sensitivities, Meyer ( 2018 ) (see box 3 ) proposes a tiered approach to the consent process, where participants are actively involved in decisions around what parts of their data and where their data are shared. Meyer ( 2018 ) also highlights the importance of selecting the right repository for your data. Some repositories are entirely open, whereas others only provide access to suitably qualified researchers. While bullying data pose particular ethical challenges, the sharing of all data is encouraged (Bishop, 2009 ; McLeod & O’Connor, 2020 ).

Reporting Standards

Reporting standards are standards for reporting a research study and provide useful guidance on what methodological and analytical information should be included in a research paper (Munafò et al., 2017 ). Such guidelines aim to ensure sufficient information is provided to enable replication and promote transparency (Munafò et al., 2017 ). Journal publishers are now beginning to outline what open science practices should be reported in articles. For example, from July 2021, when submitting a paper for review in one of the American Psychological Association journals, the authors are now required to state whether their data will be openly shared and whether or not their study was pre-registered. In a bullying context, Smith and Berkkun ( 2020 ) have highlighted that important contextual data is often missing from publications and recommend, for example, that the gender and age of participants alongside the country and date of data collection should be included as standard in papers on bullying in school.

Recommendations:

Researchers to start to share all research materials openly using an online repository. Box 3 provides some useful guidance on how to support the open sharing of research materials.

Journal editors and publishers to further promote the open sharing of research material.

Researchers to follow the recommendations set out by Smith and Berkkun ( 2020 ) and follow a set of reporting standards when reporting bullying studies.

Reviewers be mindful of Smith and Berkkun ( 2020 ) recommendations when reviewing bullying papers.

Box 3 Useful Resources on Openly Sharing Research Materials & Reporting Standards

Banks et al. ( 2019 ) provide a helpful overview of open science practices, alongside a set of recommendations for ensuring research is more open.

Meyer ( 2018 ) provides some useful guidance on managing the ethical issues of openly sharing data.

The Equator Network ( https://www.equator-network.org/reporting-guidelines/ ) is a useful resource for the sharing of different reporting standards, for example, the PRISMA guidelines for systematic reviews and STROBE standards for observational studies.

The Foster website is an online e-learning portal with a wealth of resources to help researchers develop open science practices https://www.fosteropenscience.eu/ , including sharing resources and pre-prints.

The Open Science Framework has resources to support open science practices and to use their platform https://www.cos.io/products/osf .

Smith and Berkkun ( 2020 ) provide a review of contextual information reported in bullying research papers and offer recommendations on what information to include.

The PsyArXiv https://psyarxiv.com and SocArXiv https://osf.io/preprints/socarxiv repositories accept pre-print publications in psychology and sociology.

Replication Studies

Replicated findings increase confidence in the reliability of that finding, ensuring research findings are robust and enabling science to self-correct (Cook et al., 2018 ; Drotar, 2010 ). Replication reflects the ability of a researcher to duplicate the results of a prior study with new data (Goodman et al., 2018 ). There are different forms of replication that can be broadly categorised into two: those that aim to recreate the exact conditions of an earlier study (exact/direct replication) and those that aim to test the same hypotheses again using a different method (conceptual replication) (Schmidt, 2009 ). Replication studies are considered fundamental in establishing whether study findings are consistent and trustworthy (Cook et al., 2018 ).

To date, few replication studies have been conducted on bullying in schools. A Web of Science search using the Boolean search term bully* alongside the search term “replication” identified two replication studies (Berdondini & Smith, 1996 ; Huitsing et al., 2020 ). Such a small number of replications may reflect concerns regarding the value of these and concerns about how to conduct such work when data collection is so time and resource-intensive. In addition, school gatekeepers are themselves interested in novelty and addressing their own problems and may be reluctant to participate in a study which has “already been done”. One possible solution to this challenge is to increase the number of large-scale collaborations among bullying researchers (e.g. multiple researchers across many sites collecting the same data). Munafò et al. ( 2017 ) highlight the benefits of collaboration and “team science” to build capacity in a research project. They argue that greater collaboration through team science would enable researchers to undertake higher-powered studies and relieve the pressure on single researchers. Such projects also have the benefit of increasing generalisability across settings and populations.

Undertake direct replications or, as a more manageable first step, include aspects of replication within larger studies.

Journal editors to actively promote the submission of replication studies on school bullying.

Journal editors, editorial panels, and reviewers to recognise the value of replication studies rather than favouring new or novel findings (Box 4 ).

Box 4 Useful Resources on Replication Studies

Brandt et al. ( 2014 ) provide a useful step by step guide on conducting replication studies, including a registration template form for pre-registering a replication study (available here: https://osf.io/4jd46/ ).

Coyne et al. ( 2016 ) discuss the benefits of replication to research in educational research (with a particular focus on special education).

Duncan et al. ( 2014 ) discuss the benefits of replication to research in developmental psychology.

Pre-Registration

Pre-registration requires researchers to set out, in advance of any data collection, their hypotheses, research design, and planned data analysis (van’t Veer & Giner-Sorolla, 2016 ). Pre-registering a study reduces the number of researcher degrees of freedom as all decisions are outlined at the start of a project. However, to date, there have been few pre-registered studies in bullying. There are two forms of pre-registration: the pre-registration of analysis plans and registered reports. In a pre-registered analysis plan, the hypotheses, research design, and analysis plan are registered in advance. These plans are then stored in an online repository (e.g. the Open Science Framework (OSF) or AsPredicted website), which is then time-stamped as a record of the planned research project (van’t Veer & Giner-Sorolla, 2016 ). Registered reports, however, integrate the pre-registration of methods and analyses into the publication process (Chambers et al., 2014 ). With a registered report, researchers can submit their introduction and proposed methods and analyses to a journal for peer review. This creates a two-tier peer-review process, where the registered reports can be accepted in principle or rejected in the first stage of review, based on the rigour of the proposed methods and analysis plans rather than on the findings of the study (Hardwicke & Ioannidis, 2018 ). In the second stage of the review process, the authors then submit the complete paper (at a later date after data have been collected and analyses completed), and this is also reviewed. The decision to accept a study is therefore explicitly based on the quality of the research process rather than the outcome (Frankenhuis & Nettle, 2018 ) and in practice, almost no work is ever rejected following an in-principal acceptance at stage 1 (C. Chambers, personal communication, December 11, 2020). At the time of writing, over 270 journals accept registered reports, many of which are directly relevant to bullying researchers (e.g. Developmental Science, British Journal of Educational Psychology, Journal of Educational Psychology).

Pre-registration offers one approach for improving the validity of bullying research. Employing greater use of pre-registration would complement other recommendations on how to improve research practices in bullying research. For example, Volk et al. ( 2017 ) propose a “bullying research checklist” (see Box 5 ).

Box 5 Volk et al. ( 2017 ) Bullying Research Checklist ( reproduced with permission )

State and justify your chosen definition of bullying.

Outline the theoretical logic underlying your hypotheses and how it pertains to your chosen definition and program of research/intervention.

Use one's logic model and theoretical predictions to determine which kind of measurements are most appropriate for testing one's hypotheses. There is no gold standard measure of bullying, but be aware of the strengths and weaknesses of the different types of measures. Where possible, use complementary forms of measurement and reporters to offset any weaknesses.

Implement an appropriate research or intervention design (longitudinal if possible) and recruit an appropriate sample.

Reflect upon the final product, its associations with the chosen logic model and theory, and explicitly discuss important pertinent limitations with a particular emphasis on issues concerning the theoretical validity of one's findings.

Volk et al.’s ( 2017 ) checklist highlights the importance of setting out in advance the definition of bullying, alongside the theoretical underpinnings for the hypotheses.

Pre-Registering Quantitative Studies

The pre-registration of quantitative studies requires researchers to state the hypotheses, method, and planned data analysis in advance of any data collection (van’t Veer & Giner-Sorolla, 2016 ). When outlining the hypotheses being tested, researchers are required to outline the background and theoretical underpinning of the study. This reflects the importance of theoretically led hypotheses (van’t Veer & Giner-Sorolla, 2016 ), which are more appropriately tested using NHST and inferential statistics in a confirmatory rather than exploratory design (Wagenmakers et al., 2012 ). Requiring researchers to state their hypotheses in advance of any data collection adheres to the confirmatory nature of inferential statistics and reduces the risk of HARKing (van’t Veer & Giner-Sorolla, 2016 ). Following a description of the hypotheses, researchers outline the details of the planned method, including the design of the study, the sample, the materials and measures, and the procedure. Information on the nature of the study and how materials and measures will be used and scored are outlined in full. Researchers are required to provide a justification for and an indication of the desired sample size.

The final stage of the pre-registration process requires researchers to consider and detail all steps of the data analysis process. The data analysis plan should be outlined in terms of what hypotheses are tested using what analyses and any plans for follow-up analysis (e.g. post hoc testing and any exploratory analyses). Despite concerns to the contrary (Banks et al., 2019 ; Gonzales & Cunningham, 2015 ), the aim of pre-registration is not to devalue exploratory research, but rather, to make more explicit what is exploratory and what is confirmatory (van’t Veer & Giner-Sorolla, 2016 ). While initially, the guidance on pre-registration focused more on confirmatory analyses, more recent guidance considers how researchers can pre-register exploratory studies (Dirnagl, 2020 ), and make a distinction between confirmatory versus exploratory research in the publication process (McIntosh, 2017 ). Irrespective of whether confirmatory or exploratory analyses are planned, pre-registering an analysis reduces the risk of p -hacking (van’t Veer & Giner-Sorolla, 2016 ). A final point, often a concern to those unfamiliar with open science practices, is that a pre-registration does not bind a researcher to a single way of analysing data. Changes to plans are entirely acceptable when they are deemed necessary and are described transparently.

