quantitative research paper about bullying

• Research article
• Open access
• Published: 14 December 2021

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

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

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

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

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

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

Conclusions

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

Introduction

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

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

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

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

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

Participants

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

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

Study outcome

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

Primary exposure

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

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

Statistical analyses

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

Prevalence of bullying at school and mental health problems

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

Associations between bullying at school and mental health problems

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

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

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

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

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

Strengths and limitations

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

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

Availability of data and materials

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

Code availability

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Acknowledgements

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

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

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Additional file 1..

Principal factor analysis description.

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

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• Published: 13 February 2024

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

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

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

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

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Mechanisms linking social media use to adolescent mental health vulnerability

Data availability.

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

Code availability

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

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

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

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Cyberbullying and its influence on academic, social, and emotional development of undergraduate students

This study investigated the influence of cyberbullying on the academic, social, and emotional development of undergraduate students. It's objective is to provides additional data and understanding of the influence of cyberbullying on various variables affecting undergraduate students. The survey sample consisted of 638 Israeli undergraduate students. The data were collected using the Revised Cyber Bullying Survey, which evaluates the frequency and media used to perpetrate cyberbullying, and the College Adjustment Scales, which evaluate three aspects of development in college students. It was found that 57% of the students had experienced cyberbullying at least once or twice through different types of media. Three variables were found to have significant influences on the research variables: gender, religion and sexual preferences. Correlation analyses were conducted and confirmed significant relationships between cyberbullying, mainly through instant messaging, and the academic, social and emotional development of undergraduate students. Instant messaging (IM) was found to be the most common means of cyberbullying among the students.

The main conclusions are that although cyberbullying existence has been proven, studies of cyberbullying among undergraduate students have not been fully developed. This particular population needs special attention in future research. The results of this study indicate that cyberbullying has an influence on the academic, social, and emotional development of undergraduate students. Additional Implications of the findings are discussed.

1. Introduction

Cyberbullying is defined as the electronic posting of mean-spirited messages about a person (such as a student) often done anonymously ( Merriam-Webster, 2017 ). Most of the investigations of cyberbullying have been conducted with students in elementary, middle and high school who were between 9 and 18 years old. Those studies focused on examining the prevalence and frequency of cyberbullying. Using “cyberbullying” and “higher-education” as key words in Google scholar (January, 2019) (all in title) yields only twenty one articles. In 2009, 2012 and 2013 one article appeared each year, since 2014 each year there were few publications. Of these articles only seven relates to effect of cyberbullying on the students, thus a gap in the literature exists in that it only minimally reports on studies involving undergraduate students. Given their relationship and access to technology, it is likely that cyberbullying occurs frequently among undergraduates. The purpose of this study is to examine the frequency and media used to perpetrate cyberbullying, as well as the relationship that it has with the academic, social and emotional development of undergraduate students.

Undergraduate students use the Internet for a wide variety of purposes. Those purposes include recreation, such as communicating in online groups or playing games; academics, such as doing assignments, researching scholarships or completing online applications; and practical, such as preparing for job interviews by researching companies. Students also use the Internet for social communication with increasing frequency.

The literature suggests that cyberbullied victims generally manifest psychological problems such as depression, loneliness, low self-esteem, school phobias and social anxiety ( Grene, 2003 ; Juvonen et al., 2003 ; Akcil, 2018 ). Moreover, research findings have shown that cyberbullying causes emotional and physiological damage to defenseless victims ( Akbulut and Eristi, 2011 ) as well as psychosocial difficulties including behavior problems ( Ybarra and Mitchell, 2007 ), drinking alcohol ( Selkie et al., 2015 ), smoking, depression, and low commitment to academics ( Ybarra and Mitchell, 2007 ).

Under great emotional stress, victims of cyberbullying are unable to concentrate on their studies, and thus their academic progress is adversely affected ( Akcil, 2018 ). Since the victims are often hurt psychologically, the depressive effect of cyberbullying prevents students from excelling in their studies ( Faryadi, 2011 ). The overall presence of cyberbullying victimization among undergraduate college students was found to be significantly related to the experience of anxiety, depression, substance abuse, low self-esteem, interpersonal problems, family tensions and academic underperformance ( Beebe, 2010 ).

