research gap of bullying

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Understanding Alternative Bullying Perspectives Through Research Engagement With Young People

Niamh o’brien.

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Edited by: Rita Zukauskiene, Mykolas Romeris University, Lithuania

Reviewed by: Larisa Tatjiana McLoughlin, University of the Sunshine Coast, Australia; Evangelia Karagiannopoulou, University of Ioannina, Greece

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

This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

Received 2019 Feb 28; Accepted 2019 Aug 13; Collection date 2019.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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

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

Introduction

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

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

The studies.

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

What Is Bullying?

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

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

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

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

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

Theoretical Underpinnings – Hearing From Children and Young People

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

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

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

The Studies

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

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

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

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

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

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

Informed Consent

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

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

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

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

Bullying definitions

Impact of bullying on victim

Reporting bullying

Bullying Definitions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Impact of Bullying on Victim

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reporting Bullying

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Data Availability

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

Ethics Statement

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

Author Contributions

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

Conflict of Interest Statement

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

Acknowledgments

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

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

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

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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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Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue

• Published: 12 August 2022
• Volume 4 , pages 175–179, ( 2022 )

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• Paul Horton 1 &
• Selma Therese Lyng 2  

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Introduction

School bullying research has a long history, stretching all the way back to a questionnaire study undertaken in the USA in the late 1800s (Burk, 1897 ). However, systematic school bullying research began in earnest in Scandinavia in the early 1970s with the work of Heinemann ( 1972 ) and Olweus ( 1978 ). Highlighting the extent to which research on bullying has grown exponentially since then, Smith et al. ( 2021 ) found that there were only 83 articles with the term “bully” in the title or abstract published in the Web of Science database prior to 1989. The numbers of articles found in the following decades were 458 (1990–1999), 1,996 (2000–2009), and 9,333 (2010–2019). Considering cyberbullying more specifically, Smith and Berkkun ( 2017 , cited in Smith et al., 2021 ) conducted a search of Web of Science with the terms “cyber* and bully*; cyber and victim*; electronic bullying; Internet bullying; and online harassment” until the year 2015 and found that while there were no articles published prior to 2000, 538 articles were published between 2000 and 2015, with the number of articles increasing every year (p. 49).

Numerous authors have pointed out that research into school bullying and cyberbullying has predominantly been conducted using quantitative methods, with much less use of qualitative or mixed methods (Hong & Espelage, 2012 ; Hutson, 2018 ; Maran & Begotti, 2021 ; Smith et al., 2021 ). In their recent analysis of articles published between 1976 and 2019 (in WoS, with the search terms “bully*; victim*; cyberbullying; electronic bullying; internet bullying; and online harassment”), Smith et al. ( 2021 , pp. 50–51) found that of the empirical articles selected, more than three-quarters (76.3%) were based on quantitative data, 15.4% were based on a combination of quantitative and qualitative data, and less than one-tenth (8.4%) were based on qualitative data alone. What is more, they found that the proportion of articles based on qualitative or mixed methods has been decreasing over the past 15 years (Smith et al., 2021 ). While the search criteria excluded certain types of qualitative studies (e.g., those published in books, doctoral theses, and non-English languages), this nonetheless highlights the extent to which qualitative research findings risk being overlooked in the vast sea of quantitative research.

School bullying and cyberbullying are complex phenomena, and a range of methodological approaches is thus needed to understand their complexity (Pellegrini & Bartini, 2000 ; Thornberg, 2011 ). Indeed, over-relying on quantitative methods limits understanding of the contexts and experiences of bullying (Hong & Espelage, 2012 ; Patton et al., 2017 ). Qualitative methods are particularly useful for better understanding the social contexts, processes, interactions, experiences, motivations, and perspectives of those involved (Hutson, 2018 ; Patton et al., 2017 ; Thornberg, 2011 ; Torrance, 2000 ).