Pre-Registering Qualitative Studies

Pre-registration of qualitative studies is still relatively new (e.g. Kern & Gleditsch, 2017a , b ; Piñeiro & Rosenblatt, 2016 ). This is because most of the work uses inductive and hypothesis-generating approaches. Coffman and Niederle ( 2015 ) argue that this hypothesis-generation is one of the most important reasons why pre-registering qualitative work is so important. This could help distinguish between what hypotheses are generated from the data and which were hypotheses conceptualised from the start. Therefore, it could even be argued that pre-registering qualitative research encourages exploratory work. Using pre-registration prior to a hypothesis-generating study will also help with the internal validity of this same study, as it will be possible to have a sense of how the research evolved from before to post data collection.

Using investigator triangulation, where multiple researchers share and discuss conclusions and findings of the data, and reach a common understanding, could improve the trustworthiness of a qualitative study (Carter et al., 2014 ). Similarly, where establishing intercoder reliability is appropriate, the procedures demonstrating how this is achieved can be communicated and recorded in advance. One example of this would be the use of code books. When analysing qualitative data, developing a code book that could be used by all the coders could help with intercoder reliability and overall trustworthiness (Guest et al., 2012 ). These are elements that could be considered in the pre-registration process by clearly outlining if intercoder reliability is used and, if so, how this is done. To improve the transparency of pre-registered qualitative work, it has also been suggested that researchers should clearly state whether, if something outside the scope of the interview comes to light, such novel experiences will also be explored with the participant (Haven & Van Grootel, 2019 ; Kern & Gleditsch, 2017a , b ). Issues of subjectivity, sometimes inherent to qualitative work, can be reduced as a result of pre-registering because it allows the researcher to clearly consider all the elements of the study and have a plan before data collection and analysis, which reduces levels of subjectivity.

Kern and Gleditsch ( 2017a , b ) provide some practical suggestions on how to use pre-registration with qualitative studies. For example, when using in-depth interviews, one should make the interview schedule and questions available to help others to comprehend what the participants were asked. Similarly, they suggest that all recruitment and sampling strategy plans should be included to improve transparency (Haven & Van Grootel, 2019 ; Kern & Gleditsch, 2017a , b ). Piñeiro and Rosenblatt ( 2016 ) provide an overview of how these pre-registrations could be achieved. They suggested three main elements: conceptualisation of the study, theory (inductive or deductive in nature), and design (working hypothesis, sampling, tools for data collection). More recently, Haven and Van Grootel ( 2019 ) highlighted a lack of flexibility in the existing pre-register templates to adapt to qualitative work, as such, they adapted an OSF template to a qualitative study.

Integrating Participatory Research Methods into Pre-Registration

Participatory research methods (PRMs) aim to address power imbalances within the research process and validate the local expertise and knowledge of marginalised groups (Morris, 2002 ). The key objective of PRM is to include individuals from the target population, also referred to as “local experts”, as meaningful partners and co-creators of knowledge. A scoping review of PRM in psychology recommends wider and more effective use (Levac et al., 2019 ). Researchers are calling specifically for youth involvement in bullying studies to offer their insight, avoid adult speculation, and assist in the development of appropriate support materials (O’Brien, 2019 ; O’Brien & Dadswell, 2020 ). PRM is particularly appropriate for research with children and young people who experience bullying behaviours given their explicit, defined powerlessness. Research has shown that engaging young people in bullying research, while relatively uncommon, provides lasting positive outcomes for both researchers and participants (Gibson et al., 2015 ; Lorion, 2004 ).

Pre-registration has rarely been used in research undertaking a PRM approach. It is a common misconception that pre-registration is inflexible and places constraints on the participant-driven nature of PRM (Frankenhuis & Nettle, 2018 ). However, pre-registration still allows for the exploratory and subjective nature of PRM but in a more transparent way, with clear rationale and reasoning. An appropriate pre-registration method for PRM can utilise a combination of both theoretical and iterative pre-registration. Using a pre-registration template, researchers should aim to document the research process highlighting the main contributing theoretical underpinnings of their research, with anticipatory hypotheses and complementary analyses (Haven & Van Grootel, 2019 ). This initial pre-registration can then be supported using iterative documentation detailing ongoing project development. This can include utilising workflow tools or online notebooks, which show insights into the procedure of co-researchers and collaborative decision making (Kern & Gleditsch, 2017a , b ). This creates an evidence trail of how the research evolved, providing transparency, reflexivity, and credibility to the research process.

The Perceived Challenges of Pre-Registration.

To date, there have been few pre-registered studies in bullying. A Web of Science search using the Boolean search terms bully* peer-vict*, pre-reg*, and preregist* identified four pre-registered studies on school bullying (Kaufman et al., 2022 ; Legate et al., 2019 ; Leung, 2021 ; Noret et al., 2021 ). The lack of pre-registrations may reflect concerns that it is a difficult, rigid, and time-consuming process. Reischer and Cowan ( 2020 ) note that pre-registration should not be seen as a singular time-stamped rigid plan but as an ongoing working model with modifications. Change is possible so long as this is clearly and transparently articulated, for example, in an associated publication or in an open lab notebook (Schapira et al., 2019 ). The move to pre-registering a study requires a change in workflow rather than more absolute work. However, this early and detailed planning (especially concerning analytical procedures) can improve the focus on the quality of the research process (Ioannidis, 2008 ; Munafò et al., 2017 ).

The Impact of Pre-Registration.

The impact of pre-registration on reported effects can be extensive. The pre-registration of funded clinical trials in medicine has been a requirement since 2000. In an analysis of randomised control trials examining the role of drugs or supplements for intervening in or treating cardiovascular disease, Kaplan and Irvin ( 2015 ) identified a substantial change in the number of significant effects reported once pre-registration was introduced (57% reported significant effects prior to the requirement but and only 8% after). More recently, Scheel et al. ( 2021 ) compared the results of 71 pre-registered studies in psychology with the results published in 152 studies that were not pre-registered. They found that only 44% of the pre-registered studies reported a significant effect, compared to 96% of studies that were not pre-registered. As a result, the introduction of pre-registration has increased the number of null effects reported in the literature and presents a more reliable picture of the effects of particular interventions.

When conducting your next research study on bullying, consider pre-registering the study.

Journal editors and publishers to actively encourage registered reports as a submission format.

The Benefits of Open Science for Researchers

Employing more open science practices can often be challenging, in part because they force us to reconsider methods that are already “successful” (often synonymous with “those which result in publication”). Based on our own experience, this takes time and is best approached by beginning small and building up to a wider application of the practices we have outlined in this article. Alongside increasing the reliability of research, open science practices are associated with several career benefits for the researcher. Articles which use open science practices are more likely to be accepted for publication, are more visible, and are cited more frequently (Allen & Mehler, 2019 ). Open science can also lead to the development of more supportive networks for collaboration (Allen & Mehler, 2019 ). In terms of career advancement, Universities are beginning to reward engagement with science principals in their promotion criteria. For example, the University of Bristol (UK) will consider open research practices such as data sharing and pre-registration in promotion cases in 2020–21. Given that formal recognition such as this has been recommended by the European Union for some time (O’Carroll et al., 2017 ), it is likely to be an increasingly important part of career progression in academia (Box 6 ).

Box 6 Pre-Registration

van't Veer and Giner-Sorolla ( 2016 ) provide a clear overview of the pre-registration process and provide a template for the pre-registration of studies.

Center for Open Science YouTube channel https://www.youtube.com/watch?v=PboPpcg6ik4 includes several webinars on pre-registration and the replication crisis. The OSF website also includes a number of pre-registration templates for researchers to use https://osf.io/zab38/wiki/home/?view , and provide a list of journals that accept registered reports https://www.cos.io/initiatives/registered-reports

Haven and Van Grootel ( 2019 ) review the issues around pre-registering of qualitative work and adapted an existing pre-registering OSF template to suit these types of studies.

This paper sought to clarify the ways in which bullying research is undermined by a failure to engage with open science practices. It highlighted the potential benefits of open science for the way we conduct research on bullying. In doing so, we aimed to encourage the greater use of open science practices in bullying research. Given the importance of this for the safety and wellbeing of children and young people, the transparency and reliability of this research is paramount and is enhanced via greater use of open science practices. Ultimately, researchers working in the field of bullying are seeking to accurately understand and describe the experiences of children and young people. Open science practices make it more likely that we will achieve this goal and, as a result, be well-placed to develop and implement successful evidence-based intervention and prevention programs.

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Department of Psychology, Glasgow Caledonian University, Cowcaddens Road, Glasgow, G4 0BA, UK

Simon C. Hunter & Rebecca Johnson

Graduate School of Education, University of Western Australia, Perth, Australia

Simon C. Hunter

School of Psychological Sciences and Health, University of Strathclyde, Graham Hills Building, 40 George Street, Glasgow, G1 1QE, UK

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Noret, N., Hunter, S.C., Pimenta, S. et al. Open Science: Recommendations for Research on School Bullying. Int Journal of Bullying Prevention 5 , 319–330 (2023). https://doi.org/10.1007/s42380-022-00130-0

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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.