1.1. Cyberbullying and internet

The Internet has been the most useful technology of modern times, which has enabled entirely new forms of social interaction, activities, and organizing. This has been possible thanks to its basic features such as widespread usability and access. However, it also causes undesirable behaviors that are offensive or threatening to others, such as cyberbullying. This is a relatively new phenomenon.

According to Belsey (2006, p.1) , “Cyberbullying involves the use of information and communication technologies such as e-mail, cell-phone and pager text messages, instant messaging, defamatory personal web sites, blogs, online games and defamatory online personal polling web sites, to support deliberate, repeated, and hostile behavior by an individual or group that is intended to harm others.” Characteristics like anonymity, accessibility to electronic communication, and rapid audience spread, result in a limitless number of individuals that can be affected by cyberbullying.

Different studies suggest that undergraduate students' use of the Internet is more significant and frequent than any other demographic group. A 2014 survey of 1006 participants in the U.S. conducted by the Pew Research Center revealed that 97% of young adults aged from 18 to 29 years use the Internet, email, or access the Internet via a mobile device. Among them, 91% were college students.

1.2. Mediums to perpetrate cyberbullying

The most frequent and common media within which cyberbullying can occur are:

Electronic mail (email): a method of exchanging digital messages from an author to one or more recipients.

Instant messaging: a type of online chat that offers real-time text transmission between two parties.

Chat rooms: a real-time online interaction with strangers with a shared interest or other similar connection.

Text messaging (SMS): the act of composing and sending a brief electronic message between two or more mobile phones.

Social networking sites: a platform to build social networks or social relations among people who share interests, activities, backgrounds or real-life connections.

Web sites : a platform that provides service for personal, commercial, or government purpose.

Studies indicate that undergraduate students are cyberbullied most frequently through email, and least often in chat rooms ( Beebe, 2010 ). Other studies suggest that instant messaging is the most common electronic medium used to perpetrate cyberbullying ( Kowalski et al., 2018 ).

1.3. Types of cyberbullying

Watts et al. (2017) Describe 7 types of cyberbullying: flaming, online harassment, cyberstalking, denigration, masquerading, trickery and outing, and exclusion. Flaming involves sending angry, rude, or vulgar messages via text or email about a person either to that person privately or to an online group.

Harassment involves repeatedly sending offensive messages, and cyberstalking moves harassment online, with the offender sending threatening messages to his or her victim. Denigration occurs when the cyberbully sends untrue or hurtful messages about a person to others. Masquerading takes elements of harassment and denigration where the cyberbully pretends to be someone else and sends or posts threatening or harmful information about one person to other people. Trickery and outing occur when the cyberbully tricks an individual into providing embarrassing, private, or sensitive information and posts or sends the information for others to view. Exclusion is deliberately leaving individuals out of an online group, thereby automatically stigmatizing the excluded individuals.

Additional types of cyberbullying are: Fraping - where a person accesses the victim's social media account and impersonates them in an attempt to be funny or to ruin their reputation. Dissing - share or post cruel information online to ruin one's reputation or friendships with others. Trolling - is insulting an individual online to provoke them enough to get a response. Catfishing - steals one's online identity to re-creates social networking profiles for deceptive purposes. Such as signing up for services in the victim's name so that the victim receives emails or other offers for potentially embarrassing things such as gay-rights newsletters or incontinence treatment. Phishing - a tactic that requires tricking, persuading or manipulating the target into revealing personal and/or financial information about themselves and/or their loved ones. Stalking – Online stalking when a person shares her personal information publicly through social networking websites. With this information, stalkers can send them personal messages, send mysterious gifts to someone's home address and more. Blackmail – Anonymous e-mails, phone-calls and private messages are often done to a person who bear secrets. Photographs & video - Threaten to share them publicly unless the victim complies with a particular demand; Distribute them via text or email, making it impossible for the victim to control who sees the picture; Publish the pictures on the Internet for anyone to view. Shunning - persistently avoid, ignore, or reject someone mainly from participating in social networks. Sexting - send sexually explicit photographs or messages via mobile phone.