Smith et al. ( 2021 ) suggest that the “continued emphasis on quantitative studies may be due to increasingly sophisticated methods such as structural equation modeling … network analysis … time trend analyses … latent profile analyses … and multi-polygenic score approaches” (p. 56). However, the authors make no mention of the range or sophistication of methods used in qualitative studies. Although there are still proportionately few qualitative studies of school bullying and cyberbullying in relation to quantitative studies, and this gap appears to be increasing, qualitative studies have utilized a range of qualitative data collection methods. These methods have included but are not limited to ethnographic fieldwork and participant observations (e.g., Eriksen & Lyng, 2018 ; Gumpel et al., 2014 ; Horton, 2019 ), digital ethnography (e.g., Rachoene & Oyedemi, 2015 ; Sylwander, 2019 ), meta-ethnography (e.g., Dennehy et al., 2020 ; Moretti & Herkovits, 2021 ), focus group interviews (e.g., Odenbring, 2022 ; Oliver & Candappa, 2007 ; Ybarra et al., 2019 ), semi-structured group and individual interviews (e.g., Forsberg & Thornberg, 2016 ; Lyng, 2018 ; Mishna et al., 2005 ; Varjas et al., 2013 ), vignettes (e.g., Jennifer & Cowie, 2012 ; Khanolainen & Semenova, 2020 ; Strindberg et al., 2020 ), memory work (e.g., Johnson et al., 2014 ; Malaby, 2009 ), literature studies (e.g., Lopez-Ropero, 2012 ; Wiseman et al., 2019 ), photo elicitation (e.g., Ganbaatar et al., 2021 ; Newman et al., 2006 ; Walton & Niblett, 2013 ), photostory method (e.g., Skrzypiec et al., 2015 ), and other visual works produced by children and young people (e.g., Bosacki et al., 2006 ; Gillies-Rezo & Bosacki, 2003 ).

This body of research has also included a variety of qualitative data analysis methods, such as grounded theory (e.g., Allen, 2015 ; Bjereld, 2018 ; Thornberg, 2018 ), thematic analysis (e.g., Cunningham et al., 2016 ; Forsberg & Horton, 2022 ), content analysis (e.g., Temko, 2019 ; Wiseman & Jones, 2018 ), conversation analysis (e.g., Evaldsson & Svahn, 2012 ; Tholander, 2019 ), narrative analysis (e.g., Haines-Saah et al., 2018 ), interpretative phenomenological analysis (e.g., Hutchinson, 2012 ; Tholander et al., 2020 ), various forms of discourse analysis (e.g., Ellwood & Davies, 2010 ; Hepburn, 1997 ; Ringrose & Renold, 2010 ), including discursive psychological analysis (e.g., Clarke et al., 2004 ), and critical discourse analysis (e.g., Barrett & Bound, 2015 ; Bethune & Gonick, 2017 ; Horton, 2021 ), as well as theoretically informed analyses from an array of research traditions (e.g., Davies, 2011 ; Jacobson, 2010 ; Søndergaard, 2012 ; Walton, 2005 ).

In light of the growing volume and variety of qualitative studies during the past two decades, we invited researchers to discuss and explore methodological issues related to their qualitative school bullying and cyberbullying research. The articles included in this special issue of the International Journal of Bullying Prevention discuss different qualitative methods, reflect on strengths and limitations — possibilities and challenges, and suggest implications for future qualitative and mixed-methods research.

Included Articles

Qualitative studies — focusing on social, relational, contextual, processual, structural, and/or societal factors and mechanisms — have formed the basis for several contributions during the last two decades that have sought to expand approaches to understanding and theorizing the causes of cyber/bullying. Some have also argued the need for expanding the commonly used definition of bullying, based on Olweus ( 1993 ) (e.g., Allen, 2015 ; Ellwood & Davies, 2010 Goldsmid & Howie, 2014 ; Ringrose & Rawlings,  2015 ; Søndergaard, 2012 ; Walton, 2011 ). In the first article of the special issue, Using qualitative methods to measure and understand key features of adolescent bullying: A call to action , Natalie Spadafora, Anthony Volk, and Andrew Dane instead discuss the usefulness of qualitative methods for improving measures and bettering our understanding of three specific key definitional features of bullying. Focusing on the definition put forward by Volk et al. ( 2014 ), they discuss the definitional features of power imbalance , goal directedness (replacing “intent to harm” in order not to assume conscious awareness, and to include a wide spectrum of goals that are intentionally and strategically pursued by bullies), and harmful impact (replacing “negative actions” in order to focus on the consequences for the victim, as well as circumventing difficult issues related to “repetition” in the traditional definition).

Acknowledging that these three features are challenging to capture using quantitative methods, Spadafora, Volk, and Dane point to existing qualitative studies that shed light on the features of power imbalance, goal directedness and harmful impact in bullying interactions — and put forward suggestions for future qualitative studies. More specifically, the authors argue that qualitative methods, such as focus groups, can be used to investigate the complexity of power relations at not only individual, but also social levels. They also highlight how qualitative methods, such as diaries and autoethnography, may help researchers gain a better understanding of the motives behind bullying behavior; from the perspectives of those engaging in it. Finally, the authors demonstrate how qualitative methods, such as ethnographic fieldwork and semi-structured interviews, can provide important insights into the harmful impact of bullying and how, for example, perceived harmfulness may be connected to perceived intention.