The American Psychological Association, in Washington, D.C., is the largest scientific and professional organization representing psychology in the United States. APA's membership includes more than 122,500 researchers, educators, clinicians, consultants and students. Through its divisions in 54 subfields of psychology and affiliations with 60 state, territorial and Canadian provincial associations, APA works to advance the creation, communication and application of psychological knowledge to benefit society and improve people's lives.

Jim Sliwa (202) 336-5707

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Bullying and cyberbullying: a bibliometric analysis of three decades of research in education

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Introduction

Previous bibliometric research on bullying or cyberbullying, conclusions, assumptions and limitations, future work, disclosure statement, additional information.

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Bullying is a topic of international interest that attracts researchers from various disciplinary areas, including education. This bibliometric study aims to map out the landscape of educational research on bullying and cyberbullying, by performing analyses on a set of Web of Science Core Collection-indexed documents published between 1991-2020. The main findings of the study provide data regarding (1) the evolution of publication trends over the past 30 years and the characteristics of the documents; (2) the most productive countries and institutions; (3) influential journals, authors, and articles in the area; (4) the collaboration patterns and the conceptual structure of educational research on bullying and cyberbullying. Based on the comprehensive overview of the educational research on the topic, suggestions for future work are provided.

• Educational research
• cyberbullying
• bibliometrics
• Web of Science

Bullying has been considered “one of the most outstanding topics in educational research” (Espinosa, Citation 2018 ), a public health problem among children and adolescents (Chester et al., Citation 2015 ), and also a reason for concern in schools and communities (Bradshaw, Citation 2015 ). According to the PISA 2018 report, on average, 23% of students reported being bullied at least a few times a month across Organisation for Economic Co-operation and Development (OECD) countries, which has serious consequences for students’ lives (OECD, Citation 2019 ). In the 2030 Agenda for Sustainable Development there are calls for providing safe, non-violent, inclusive, effective learning environments, for peaceful and inclusive societies (United Nations, Citation 2015 ). Bullying is seen as a barrier to ensuring inclusive and quality education for everyone (Wang & Florian, Citation 2019 ). The evolution of bullying is different from one country to another. Thus, some have seen a decline in the prevalence of bullying whereas others experience an increase in the prevalence of bullying or no significant change (UNESCO, Citation 2019 ).

Bullying, as a form of school violence, is not a new issue. It was brought to the attention of the international academic community in the 1970s through research conducted by the Norwegian psychologist Dan Olweus. Bullying was defined as intentionally harmful, aggressive behaviour that is repetitive and occurs within a relationship characterized by an imbalance of strength (Olweus, Citation 1993 ). The definition provided by Olweus has been the basis of numerous studies in recent decades on this phenomenon (Volk et al., Citation 2014 ). There have been some controversies over the qualification of repetitiveness of aggressive behaviour. Some researchers consider that a single act of aggression could be very harmful to a person especially in cyberbullying (Slonje & Smith, Citation 2008 ). An updated definition of bullying, which removes the criterion of repetition and supports a goal-directed behaviour perspective, is suggested in a study by Volk et al. ( Citation 2014 ).

A common form of aggression in recent years is cyberbullying, also known as electronic or internet bullying. Cyberbullying was defined as “any behaviour performed through electronic or digital media by individuals or groups that repeatedly communicates hostile or aggressive messages intended to inflict harm or discomfort on others” (Tokunaga, Citation 2010 , p. 278). The Internet offers anonymity, reduces the control of aggressive, hostile, harmful incidents, and makes the victims accessible all the time through electronic devices (Bonanno & Hymel, Citation 2013 ). The victims of cyberbullying are, in many cases, victims of bullying too (Shin et al., Citation 2016 ; Tokunaga, Citation 2010 ). Olweus and Limber ( Citation 2018 ) recommend that cyberbullying be seen as a form of bullying, while other researchers consider cyberbullying to be distinct from traditional bullying (Boulton et al., Citation 2012 ).

The negative effects of bullying are multiple, such as victimization, depression, suicide, stress, anxiety etc. and are described by a number of specialists in various studies (Gunther et al., Citation 2016 ; Hinduja & Patchin, Citation 2019 ; Klomek et al., Citation 2007 ; Kowalski et al., Citation 2014 ; Uusitalo-Malmivaara & Lehto, Citation 2016 ). The phenomenon cannot be explained exclusively in terms of individual characteristics since they are heavily influenced by interaction with peers, families, teachers, community, and society (Acquah et al., Citation 2016 ; Attar-Schwartz et al., Citation 2019 ; Bauman & Hurley, Citation 2005 ; Bjereld et al., Citation 2019 ; Cunningham et al., Citation 2016 ; Rajaleid et al., Citation 2020 ; Swearer & Hymel, Citation 2015 ). The complexity of this phenomenon makes the fight against bullying very difficult, calling for cooperative efforts to prevent and diminish it (Bradshaw, Citation 2015 ; Fekkes et al., Citation 2004 ; Swearer & Hymel, Citation 2015 ).

The occurrence of bullying and cyberbullying among children and adolescents, and the worries related to their negative implications have led to an increasing number of publications focusing on the topic emerging from different areas of research, including education sciences. With such abundant literature, it can be overwhelming and difficult to have a clear overview of the subject matter. In addition to the systematic review of the literature, bibliometric analysis may be useful in structuring research literature and reaching a deeper understanding of a research area (Fellnhofer, Citation 2019 ; Keathley-Herring et al., Citation 2016 ). Cobo et al. ( Citation 2011a ) defined bibliometrics as a set of methods used to study or measure texts and information, especially in large datasets. Bibliometrics is based on the application of the quantitative approach for the description, evaluation, and monitoring of published research (Zupic & Cater, Citation 2015 ). It is used in various scientific fields (Ellegaard & Wallin, Citation 2015 ) to track their development, the organizations and authors that contribute to their evolution, and to identify collaboration networks and trends. This approach is attractive, objective, and popular for the evaluation of the research (Noyons et al., Citation 1999 ) and by covering large amounts of data over long periods of time, it can lead to a comprehensive picture of the development of a domain (Hernandez-Torrano & Ibrayeva, Citation 2020 ; Zupic & Cater, Citation 2015 ). Access to various online databases that store impressive amounts of scientific papers, as well as the diversity of tools available for conducting bibliometric analyses (Moral-Munoz et al., Citation 2020 ; Zupic & Cater, Citation 2015 ) help researchers to explore scholarly literature from various perspectives.

In recent years, educational researchers manifested a growing interest in the use of bibliometric methods for different purposes. Thus, some researchers have been interested in measuring research performance in the field of education, focusing on aspects such as the internationalization of European educational research (Aman & Botte, Citation 2017 ), the evolution of topics in the field (Huang et al., Citation 2020 ), the analysis of highly cited articles in the educational research (Ivanovic & Ho, Citation 2019 ), and the analysis of research performance of individual researchers from certain countries (Diem & Wolter, Citation 2013 ; Gulmez et al., Citation 2020 ). Other researchers have used bibliometric approaches to analyse specific topics in education, such as creativity (Hernandez-Torrano & Ibrayeva, Citation 2020 ), inclusive education (Cretu & Morandau, Citation 2020 ; Hernández-Torrano, Ibrayeva, et al., Citation 2020 ), educational administration (Hallinger & Kovacevic, Citation 2019 ), entrepreneurship education (Fellnhofer, Citation 2019 ), classroom dialogue (Song et al., Citation 2019 ), gamification (Marti-Parreno et al., Citation 2016 ; Swacha, Citation 2021 ), etc. Bibliometric studies have been conducted to evaluate the performance of scientific journals in the field, too. For instance, to celebrate the 50th anniversary of the British Journal of Educational Technology, Chen, Zou, and Xie ( Citation 2020 ) provided a retrospective of it based on the analysis of its publications. Another recent study illustrated the research topics and trends in education technologies, by analysing the publications in the journal Computers & Education over forty years (Chen, Zou, Cheng, et al., Citation 2020 ). These studies have revealed the multiple possibilities regarding the use of bibliometric methods for the quantitative analysis of educational research.

RQ1: How has educational research on bullying and cyberbullying evolved?

RQ2: Which countries and institutions bring significant contributions to the study of bullying and cyberbullying in education?

RQ3: What are the most influential journals and leading authors in the field of education sciences?

RQ4: What are the collaboration patterns and the intellectual structure of research on bullying and cyberbullying in education?

RQ5: What is the conceptual structure of research on bullying and cyberbullying in education?

The increasing body of scientific literature on bullying and cyberbullying has generated a need for quantitative analysis. Thus, some recent bibliometric studies have focused on mapping research literature on bullying or cyberbullying from various perspectives. For instance, in their bibliometric study, Velasco et al. ( Citation 2016 ) described Spanish research on bullying between 1990-2015, by focusing on papers indexed in the Scopus database. Two other bibliometric studies analysed scientific output in Latin American countries on bullying (Villanueva et al., Citation 2020 ), and on bullying and cyberbullying (Herrera-López et al., Citation 2018 ). Some authors have been interested in analysing the scientific production on certain forms of bullying, such as racial or ethnic bullying among teens (Cabrera et al., Citation 2019 ), or verbal bullying in schools (Kurniasih et al., Citation 2020 ). Three other recent bibliometric studies focusing on cyberbullying have been carried out in Spain. Esteban et al. (2020), mapped out the Spanish scientific production on cyberbullying, by analysing 178 papers indexed in WoS database in Psychology or Educational Research. To document cyberbullying, Lopez-Meneses et al. ( Citation 2020 ) looked at Scopus-indexed research work at a global level from the perspective of its socio-economic implications in the educational context and found that Social Sciences, Psychology, Medicine, and Computer Science were the most influential fields. A growing interest in investigating cyberbullying, particularly in the fields of Psychology and Educational Research, was also identified in another bibliometric study based on documents included in WoS (González-Moreno et al., Citation 2020 ).