1.4. Prevalence of cyberbullying

Previous studies have found that cyberbullying incidents among college students can range from 9% to 34% ( Baldasare et al., 2012 ).

Beebe (2010) conducted a study with 202 college students in United States. Results indicated that 50.7% of the undergraduate students represented in the sample reported experiencing cyberbullying victimization once or twice during their time in college. Additionally, 36.3% reported cyberbullying victimization on a monthly basis while in college. According to Dılmaç (2009) , 22.5% of 666 students at Selcuk University in Turkey reported cyberbullying another person at least once and 55.35% reported being a victim of cyberbullying at least once in their lifetimes. In a study of 131 students from seven undergraduate classes in United States, 11% of the respondents indicated having experienced cyberbullying at the university ( Walker et al., 2011 ). Of those, Facebook (64%), cell phones (43%) and instant messaging (43%) were the most frequent technologies used. Students indicated that 50% of the cyberbullies were classmates, 57% were individuals outside of the university, and 43% did not know who was cyberbullying them.

Data from the last two years (2017–18) is similar to the above. A research, of 187 undergraduate students matriculated at a large U.S. Northeastern metropolitan Roman Catholic university ( Webber and Ovedovitz, 2018 ), found that 4.3% indicated that they were victims of cyberbullying at the university level and a total of 7.5% students acknowledged having participated in bullying at that level while A survey (N = 338) at a large midwestern university conducted by Varghese and Pistole (2017) , showed that frequency counts indicated that 15.1% undergraduate students were cyberbully victims during college, and 8.0% were cyberbully offenders during college.

A study of 201 students from sixteen different colleges across the United States found a prevalence rate of 85.2% for college students who reported being victims of cyberbullying out of the total 201 responses recorded. This ranged from only occasional incidents to almost daily experiences with cyberbullying victimization ( Poole, 2017 ).

In A research of international students, 20.7% reported that they have been cyberbullied in the last 30 days once to many times ( Akcil, 2018 ).

1.5. Psychological impact of cyberbullying

Cyberbullying literature suggests that victims generally manifest psychological problems such as depression, anxiety, loneliness, low self-esteem, social exclusion, school phobias and poor academic performance ( DeHue et al., 2008 ; Juvonen and Gross, 2008 ; Kowalski and Limber, 2007 ; Grene, 2003 ; Juvonen et al., 2003 ; Rivituso, 2012 ; Varghese and Pistole, 2017 ; Na, 2014 ; Akcil, 2018 ), low self-esteem, family problems, school violence and delinquent behavior ( Webber and Ovedovitz, 2018 ), which brings them to experience suicidal thoughts as a means of escaping the torture ( Ghadampour et al., 2017 ).

Moreover, research findings have shown that cyberbullying causes emotional and physiological damage to defenseless victims ( Faryadi, 2011 ) as well as psychosocial problems including inappropriate behaviors, drinking alcohol, smoking, depression and low commitment to academics ( Walker et al., 2011 ).

The victims of cyberbullying, under great emotional stress, are unable to concentrate on their studies, and thus their academic progress is adversely affected ( Faryadi, 2011 ). Since the victims are often hurt psychologically, the depressive effect of cyberbullying prevents students from excelling in their studies ( Faryadi, 2011 ).

In a Malaysian university study with 365 first year students, the majority of the participants (85%) interviewed indicated that cyberbullying affected their academic performance, specifically their grades ( Faryadi, 2011 ). Also, 85% of the respondents agreed that bullying caused a devastating impact on students' emotions and equally caused unimaginable psychological problems among the victims. Heiman and Olenik-Shemesh (2018) report that for students with learning disabilities, predictors of cybervictimization were low social support, low self-perception, and being female, whereas for students without learning disabilities, the predictors were low social support, low well-being, and low body perception.

1.6. Academic, social, and emotional development of undergraduate students

The transition to academic institutions is marked by complex challenges in emotional, social, and academic adjustment ( Gerdes and Mallinckrodt, 1994 ; Parker et al., 2004 ).