In the second article, Understanding bullying and cyberbullying through an ecological systems framework: The value of qualitative interviewing in a mixed methods approach , Faye Mishna, Arija Birze, and Andrea Greenblatt discuss the ways in which utilizing qualitative interviewing in mixed method approaches can facilitate greater understanding of bullying and cyberbullying. Based on a longitudinal and multi-perspective mixed methods study of cyberbullying, the authors demonstrate not only how qualitative interviewing can augment quantitative findings by examining process, context and meaning for those involved, but also how qualitative interviewing can lead to new insights and new areas of research. They also show how qualitative interviewing can help to capture nuances and complexity by allowing young people to express their perspectives and elaborate on their answers to questions. In line with this, the authors also raise the importance of qualitative interviewing for providing young people with space for self-reflection and learning.

In the third article, Q methodology as an innovative addition to bullying researchers’ methodological repertoire , Adrian Lundberg and Lisa Hellström focus on Q methodology as an inherently mixed methods approach, producing quantitative data from subjective viewpoints, and thus supplementing more mainstream quantitative and qualitative approaches. The authors outline and exemplify Q methodology as a research technique, focusing on the central feature of Q sorting. The authors further discuss the contribution of Q methodology to bullying research, highlighting the potential of Q methodology to address challenges related to gaining the perspectives of hard-to-reach populations who may either be unwilling or unable to share their personal experiences of bullying. As the authors point out, the use of card sorting activities allows participants to put forward their subjective perspectives, in less-intrusive settings for data collection and without disclosing their own personal experiences. The authors also illustrate how the flexibility of Q sorting can facilitate the participation of participants with limited verbal literacy and/or cognitive function through the use of images, objects or symbols. In the final part of the paper, Lundberg and Hellström discuss implications for practice and suggest future directions for using Q methodology in bullying and cyberbullying research, particularly with hard-to-reach populations.

In the fourth article, The importance of being attentive to social processes in school bullying research: Adopting a constructivist grounded theory approach , Camilla Forsberg discusses the use of constructivist grounded theory (CGT) in her research, focusing on social structures, norms, and processes. Forsberg first outlines CGT as a theory-methods package that is well suited to meet the call for more qualitative research on participants’ experiences and the social processes involved in school bullying. Forsberg emphasizes three key focal aspects of CGT, namely focus on participants’ main concerns; focus on meaning, actions, and processes; and focus on symbolic interactionism. She then provides examples and reflections from her own ethnographic and interview-based research, from different stages of the research process. In the last part of the article, Forsberg argues that prioritizing the perspectives of participants is an ethical stance, but one which comes with a number of ethical challenges, and points to ways in which CGT is helpful in dealing with these challenges.

In the fifth article, A qualitative meta-study of youth voice and co-participatory research practices: Informing cyber/bullying research methodologies , Deborah Green, Carmel Taddeo, Deborah Price, Foteini Pasenidou, and Barbara Spears discuss how qualitative meta-studies can be used to inform research methodologies for studying school bullying and cyberbullying. Drawing on the findings of five previous qualitative studies, and with a transdisciplinary and transformative approach, the authors illustrate and exemplify how previous qualitative research can be analyzed to gain a better understanding of the studies’ collective strengths and thus consider the findings and methods beyond the original settings where the research was conducted. In doing so, the authors highlight the progression of youth voice and co-participatory research practices, the centrality of children and young people to the research process and the enabling effect of technology — and discuss challenges related to ethical issues, resource and time demands, the role of gatekeepers, and common limitations of qualitative studies on youth voice and co-participatory research practices.

Taken together, the five articles illustrate the diversity of qualitative methods used to study school bullying and cyberbullying and highlight the need for further qualitative research. We hope that readers will find the collection of articles engaging and that the special issue not only gives impetus to increased qualitative focus on the complex phenomena of school bullying and cyberbullying but also to further discussions on both methodological and analytical approaches.

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Acknowledgements

We would like to thank the authors for sharing their work; Angela Mazzone, James O’Higgins Norman, and Sameer Hinduja for their editorial assistance; and Dorte Marie Søndergaard on the editorial board for suggesting a special issue on qualitative research in the journal.

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Horton, P., Lyng, S.T. Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue. Int Journal of Bullying Prevention 4 , 175–179 (2022). https://doi.org/10.1007/s42380-022-00139-5

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Bullying: What we know based on 40 years of research

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 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 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.

A Relational Framework for Understanding Bullying : Developmental Antecedents and Outcomes 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.

Translating Research to Practice in Bullying Prevention 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.

Law and Policy on the Concept of Bullying at School 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.

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.

• Children's Health
• Mental Health Research
• Staying Healthy
• Anger Management
• K-12 Education
• Educational Psychology
• Social Psychology
• Psychologist
• Double blind
• Pomegranate
• Stem cell treatments
• Cyber-bullying
• Evolutionary psychology

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Journal Reference :

• Shelley Hymel, Susan M. Swearer. Four decades of research on school bullying: An introduction. . American Psychologist , 2015; 70 (4): 293 DOI: 10.1037/a0038928

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