The review of bibliometric studies on bullying and/or cyberbullying allowed us to reach some important conclusions. Firstly, we noticed that bullying and/or cyberbullying have captured the interest of researchers. This becomes evident from the fact that each bibliometric study is based on the analysis of hundreds or even thousands of research publications. While some studies focused on the mapping of the research produced in a single country or in countries in the same region, others offer a global perspective but focus on specific aspects of the phenomenon, such as certain types of bullying, or cyberbullying-related factors. Secondly, WoS and Scopus are the most widely used databases for document collection and analysis of publications on bullying or cyberbullying. It seems that most bibliometric analyses have in common their data sources (Mongeon & Paul-Hus, Citation 2016 ). Thirdly, Spanish authors have manifested an interest in developing bibliometric approaches. Of the eight bibliometric studies included here, five are authored or co-authored by Spanish researchers. Finally, all these bibliometric studies were published between 2016 and 2020, which supports the idea of the relatively recent use of bibliometric approaches in the field of education (Hernández-Torrano, Somerton, et al., Citation 2020 ).

Despite these initiatives, the bibliometric research on bullying or cyberbullying is quite fragmented. An overall image of the evolution of research on both phenomena in educational settings is not available. The present study complements the previous bibliometric works in many ways, namely (1) it provides an integrated approach to both bullying and cyberbullying research in the field of education sciences; (2) it considers publications on various forms of bullying at different ages and in different educational contexts; (3) it offers not only a national/regional perspective but also a global overview on the subject matter for an extended period of thirty years.

The search strategy

The mapping of bullying and cyberbullying-focused research in education was based on data extracted from Web of Science Core Collection (WoSCC) database of Clarivate Analytics. The decision to use this database as a source and support for the present study was influenced by the possibilities of refining the results on a specific area of research, which is education and educational research (E&ER) in this case. In Scopus, this would not have been possible, as education is included in the field of social sciences, which would have generated a larger sample of papers, but further away from the field of interest. Furthermore, Wang and Waltman ( Citation 2016 ) demonstrated higher accuracy in the journal classification system in WoS in comparison to Scopus. Hence the process of searching for literature was carried out during the first weeks of 2021 and the final data were downloaded on 25th January 2021.

Figure 1. Search strategy in the WoSCC (Data available on 25th January 2021).

Data analysis

The bibliometric analyses were performed by following two main procedures, namely performance analysis and science mapping (Gutierrez-Salcedo et al., Citation 2017 ; Noyons et al., Citation 1999 ). The performance analysis focuses on the volume and growth trajectory of publications over time, type of documents and citation data, the contribution of countries, institutions, or authors, and the impact of their work. We conducted a descriptive analysis using WoS tools (Analyze Results and Creation Citation Report). The analyses were performed on the extended sample for RQ1, RQ2, and on articles only for RQ3, RQ4, RQ5.

Science mapping uses bibliometric methods to determine the structure of a research area by grouping documents, authors, journals, and words (Noyons et al., Citation 1999 ; Zupic & Cater, Citation 2015 ). The result is a graphic description in the form of science maps or networks of the clusters that emerge (Zupic & Cater, Citation 2015 ). The map construction process using VOSviewer comprises three main steps: first, a similarity matrix is calculated, then a map is constructed, and during the last step the map is translated, rotated, and reflected. These transformations are the most important operations that ensure consistent results for VOSviewer (van Eck & Waltman, Citation 2010 ). Association strength was used for data normalization. We used the full counting method for all analyses, which is normally used by researchers to construct bibliometric networks (Perianes-Rodriguez et al., Citation 2016 ). The clusters are constructed starting from the strength of the links between units of analysis.

For science mapping, three types of analysis were performed using VOSviewer software (version 1.6.16): co-authorship, co-citation, and co-occurrence of keywords. All these are bibliometric techniques based on the co-occurrence data principle (van Eck & Waltman, Citation 2009 ). Bibliometric networks were created to see the collaboration patterns and the conceptual structure (Zupic & Cater, Citation 2015 ) of scientific research on bullying and cyberbullying. Co-authorship analysis offered evidence of networks of authors. Authors and countries that have contributed to the same papers are connected through links that reveal the intellectual structure of a field. They are grouped in clusters marked with different colours, depending on the intensity of their collaboration. These networks suggest related content and are based on the frequency with which units of analysis (author, paper, journal) are cited together (Small, Citation 1973 ). In the co-citation analysis, authors or papers that occur in the references of the same papers are grouped in clusters depending on the intensity of co-occurrence. To identify the main themes from a research field, co-word analysis is a very useful bibliometric technique and could be explored using different topic modelling approaches (Chen, Zou, Cheng, et al., Citation 2020 ; Cobo et al., Citation 2011a ). In our study, co-occurrence of keywords produced an image on concepts that appear in the same keyword list and reflect the research interests of the authors. The items or units of analysis included in our science mapping procedure (authors, countries, references, keywords) are visualized on a map with nodes and lines that suggest the intensity of connections between these items (van Eck & Waltman, Citation 2010 ).

The evolution of E&ER on bullying and cyberbullying over time

The volume and growth trajectory.

As concerns the extended sample, it turned out that a substantial number of publications ( n  = 2119) emerged in the E&ER area between 1991 and 2020. This indicates that the field of E&ER is the second in the ranking by the number of documents on the topic, being surpassed only by Psychology, which records an impressive number of over 5000 documents.

Figure 2. Time perspective of the bullying and cyberbullying documents indexed on E&ER in WoSCC. Note: B = Bullying; CB = Cyberbullying.

The initial stage, the first decade (1991-2000) during which 40 documents were published, representing 1.88% of the total number of publications.

The growth stage, the second decade (2001-2010), when 260 publications were identified, representing 12.26% of the total.

The accelerating growth stage, the third decade (2011-2020), when 1819 documents were published; this stage accounted for 85.84% of the total publications over the last three decades.

The most productive decade was between 2011 and 2020 with a peak in the last six years (2015-2020), when 65% of the total publications were recorded and when the yearly number of documents did not drop below 200. A sharp peak was recorded in 2019 ( n  = 280). The number of authors increased sharply from one decade to the next from 61 contributors in the first decade and 519 in the second decade, to almost 4,000 in the third decade.

Types of documents and citation data

Thirteen document types were published in 691 source titles (journals, conference volumes, books). The most numerous were articles ( n  = 1633, 77.06% of the total documents), proceedings papers ( n  = 303, 14.29%), and book chapters ( n  = 214, 10.09%). Next come book reviews ( n  = 62, 2.92%), reviews ( n  = 52, 2.45%), editorials ( n  = 51, 2.4%), and early access documents ( n  = 47, 2.21%). Other document types (books, meeting abstracts, corrections, etc.) made up under 1% of all documents. Some publications (especially the book chapters) have been categorized as more than one document type. This explains why the sum of percentages was higher than 100%.

Over the three decades, all these document types have accumulated 23,104 citations. The number of citations increased considerably from one decade to the next as follows: the first decade, 158 citations (0.68% of the total citations), the second decade, 2049 citations (8.86%), the third decade, 20,897 citations (90.44%). On a yearly basis, most citations occurred in 2020 (3947 citations, 17% of the total). Articles account for 20,836 citations, making up 90% out of the total citations, which proves their impact on the research output. Proceedings papers and editorial materials are also cited, but in smaller proportions (3%). A high average citation per item (29.40), in the case of the reviews, was identified. However, as their number is small ( n  = 52), compared to the articles, they contribute only 6.61% to the total citations.

As concerns the language, English was the most widely used language in educational publications about bullying and cyberbullying ( n  = 1906, 89.94%), followed by Spanish ( n  = 148, 6.98%) and Portuguese ( n  = 33, 1.55%). The remaining documents were published in other languages (under 1% each), such as Turkish, Russian, French, German, Chinese. Only 25% of the documents were published in the open access regime. Of these, over 80% were recorded between 2015 and 2020.

Productive countries and institutions

Over the years, the number of countries contributing to educational publications on bullying and cyberbullying has increased (we refer here to the extended sample). Thus, between 1991 and 2000 only six countries contributed with publications mostly coming from the United Kingdom (UK), whereas over the next decade (2001-2010), their number increased to 34, most of them being published by authors from the USA. Over the past decade (2011-2020), 80 countries contributed educational research on bullying and cyberbullying, and the USA has consolidated its leading position. The top countries with contributions over 100 publications each are: the USA ( n  =  652, 30.76%), the UK ( n  = 247, 11.65%), Spain ( n  = 189, 8.91%), Australia ( n  = 169, 7.97%), and Turkey (102, 4.81%). In the case of the UK, papers from England, Scotland, Northern Ireland, and Wales were considered. The researchers in the top five countries have produced 64.13% of the E&ER on the topic. Among them, the USA were in top position, contributing almost a third to the entire research literature. Other contributions (under 100 documents/country) came from Canada ( n  = 91, 4.29%), Sweden ( n  = 55, 2.59%), South Africa ( n  = 44, 2.07%), Brazil ( n  = 42, 1.98%), and Finland ( n  = 41, 1.93%). A distinct analysis of E&ER publications focusing on cyberbullying ( n  = 497) shows Spain in the first place ( n  = 98, 19.71%), followed closely by the USA ( n  = 95, 19.11%).