The adaptation to a new environment is an important factor in academic performance and future achievement. Undergraduate students are not only developing academically and intellectually, they are also establishing and maintaining personal relationships, developing an identity, deciding about a career and lifestyle, and maintaining personal health and wellness. Many students are interacting with people from diverse backgrounds who hold different values and making new friends. Some are also adapting to living away from home for the very first time ( Inkelas et al., 2007 ).

The concept of academic development involves not only academic abilities, but motivational factors, and institutional commitment. Motivation to learn, taking actions to meet academic demands, a clear sense of purpose, and general satisfaction with the academic environment are also important components of the academic field ( Lau, 2003 ).

A second dimension, the social field, may be as important as academic factors. Writers have emphasized integration into the social environment as a crucial element in commitment to a particular academic institution ( Tinto, 1975 ). Becoming integrated into the social life of college, forming a support network, and managing new social freedoms are some important elements of social development. Crises in the social field include conflict in a living situation, starting or maintaining relationships, interpersonal conflicts, family issues, and financial issues ( McGrath, 2005 ), which are manifested as feelings of loneliness ( Clark et al., 2015 ).

In the emotional field, students commonly question their relationships, direction in life, and self-worth ( Rey et al., 2011 ). A balanced personality is one which is emotionally adjusted. Emotional adjustment is essential for creating a sound personality. physical, intellectual mental and esthetical adjustments are possible when emotional adjustment is made ( Ziapour et al., 2018 ). Inner disorders may result from questions about identity and can sometimes lead to personal crises ( Gerdes and Mallinckrodt, 1994 ). Emotional problems may be manifested as global psychological distress, somatic distress, anxiety, low self-esteem, or depression. Impediments to success in emotional development include depression and anxiety, stress, substance abuse, and relationship problems ( Beebe, 2010 ).

The current study is designed to address two research questions: (1) does cyberbullying affect college students' emotional state, as measured by the nine factors of the College Adjustment Scales ( Anton and Reed, 1991 ); (2) which mode of cyberbullying most affects students' emotional state?

2.1. Research settings and participants

The present study is set in Israeli higher education colleges. These, function as: (1) institutions offering undergraduate programs in a limited number of disciplinary fields (mainly the social sciences), (2) centers for training studies (i.e.: teacher training curricula), as well as (3) as creators of access to higher education. The general student population is heterogeneous, coming from the Western Galilee. In this study, 638 Israeli undergraduate students participated. The sample is a representative of the population of the Western galilee in Israel. The sample was 76% female, 70% single, 51% Jewish, 27% Arabs, 7% Druze, and 15% other ethnicity. On the dimension of religiosity, 47% were secular, 37% traditional, 12% religious, 0.5% very religious, and 3.5% other. On the dimension of sexual orientation, 71% were straight women, 23.5% straight men, 4% bisexual, 1% lesbians, and 0.5% gay males (note: according to the Williams Institute, approximately 4% of the population in the US are LGBT, [ Gates, 2011 ], while 6% of the EU population are LGBT, [ Dalia, 2016 ]).

2.2. Instrumentation

Two instruments were used to collect data: The Revised Cyber Bullying Survey (RCBS), with a Cronbach's alpha ranging from .74 to .91 ( Kowalski and Limber, 2007 ), designed to measure incidence, frequency and medium used to perpetrate cyberbullying. The survey is a 32-item questionnaire. The frequency was investigated using a 5-item scale with anchors ranging from ‘it has never happened to me’ to ‘several times a week’. Five different media were explored: email, instant messaging, chat room, text messaging, and social networking sites. Each medium was examined with the same six questions related to cases of cyberbullying (see Table 1 ).

Description of the Revised Cyber Bullying Survey (RCBS) variables.

Note: the theoretical range is between zero to twenty-four.

Table 1 shows the five variables that composed the RCBS questionnaire (all of the variables are composed of 6 statements). The results indicate that the levels of all the variables is very low, which means that the respondents experienced cyberbullying once or twice. The internal consistency reliability estimate based on the current sample suggested that most of the variables have an adequate to high level of reliability, with a Cronbach's alpha of 0.68–0.87.