Over 1600 organizations contributed to educational research related to bullying and cyberbullying. The University of London is the most productive organization with 39 documents accounting for 1.84% of all publications. It is followed by the State University System of Florida ( n  = 32, 1.51%), the University of Illinois System ( n  = 30, 1.41%), Linkoping University ( n  = 29, 1.36%), the University of Illinois Urbana Champaign ( n  = 28, 1.32%). The next positions are held by the Pennsylvania Commonwealth System of Higher Education ( n  = 26, 1.22%), the University of North Carolina (n = 25, 1.18%), the California State University System (n = 24, 1.13%), the University System of Georgia (n = 22, 1.03%), the Pennsylvania State University (n = 21, 0.99%). These top ten organizations generated 13% of the total publications.

Influential journals, articles, and authors

Table 1. productive journals of e&er on bullying and cyberbullying..

We found out that a small number of journals, which were assigned to the E&ER category in WoS, published a considerable amount of research. More precisely, the six journals presented in Table 1 , published 20.14% of the total number of articles indexed in the E&ER area. The Journal of School Violence ranked first place by the number of published articles, but those published by the Journal of School Health , which comes second, accumulated the highest number of citations. These two journals can be considered core journals on bullying and cyberbullying educational research and together they host 12.85% of the total number of articles. Educational Research journal ranks third in the number of publications, and second in the number of citations. Papers published in the three journals received more than 30% of the total citations accumulated by all articles, which highlights the influence of these journals in the field. Other journals with contributions are presented in Table 1 .

Table 2. The most cited articles on bullying and cyberbullying in E&ER.

The top ten most cited articles were published during the first and the second decade (four articles and six articles respectively) of the three analysed. All these ten articles were cited in documents published in 2020 (receiving between 12 and 60 citations), which shows that they still have an influence on the research on the topic. Of all of these, the most cited in 2020 were the two works focused on cyberbullying with 62, and 28 citations respectively (Juvonen & Gross, Citation 2008 ; Patchin & Hinduja, Citation 2010 ). None of the articles published over the last decade are in this top list. It takes some years for articles to collect citations.

Table 3. The most prolific authors in E&ER area.

Collaboration patterns and the intellectual structure of e&er on bullying and cyberbullying.

Further research using VOSviewer software was conducted to answer RQ4. A co-authorship analysis helped us identify the collaboration patterns between the main actors in this research area. To establish the intellectual structure of the research produced on bullying and cyberbullying we used co-citation analysis at cited authors and cited references level.

Co-authorship on authors and countries

Figure 3. Network of co-authorship, authors level (minimum number of documents for each author, 5, n  = 53)

A large global network for E&ER on bullying and cyberbullying at author level cannot be identified. Some of the most prolific authors work in rather small networks, e.g. Christina Salmivalli (University of Turku), Li-Ming Chen (National Sun Yat Sen University), Sheri Bauman (University of Arizona). Out of 53 authors with at least five articles, five significant clusters occur, led by Donna Cross (University of Western Australia), Robert Thornberg (Linkoping University), Rosario Ortega-Ruiz (University of Cordoba), Marilyn A. Campbell (Queensland University of Technology), and Dorothy L. Espelage (University of North Carolina).

Figure 4. Network of co-authorship, countries level (minimum number of documents for each country, 10, n  = 30). Only countries that are connected are represented here ( n  = 29)

Co-citation analysis – cited authors and cited references

Figure 5. Network of co-citations-cited authors (minimum number of citations of an author, 150, n   = 29)

Co-citation analysis reflects the theoretical framework of the articles in our sample. The most influential authors whose works on bullying and cyberbullying have been co-cited in articles classified in the field of E&ER are Dan Olweus, Peter K. Smith, Christina Salmivalli, Dorothy L. Espelage, Robin M. Kowalski. Moreover, the most relevant paper ranked by co-citation across E&ER literature is the book of Dan Olweus, who is recognized as an authority in bullying studies.

The conceptual structure of E&ER on bullying and cyberbullying

Figure 6. Co-occurrence of author keywords. Minimum number of occurrences of a keyword, 7 ( n  = 102).

The ten clusters resulting from the keyword analysis revealed several topics of interest in bullying and cyberbullying. The first one (red cluster, 20 items) addresses bullying in the context of sexual discrimination, racism, social justice, and teacher education. The second cluster (green, 20 items) indicates the interest in bully behaviour, their victims (students with disabilities, especially with autism), and the need for inclusive education and intervention. Another topic that emerges from the third cluster (blue, 14 items) is related to identity, leadership, peer relationships and prevention in primary, secondary school, and university with the involvement of students, parents, and teachers. The fourth cluster (yellow, 12 items) brings together aspects related to qualitative research on school bullying focusing on bullying victimization and the mental and emotional health of children and adolescents as a public health issue, with an emphasis on risk behaviour. The fifth cluster (purple, 11 items) reveals another topic of interest, namely bullying prevention and victimization among adolescents in the context of depression, anxiety, loneliness, and suicide, as the most serious effect. A general theme discovered by our analysis (cluster six, light blue, 11 items) addresses children, diversity, education, family, social media, and risk. Studies focused on cyberbullying investigate this behaviour in higher education and among university students and are associated mainly with social networks, the internet, and self-esteem, as suggested in cluster seven (orange, eight items). A very important topic, which is less explored, is related to implementation, law, and policy. The results of all research should materialize in policies and strategies to be implemented in educational settings. The eighth cluster (brown, four items) addresses this issue and can be considered a good start, but it is important to be carried on. Isolated topics (cluster nine and cluster ten) are early childhood and aggression, and moral disengagement. The analysis of the clusters suggests variety in terms of research themes, as well as different degrees of interest on the part of researchers since some topics seem to be covered better than others that are marginal.

This study presents a bibliometric analysis of the research on bullying and cyberbullying in education by considering the publications indexed in WoS in the E&ER area over three decades (1991-2020). By choosing this quantitative research approach, we have been able to track a topic in a specific field for a certain period using bibliometric procedures. Thus, the performance analysis revealed its volume and growth trajectory, types of document and citation counts, productive countries and institutions, influential journals, articles, and authors. In addition, the science mapping allowed the identification of collaboration patterns among countries and authors, relevant references for this topic, and themes addressed by the authors. The bibliometric analysis reflects the characteristics of bullying and cyberbullying as areas of inquiry in education.

The chronological analysis of 2119 WoSCC-indexed documents revealed an important feature of educational research on bullying and cyberbullying, namely its exponential increase over the three decades. A remarkable increase could be noticed especially over the third decade, with a peak reached in 2019 when seven times more papers were published than in the entire first decade considered in the analysis. The growing trend of scientific production, especially in recent years, was also reported in the bibliometric study focusing on racial and ethnic bullying, carried out by Cabrera et al. ( Citation 2019 ). This drive has been fuelled by the increasing number of researchers apparent from one decade to the next, which reflects their awareness and concern that bullying has become an increasingly worrying issue in the field of education. However, a slight decrease in the number of publications was noticed in 2020. This may be due to the fact that at the time the database was set up for this study, only published works already indexed in WoS were taken into account. It could also be associated with the disruption of the activity of education researchers due to the coronavirus pandemic. On the other hand, as the COVID-19 pandemic forced many educational institutions around the world to switch to online activities in the spring of 2020, the students’ exposure to traditional bullying decreased considerably. For instance, in a recent study, a declining trend in school bullying rates has been identified among Canadian students during the pandemic compared to the pre-pandemic period, whereas cyberbullying rates dropped only slightly (Vaillancourt et al., Citation 2021 ). The dynamics of research focusing on the two issues during and after the pandemic remain to be discovered through further studies in various parts of the world.

Cyberbullying has become a topic of interest for researchers in the field of education, especially over the past decade, and has developed as part of research into bullying. Tokunaga ( Citation 2010 ) argues that research in traditional bullying has also informed cyberbullying research. The nature of recent literature on cyberbullying and the growing interest in this topic, especially by researchers in the fields of Psychology and Education, were also highlighted in another bibliometric study, based on data extracted from WoS (González-Moreno et al., Citation 2020 ). The interest of Spanish and American researchers in the issue of cyberbullying detected in the present study has been remarked upon in previous studies on the subject, too (González-Moreno et al., Citation 2020 ; Zych et al., Citation 2016 ). However, as research on cyberbullying is still young in the field of education, the level of scientific output is modest compared to that related to bullying, which has been in the attention of researchers for many years. Other authors also describe scientific production on cyberbullying in the educational field as “low and irregular” (Jimenez et al., Citation 2020 ). Given the increasing use of information and communication technologies in education and the risks associated with them, we appreciate that cyberbullying in education marks an area in full development that will capture the interest of researchers in the coming years.

By revealing the publication patterns, we could see diversity in the type of documents about bullying and cyberbullying in education. Journal articles remain the main way of disseminating research results to the scientific community, but other options (reviews, books, conference papers, etc.) should not be neglected either. The fact that English is the language of most publications reflects the dominance of the English-language journals over other languages in WoS (Mongeon & Paul-Hus, Citation 2016 ). The analysis of the extended sample indicated a variation of citation between publication types (Wallin, Citation 2005 ), with reviews and articles being more cited compared to conference papers and editorial materials. Since articles are the most numerous and account for the most citations, we can consider publications of this type as representing the main flow of research on bullying and cyberbullying and having the greatest impact on the field of research. As a matter of fact, citation frequency is a common indicator of the impact or influence of papers (Narin & Hamilton, Citation 1996 ). The growing trend in citations, for publications covering both bullying and cyberbullying, can be seen as an obvious signal for the dynamic development of educational research on the topic. This trend may also be related to the increase of open access publications in recent years. The transparency of data research is a great opportunity not only for investigators in terms of exploring the topic and advancing outcomes for such challenging issues, but also for education practitioners and policymakers who can benefit from the research to inform the approach to the phenomena in different educational settings. This reinforces the idea that the link between research and educational practice is essential in combating bullying (Swearer et al., Citation 2010 ).

It is noteworthy that the number of citations accumulated by the publications included in the sample comes only from papers covered in the WoSCC, which means that this is only a partial picture of their impact. As there are differences among databases in terms of citation counts and the number of publications (Agarwal et al., Citation 2016 ), the analysis of publications and their citation counts in other databases (Scopus, Google Scholar, etc.) could be considered to obtain a more comprehensive picture. At the same time, the findings do not exclude the possibility of some negative citations that could not be accounted for by the analyses performed in this study. This is a limitation of the traditional bibliometric tools (Agarwal et al., Citation 2016 ) that may affect the results discussed here.

In essence, the increase in the number of publications and citations over time indicates that bullying and cyberbullying have generated a distinct zone in the landscape of educational research, that will probably continue to expand as long as these issues continue to occur in different educational settings globally. Even though bullying and cyberbullying have become research topics in various parts of the world, most publications were written by researchers located in a few developed countries such as the USA, the UK, Spain, Australia, Canada, etc. The University of London leads in the ranking of the most productive organizations, but the dominant presence of American universities reinforces the USA's position as a leader in educational research on bullying and cyberbullying. Over the last decade, the geographical diversity has increased through the contributions of authors from countries in South America, Africa or Asia. There is still a lot of room for research to emerge from different regions of the world, particularly from countries where the prevalence of bullying is very high such as Brunei Darussalam, the Dominican Republic, Indonesia, Morocco, and the Philippines (OECD, Citation 2019 ). The international visibility of research emerging from these countries recorded by WoSCC seems to be low or non-existent. This situation may reflect differences between countries in terms of development, allocation of research funds, or research capacity. Therefore, a specific feature of educational research on the topic is its imbalanced distribution at a global level. This is consistent with the findings of another study conducted by Zych et al. ( Citation 2015 ). Since bullying is a problem that knows no borders and is not compatible with a healthy and sustainable education environment (Gomez-Galan et al., Citation 2021 ), it is necessary to research the phenomenon in various cultural, geographical, and socio-economic environments. This should be accompanied by enhancing international visibility especially for works coming from countries that are less represented in the research landscape on the topic. However, we draw attention to the fact that the findings discussed here must be viewed with caution and through the perspective provided by the used database. Publications of local or regional importance, those indexed in national or international databases other than WoSCC may reveal evidence for contribution to research output from different countries.

The journals’ analysis revealed that the number of those devoted to topics in education is quite limited. Only two journals stand out through the larger number of articles published, namely the Journal of School Violence and Journal of School Health. In both journals the presence of American researchers is dominant, and the articles published focus on investigating bullying or cyberbullying in school-related contexts. As there is no international peer-reviewed journal indexed by WoSCC exclusively dedicated to bullying, these two journals will likely continue to host future publications in the field. Educational researchers studying bullying or cyberbullying outside the school environment may consider publishing their work in specialized journals focusing on certain levels of education such as early education, higher education, adult education or in journals which provide a venue for broader educational topics, irrespective of education level. Publishing in interdisciplinary journals is another option available to educational researchers. Then, even if the number of researchers engaged with the topic has increased from one year to another, only 5% of the authors have published at least three articles or more, and the remaining 95% have fewer contributions, with one or two works per author. Based on these findings, we appreciate that there is a rather small core of journals and prolific authors in bullying and cyberbullying research in the field of education. This is another notable feature of educational research on the topic in question. Moreover, the results indicate that at least as far as this topic is concerned, education has benefitted from the contribution of some top researchers in Psychology, which remains the most prolific field on this subject. This finding is in agreement with the interdisciplinary and multidisciplinary nature of educational research (Huang et al., Citation 2020 ). Addressing such a complex phenomenon as bullying relies on the efforts of experts in related research fields.

The works of the most prolific authors illustrate the diversity of research interests and the multiple perspectives of addressing bullying and cyberbullying in education. School-related bullying during childhood and adolescence or among students with disabilities, social-emotional learning interventions, bullying and cyberbullying prevention and intervention, social and moral processes involved in bullying, bystander responses to bullying, cyberbullying among college students, perspectives of students, teachers, pre-service teachers, parents or school counsellors on bullying, cross-national comparisons in bullying, etc. are some of the directions that have been explored in their research. The absence of other researchers from the ranking of the most productive authors should not necessarily be interpreted as a result of lower or inconsistent interest in bullying or cyberbullying in education research. This may reflect the fact that researchers are at different stages of their careers. The established scientists are at an advantage when using bibliometrics (Agarwal et al., Citation 2016 ).

The scientific production on bullying and cyberbullying in the field of E&ER has obviously increased over time, but when it comes to authors it seems quite scattered. Findings based on co-authorship analysis revealed the existence of small and isolated authors’ networks. These results are similar to those described by the authors of a bibliometric study on students’ mental health and wellbeing, in which the structure of the field at author level is compared with the image of an archipelago, and the research networks are like islands with few connections between them (Hernández-Torrano, Ibrayeva, et al., Citation 2020 ). Our findings suggest that more international cooperation is needed to connect these groups of researchers and create a large global network with greater impact on the issue of bullying and cyberbullying.

Evidence regarding strong collaborations among Australian researchers has been found, which may serve as a good example in terms of joint effort at a national level. Also, the fact that most of the top ten cited articles are the result of collaborative relationships, supports the idea that cooperation is an efficient strategy in research. The culture of international collaboration seems to be especially evident among researchers in the USA and the UK, proving that the most prolific countries are also the most open to collaboration. The contribution of researchers from other countries (Spain, Italy, Australia) should not be neglected. Only 40% of the world’s countries contributed to research on this topic and sharing a a common language seems to stimulate co-authorship at country level. In terms of collaboration patterns, small networks with strong links have been discovered for co-authorship at author level, and a large network with weak links, at country level. However, national collaboration among authors is more common than international collaboration and could be considered a feature of research on bullying and cyberbullying in E&ER. There is a need to strengthen and expand collaboration between researchers and countries to address this global problem and support the transfer of research knowledge to different educational environments.

The intellectual structure analysis revealed that the scientific database used for bibliometric analysis on bullying and cyberbullying proved not to bring significant differences in the case of cited references. In another bibliometric study on cyberbullying which used the Scopus database (Lopez-Meneses et al., Citation 2020 ), the paper by Smith et al. ( Citation 2008 ) is reportedly the most cited, which shows that this article is a very relevant landmark in studying not only cyberbullying, but also bullying. This similarity can be explained by the fact that co-citation analysis allowed us to cross the boundaries imposed by the WoS database and as a result, papers from the broader scientific literature have been taken into consideration. The most influential co-cited authors in articles classified in the field of E&ER are acknowledged for their research activity in Psychology, rather than in E&ER. This is explained by the rich scientific research on the topic in the field of Psychology which has provided conceptual support for E&ER studies. Working in interdisciplinary teams, by integrating researchers from the two key fields as well as others (i.e. sociology, health care science, management, etc.), could be a path to generating new perspectives in addressing the topic of bullying and advance research.

The research lines identified by the keywords analysis are diverse and cover mainly aspects related to the characteristics and the occurrence of the phenomena during childhood and adolescence among different categories of students in various contexts. The actors and the negative effects of bullying and cyberbullying, the emotional and social processes involved, the prevention and intervention-related aspects were also explored by the researchers. However, the researchers have focused predominantly on bullying or cyberbullying in school settings, while other educational contexts, such as early education, higher education, non-formal education, etc. still need to be further explored. These phenomena may occur at different levels and within many types of education and therefore will need the attention of researchers and practitioners in the field. In their study focusing on cyberbullying, Myers and Cowie ( Citation 2019 ) support as a matter of urgency the need for an approach across the educational lifespan and even shared expertise among schools, colleges, and universities for effective interventions, which may be relevant in future research. Although prevention and intervention issues occur in clusters, they are not central. We agree with Zych et al. ( Citation 2015 ) that more attention should be paid to research on prevention and intervention programmes in bullying and cyberbullying, not only in school settings but in all educational contexts. Currently research interest tends to focus on identifying the context of bullying, its effects, and eventually, on intervention. More effort is necessary to study policies, to develop strategies for bullying and especially cyberbullying prevention. At this level, an international effort might be more helpful in dealing with the issue. A sensitive topic is related to sexual behaviour in adolescents, which can easily turn into aggressive behaviour, especially in virtual environments and in the absence of parental surveillance. Establishing a connection between inclusive education strategies and bullying and cyberbullying prevention may be helpful in upcoming research and interventions.