The College Adjustment Scales (CAS) ( Anton and Reed, 1991 ), evaluated the academic, social, and emotional development of college students. Values were standardized and validated for use with college students. The validity for each subscale ranged from .64 to .80, noting high correlations among scales. Reliability of the scales ranged from .80 to .92, with a mean of .86. The instrument included 128 items, divided into 10 scales: anxiety, depression, suicidal ideation, substance abuse, self-esteem problems, interpersonal problems, family problems, academic problems, career problems, and regular activities (see Table 2 ). Students responded to each item using a four-point scale.

Description of CAS variables.

Anxiety: A measure of clinical anxiety, focusing on common affective, cognitive, and physiological symptoms.

Depression: A measure of clinical depression, focusing on common affective, cognitive, and physiological symptoms.

Suicidal Ideation: A measure of the extent of recent ideation reflecting suicide, including thoughts of suicide, hopelessness, and resignation.

Substance Abuse: A measure of the extent of disruption in interpersonal, social, academic, and vocational functioning as a result of substance use and abuse.

Self-esteem Problems: A measure of global self-esteem which taps negative self-evaluations and dissatisfaction with personal achievement.

Interpersonal Problems: A measure of the extent of problems in relating to others in the campus environment.

Family Problems: A measure of difficulties experienced in relationships with family members.

Academic Problems: A measure of the extent of problems related to academic performance.

Career Problems: A measure of the extent of problems related to career choice.

Participants also responded to a demographic questionnaire that included items on gender, birth year, marital status, ethnicity, and sexual orientation. As sexual orientation is a major cause for bullying ( Pollock, 2006 ; Cahill and Makadon, 2014 ), it was included in the background information.

Convenience sampling and purposive sampling were used for this study. Surveys with written instructions were administered in classrooms, libraries and online via Google Docs at the end of the semester.

The surveys were translated to Hebrew and back translated four times until sufficient translation was achieved. The research was approved by the Western Galilee College Research and Ethic Committee.

A sizeable percentage, 57.4% (366), of the respondents reported being cyber bullied at least once and 3.4% (22) reported being cyber bullied at least once a week. The types of bullies can be seen in Fig. 1 .

Types of bullies.

Three variables were found to have significant influences on the research variables: (1) gender (see Table 3 ); (2) religion (see Table 4 ); and (3) sexual preferences (see Table 5 ).

Results of independent t-tests for research variables by gender.

Note: n male = 127, n female = 510, *p < .05.

Results of independent t-tests for research variables by level of religion.

Note: n religious = 345, n secular = 293, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

Results of independent t-tests for research variables by sexual preference.

Note: n heterosexual = 596, n other = 42, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

Independent t-tests between the CAS variables and gender show significant differences between females and males (see Table 3 ).

Independent t-tests between the CAS variables and level of religiosity show significant differences between secular and religious persons, i.e., observant believers (see Table 4 ).

Independent t-tests between the CAS variables and sexual preference show significant differences between heterosexual individuals and others (see Table 5 ).

The research population was divided into three age groups having five year intervals. One respondent who was 14 years old was removed from the population.

For the variable “career problems” it was found that there was a significant difference between the 26–30 year age group [p < .05, F(2,5815) = 3.49, M = 56.55] and the 31–35 (M = 56.07) as well as the 20–25 (M = 54.58) age groups.

For the variable "depression" it was found that there was a significant difference between the 20–25 year age group [p < .05, F(2,5815) = 3.84, M = 54.56] and the 31–35 (M = 51.61) as well as the 26–30 (M = 52.83) age groups.

For the variable “interpersonal problems” it was found that there was a significant difference between the 20–25 year age group [p < .06, F(2,5815) = 3.84, M = 53.85] and the 31–35 (M = 51.29) as well as the 26–30 (M = 52.19) age groups.

For the variable “suicidal ideation” it was found that there was a significant difference between the 20–25 year age group [p < .06, F(2,5815) = 3.84, M = 55.45] and the 31–35 (M = 49.71) as well as the 26–30 (M = 50.13) age groups (see Table 6 ).