The science mapping analysis allowed us to identify several features of E&ER on bullying and cyberbullying as follows: the existence of small, mostly isolated networks of authors developed over time; greater openness to collaboration among countries with the highest scientific productivity on the topic; a substantial contribution by authors in Psychology to ensure the theoretical framework of the E&ER on bullying and cyberbullying; the development of more lines of research over time, with an emphasis on describing and explaining the phenomenon in adolescence and in the school environment.

Our study has several pedagogical and practical implications. As bullying and cyberbullying are pressing problems that occur in various countries and contexts, at different ages, at all educational stages, teaching and learning about them is necessary. Influential documents revealed within this study can be included in the training of new generations of scientists (Hallinger & Kovacevic, Citation 2019 ). The research work of prolific and influential scholars in the field and the strengthening of collaborative networks over time can encourage young researchers to manifest commitment and join efforts to address issues in this area. Consistency is much needed in this respect. Then, teacher educators can use the knowledge produced by educational research to support pre-service or in-service teachers’ learning about bullying and cyberbullying. Navigating the educational research landscape on the topic could be facilitated by the present study. The work of researchers can be a source of inspiration for pedagogical approaches in different educational institutions, including the training of students and parents in connection with the topic. Valuable lessons across all levels of education can be learned from the experiences of countries that foster research to address and mitigate these issues.

Even if the present study led us to an overview of scientific production on bullying and cyberbullying in the field of education, the bibliometric approach needs to be supplemented with qualitative analyses for a more thorough examination of different perspectives, methodological aspects or results of research work in education. The decision to restrict the investigation in the present study only to the works in the field of E&ER was supported by the assumption that such an approach could be closer to the needs of educational researchers and practitioners. Given the cross-disciplinary nature of the topic, publications from multiple research fields that are indexed in several databases should be included to generate a more comprehensive picture. Undoubtedly, the analyses performed here can be extended or deepened. For instance, we have looked at three decades globally, but a more in-depth approach to each decade (i.e. at the level of topics of interest investigated) would probably have helped us to depict finer insights into research evolution. For this purpose, the use of more advanced analysis tools would be needed. However, this perspective may be further explored in the future. Moreover, the findings regarding the collaboration patterns, the intellectual and conceptual structures were derived exclusively from the analysis of the articles, not from other documents, which were included in the first part of the bibliometric analysis. The prevalence of articles among all publications was the reason behind this choice. Due to the large number of publications considered, we cannot comment on the nature of studies, the methodology used (quantitative, qualitative, mixed), or on sample size. The presence of terms such as “focus groups” and “qualitative” in the co-occurrence keywords analysis suggests the prevalence of qualitative studies, but a more in-depth analysis is needed.

This study maps the scientific literature on bullying and cyberbullying published between 1991–2020 and indexed by the WoSCC database in E&ER. The bibliometric analysis revealed the evolution of publication trends over the past 30 years, the contributions of countries and institutions, leading journals and authors, the citation dynamics, as well as the collaboration patterns, the intellectual and conceptual structure of the knowledge base mentioned above.

The use of the bibliometric approach was motivated by the need to achieve a global vision of bullying and cyberbullying education research. Getting an overview of the literature is an emerging trend in many scientific fields (Ellegaard & Wallin, Citation 2015 ). The bibliometric approach allowed us to deal with a rich data set, in a systematic, replicable (Linnenluecke et al., Citation 2020 ; Martinez et al., Citation 2015 ), and unbiased manner (Zupic & Cater, Citation 2015 ). Different types of bibliometric indicators were used: (a) quantity indicators, to measure the productivity of the field, countries, institutions, journals, and researchers regarding the topics of interest; (b) quality indicators, to examine the performance of the research output in terms of citations; (c) structural indicators, to explore connections between publications, authors, and research focus (Durieux & Gevenois, Citation 2010 ). Even if this approach did not allow a detailed review of all publications, the bibliometric indicators and the network analyses enabled us to gain valuable insights into education research into bullying and cyberbullying.

As regards RQ1, the results indicated E&ER as the second richest area in terms of the number of indexed works for the analysed period, after Psychology. We discovered an exponential growth pattern of publications during the three decades timeframe, which is fuelled by the continuous increase in the number of authors exploring the topic from one decade to the next. Papers on bullying are dominant compared to those on cyberbullying, which are more recent. Most publications are written in English and the mainstream of research in the field is provided by article type documents, which are the most numerous and accumulated the highest number of citations. The number of open access publications increased substantially between 2015-2020. Increasing visibility through open access publishing practices can be a welcome support for researchers and practitioners in various parts of the world, for further developments in educational research, and for effective interventions in educational contexts. Based on the results regarding the annual number of publications, but also on their citations over the three decades, we can expect a continuous increase of interest in the subject matter in the field of E&ER.

As far as RQ2 is concerned, we have discovered that although bullying is a topic of international interest and the number of contributing countries has increased over time, there is a small core of key players in the field. The USA has been a leader and contributes almost a third to the total number of works. The interest of Spanish and American researchers in addressing cyberbullying is noteworthy. The University of London has been identified as the most prolific institution, but many American universities are among the top institutions with a large number of papers. High research output is associated with developed countries. As bullying and cyberbullying are issues of global concern, educational research coming from regions with poor research contribution are necessary to understand the phenomena better and to inform educational policy and practice.

As concerns RQ3, it has been found that a small number of journals and authors are more devoted to the topic. The Journal of School Violence and Journal of School Health published the largest number of articles on bullying and cyberbullying classified in E&ER. Relying mainly on research in the field of Psychology, we may say that E&ER on bullying and cyberbullying has become a distinct field of inquiry over time, but with a rather small number of core journals that support publications on the topic. More scholars with expertise in the field of education and more specialized journals in violence-related issues in different educational settings, not just in school, will be needed to support the global research activity.

As concerns RQ4, our analysis indicates a reduced collaboration among researchers in the E&ER on bullying and cyberbullying. Some of the most prolific authors work in small networks. In terms of country distribution, the USA is the centre of the research network in our field of analysis. Collaborative patterns at national level seem to be dominant whereas, at the international level, they are much less so. Dan Olweus, Peter K. Smith, Christina Salmivalli, Dorothy L. Espelage, Robin M. Kowalski are the authors whose scientific work constitutes the foundation for articles on bullying and cyberbullying in E&ER. Also, the most cited references are papers belonging to Dan Olweus, Peter K. Smith, and Tonja R. Nansel as authors or co-authors. All these authors publish mostly in the field of Psychology, which means that educational research is informed by scientific research in this area.

Finally, as regards RQ5, more lines of investigation, which reflect the various interests of the researchers in the field over the three decades, have been identified in our analysis. These are mainly related to the characteristics and manifestations of the phenomena in school environments. Hot topics identified include the emergence of bullying and cyberbullying among school students or vulnerable populations (i.e. students with special needs); the social and emotional health of students associated with bullying and cyberbullying; adult awareness (teachers, parents, etc.); and prevention and interventions in school to reduce bullying.

All research questions were worth exploring. By addressing them, the first global view of the research on bullying and cyberbullying in education from its beginnings to the present has been provided. The analysis of publications, countries, institutions, journals, authors, and citation structure is useful in providing an overview of a research field or a particular topic in a field (Merigo et al., Citation 2015 ; Modak et al., Citation 2020 ). Given that the synthesis of past research can contribute to the advancement of research (Zupic & Cater, Citation 2015 ), we hope that the findings of this bibliometric study can inspire new research and commitment to the field. For instance, understanding how bullying and cyberbullying have evolved in the scientific literature (RQ1) is useful for educational researchers. They can see that the topic remains a current issue to be investigated and the rich body of literature accumulated over the three decades can support new approaches in the field. The findings concerning prolific countries and institutions (RQ2) help experienced and novice researchers alike to discover common research concerns at the international level, which can turn into opportunities for collaboration in an ever-evolving scientific landscape. Research funding agencies may find here evidence for the need to support inquiry regarding such complex phenomena in the educational environment. These results can also inform educational policy factors for the development and promotion of anti-bullying legal and pedagogical initiatives in different parts of the world. The educational researchers looking for a suitable journal to publish their work on bullying or cyberbullying can be helped by the answers provided for RQ3. They can also acquire a quick overview of the articles and authors that have a high impact on bullying and cyberbullying research. Teachers, school leaders, school counsellors, teacher educators can access publications that inform and inspire their effort to combat bullying and cyberbullying in various educational settings. By using science mapping to address RQ4 and RQ5 we provided new insights regarding the structure and dynamics of bullying and cyberbullying in education, which is useful in reviewing a line of research (Zupic & Cater, Citation 2015 ). Researchers can visualize the network structure at the level of countries and authors, and this can encourage the expansion of different research groups, as well as facilitate the connections between them around common lines of inquiry. The development of new groups of educational researchers would be necessary to ensure the sustainability of research on the topics and to support educational practitioners in managing the phenomena. Identifying the main themes explored by authors offers a perspective on the level of knowledge in research focusing on bullying and cyberbullying in education, the aspects that need further analysis, and the current gaps.

Due to all the evidence mentioned above, this study can be a reference point for researchers and practitioners in the field. Furthermore, this study joins the efforts of authors of other bibliometric studies that investigated the educational research from various perspectives and for a better and deeper understanding of the field and its various topics (Aman & Botte, Citation 2017 ; Braojos et al., Citation 2015 ; Cretu & Morandau, Citation 2020 ; Diem & Wolter, Citation 2013 ; Esteban et al., Citation 2020 ; Fellnhofer, Citation 2019 ; Hernandez-Torrano & Ibrayeva, Citation 2020 ; Ivanovic & Ho, Citation 2019 ; Lopes et al., Citation 2017 ; Rodriguez-Sabiote et al., Citation 2020 ).

The present work is based on a number of assumptions and presents some limitations. The results of bibliometric analyses offered here are based on publications from a single database, namely WoSCC, whereas those from other databases were not taken into consideration. As the findings may vary depending on the database used (Mongeon & Paul-Hus, Citation 2016 ), we do not exclude the possibility that other bibliometric studies conducted on other databases may lead to different results. The database used in this study covers only a limited amount of the worldwide educational research output (Aman & Botte, Citation 2017 ). For instance, Scopus presents a larger journal coverage in all fields (Mongeon & Paul-Hus, Citation 2016 ), but these journals have a lower impact and are limited to recent articles by comparison to WoS (Aghaei Chadegani et al., Citation 2013 ). The delimitation of the study sample was based on the assumption that the relevant works for researchers, practitioners and other stakeholders in education are indexed in E&ER, but publications on bullying and cyberbullying have emerged from different research areas. By limiting our research exclusively to those pertaining to E&ER, possible relevant contributions from other research areas have been omitted. Therefore, the results of this study shed light only on one facet of bullying and cyberbullying research. This can be also viewed as an opportunity for future bibliometric research that can integrate publications on bullying and cyberbullying indexed in more research areas. A multi-disciplinary perspective on the subject may be useful (Holt et al., Citation 2017 ) and of interest to the research community.

In the data search process, we introduced several terms to constitute a comprehensive data set, but it is possible that due to the misspelling of concepts in some records (e.g. “bulling”, “cyberbulling”, “cyber bulling”, etc.), or terminological variations (aggression, harassment, peer victimization, etc.) there are other works relevant to the topic under investigation, but which were not included in this study. Given that the search process for delimiting the data set took place at the end of January 2021, the publications indexed after the date of the download process were not included in this study. However, their number is estimated to be very small, and it is unlikely that this could affect the findings significantly.

In citation analysis, one assumption refers to the number of citations received by documents as an indicator of their scientific impact. However, our bibliometric study did not capture the nature of citations (positive, negative), or the self-citation practices, which can be sometimes used to increase the documents’ scientific impact. Then, the indicators based on the number of citations for journals, documents, and authors were generated only by counting citations from publications covered by WoS. The number of citations may be higher if other databases with broader coverage on social science research are explored. Besides the scientific impact of authors or publications, the societal one cannot be revealed by doing citation analysis. In addition, alternative metrics can be considered such as views and downloads of scientific publications, recommendations of certain publications, influence on social media (posts, tweets, comments on articles, or journals), blogs, forums, policy-related documents, etc. (Bornmann & Haunschild, Citation 2018 ). Then, there are various other ways in which researchers can contribute to the advancing of a field, such as the development of the research agenda, the exchange of ideas and experiences, the support provided to young researchers, etc., which cannot be captured by various metrics and were not considered in this study.

As concerns the co-authorship techniques, these exclude papers with only one author, which means that publications or authors with high impact might not be considered. On the other hand, in the case of co-cited authors, a situation discussed by specialists is related to the publications with multiple authors and the way they appear in co-citation networks (Perianes-Rodriguez et al., Citation 2016 ). There is no consensus on whether only the first author, the last or all authors should appear. In our analysis, all the authors of one publication were considered, in the case of co-cited authors. In the case of co-cited references, only the first author is marked on the map.

More challenges have been encountered while preparing the data and performing the analysis. For instance, while preparing the data we faced the situation of some records (especially book chapters) categorized within the WoSCC as dual document types, due to their coverage in multiple indexes. We decided to preserve the classification system used by WoSCC and include them in the study samples accordingly. Another challenge encountered was generated by alternative names in the case of several authors (e.g. Espelage, D.L. and Espelage D., Ortega-Ruiz, R. and Ortega, R., Campbell, M. and Campbell, M.A., Bauman, S. and Bauman, S.A., etc). The history of the affiliations of the authors displayed in the database and the profiles claimed by some authors helped us to clarify the uncertainties generated by such situations. Consequently, we merged the records for certain authors. In the case of institutions, the organizations-enhanced option in WoS helped us with the issue of their name variants. Data cleaning regarding author names and institutions, which is considered to be essential to applying bibliometric indicators in research assessment (Haustein & Larivière, Citation 2015 ), has been a lesson learnt during the process of preparing the data for analysis. A similar problem was encountered in the case of keywords that support alternative spellings or have several synonyms. In this case, the counting could be biased, especially when an alternative keyword does not meet the threshold established for the co-keyword analysis. A solution we considered was to build a thesaurus file to merge these terms. While identifying these keywords was time-consuming, it was important to go through the list before starting the analysis. In terms of detecting the most important themes a difficult task was to interpret borderline keywords (Cobo et al., Citation 2011b ). When the threshold changes, some terms migrate to a different cluster. In interpreting the main themes, we focused especially on those keywords that make up the core of the clusters.

The data normalization process can be difficult when working with a large data set. As our study covers a single field (E&ER), we kept the normalization approach at a basic level, using the classification of sources offered by WoS, and the standard full counting method in the case of co-authored publications but more sophisticated approaches can be developed (Waltman & van Eck, Citation 2019 ). As there is not a perfect software tool for science mapping analysis, it is necessary to use a variety of tools, each of them proving to be more appropriate for some specific analysis (Cobo et al., Citation 2011b ). This could be an important suggestion for further studies.

Finally, even if large amounts of data can be managed through bibliometric analyses, an in-depth investigation is recommended (Chen, Zou, & Xie, Citation 2020 ), paying particular attention to methods or approaches for qualitative evaluation of the research field. Despite these limitations and challenges and the fact that no bibliometric study can provide a perfect picture of a field (Hernandez-Torrano & Ibrayeva, Citation 2020 ), this work has its advantages. It provides useful and interesting insights in the evolution of bullying and cyberbullying research in education over the past 30 years, and it promotes the understanding of professionals interested in this line of research. It may also inspire new research initiatives, further strengthening interest in the topic.

We estimate that bullying and cyberbullying will continue to capture the interest of educational researchers. Therefore, further bibliometric investigations could draw on other databases such as Scopus, Springer link, Elsevier, etc., or combinations of these, for the widest possible coverage. Future work may also consider bibliometric approaches based on non-English language databases to gain a picture of research directions and practices in bullying or cyberbullying at the local, or regional level. Studies using more complex techniques for topic modelling analysis will be very useful to explore further the relevant themes addressed by researchers in bullying and cyberbullying studies. Also, to describe the conceptual structure, studies that include abstracts and titles for word analysis can offer a more comprehensive perspective.

As the problems related to bullying and cyberbullying in different educational environments have inspired numerous studies, it would be interesting to find out how the translation of the research on bullying and cyberbullying into educational practice is undertaken. How does this research support pedagogical practices' development in educational settings? How accessible is this research to practitioners working at different levels in education, and how can they benefit from the results of the research being disseminated through various types of publications? Following this direction, to facilitate the transfer from research to practice, future work can focus on providing specific data outputs such as: syntheses on bullying and/or cyberbullying related literature either through systematic reviews or bibliometric studies on various levels of education (for example, in primary/secondary school, in higher education, etc.); syntheses on the curriculum-based approaches to combat bullying (e.g. bullying and physical education); inventories of anti-bullying programs in schools or other educational settings; systematic evaluations of the research output on bullying and cyberbullying at the national level; syntheses on effective strategies for preventing and addressing bullying to inform targeted categories (teachers or future teachers, parents, principals, students, educational counsellors, community stakeholders, etc.). Such work can support the identification of what has been done and the perspectives that may be insufficiently explored or that deserve more attention (Zych et al., Citation 2015 ), and could also meet the needs of the various categories of actors connected to the educational environments who need to tackle these damaging phenomena. Other researchers may be interested in developing analyses of the methodologies used in research on bullying or cyberbullying in educational settings, as is the study authored by Hong and Espelage ( Citation 2012 ).

Some lines of research, perhaps less explored, can still and need to be addressed, for example, embedding bullying and cyberbullying issues in initial and continuous teacher education. It would be interesting to study bullying and especially cyberbullying in various educational environments that are strongly affected by the Covid-19 pandemic. Given that many educational institutions have had to switch to emergency online teaching, which has led to an increased use of technology for educational purposes, it is possible to witness a development in the research dedicated to cyberbullying. Students at all levels of education are more exposed to the phenomenon than ever before. New horizons for educational research have emerged, such as the impact of the Covid-19 pandemic on various manifestations of bullying, on educational institutions, and consideration of the curriculum and pedagogic strategies to deal with the phenomenon during and after the pandemic. There is still room for more contributors to address various aspects of bullying and cyberbullying in education and to expand research in the area even further.

No potential conflict of interest was reported by the author(s).

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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.

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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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