Results of one way Anova for research variables by age.

Note: n 20-25 = 216, n 26-30 = 287, n 31-35 = 82, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

To confirm that there was no effect among the independent variables, a Pearson correlation analysis of cyberbullying with CAS variables was run. As the correlations between the independent variables are weak, no multicollinearity between them was noted (see Table 7 ).

Pearson correlation of cyberbullying with CAS variables.

Note: n = 638, ∼ p < .06, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

Regression analyses on the effect of the cyberbullying variables on the CAS variables (see Fig. 2 ) show that an increase in cyberbullying by social networking and IM increases the academic problems variable. The model explained 6.1% of the variance (F (13,585) = 2.94, p < .001) and shows an increase in the suicidal ideation variable. There is also a marginal effect of cyberbullying by SMS on suicidal ideation, revealing that an increase in cyberbullying by SMS causes a decrease in suicidal ideation. The explained variance of the model is 24.8% (F (11,584) = 14.80, p < .001). Higher cyberbullying by social networking results in an increase in the anxiety variable. The explained variance of the model is 8.8% (F (13,584) = 4.32, p < .001). An increase in cyberbullying by chat and IM shows an increase in the substance abuse variable. The model explains 13% of the variance (F (13,584) = 6.71, p < .001). Increasing cyberbullying by social networking and IM increases the self-esteem problems variable. The explained variance of the model is 9% (F (13,584) = 4.43, p < .001). An increase of cyberbullying by email increases the problems students have with regular activities. The explained variance of the model is 5.2% (F (13,575) = 2.44, p < .01). Heightened cyberbullying by social networking and IM increases students' interpersonal problems. There is also an effect of cyberbullying by IM on suicidal ideation, such that an increase in cyberbullying by IM causes a decrease in interpersonal problems. The explained variance of the model is 8% (F (13,584) = 3.89, p < .001). An increase in cyberbullying by SMS decreases the family problems variable. The explained variance of the model is 11.4% (F (13,584) = 5.76, p < .001). And finally, heightened cyberbullying by IM and social networking decreases the depression variable. The variance explained by the model is 11.9% (F (13,584) = 6.04, p < .001).

The influence of academic cyberbullying variables on the CAS variables.

4. Discussion

The objective of this study was to fill an existing gap in the literature regarding the influence of cyberbullying on the academic, social, and emotional development of undergraduate students.

As has been presented, cyberbullying continues to be a disturbing trend not only among adolescents but also undergraduate students. Cyberbullying exists in colleges and universities, and it has an influence on the development of students. Fifty seven percent of the undergraduate students who participated in this study had experienced cyberbullying at least once during their time in college. As previous studies have found that cyberbullying incidents among college students can range from 9% to 50% ( Baldasare et al., 2012 ; Beebe, 2010 ) it seems that 57% is high. Considering the effect of smartphone abundance on one hand and on the other the increasing use of online services and activities by young-adults can explain that percentage.

Considering the effect of such an encounter on the academic, social and emotional development of undergraduate students, policy makers face a formidable task to address the relevant issues and to take corrective action as Myers and Cowie (2017) point out that due to the fact that universities are in the business of education, it is a fine balancing act between addressing the problem, in this case cyberbullying, and maintaining a duty of care to both the victim and the perpetrator to ensure they get their degrees. There is a clear tension for university authorities between acknowledging that university students are independent young adults, each responsible for his or her own actions, on one hand, and providing supervision and monitoring to ensure students' safety in educational and leisure contexts.

Although there are increasing reports on connections between cyberbullying and social-networks (see: Gahagan et al., 2016 ), sending SMS or MMS messages through Internet gateways ensures anonymity, thus indirectly supporting cyberbullying. A lot of websites require only login or a phone number that can also be made up ( Gálik et al., 2018 ) which can explain the fact that instant-messaging (IM) was found to be the most common means of cyberbullying among undergraduate students with a negative influence on academic, family, and emotional development (depression, anxiety, and suicidal ideation). A possible interpretation of the higher frequency of cyberbullying through IM may be that young adults have a need to be connected.

This medium allows for being online in ‘real time’ with many peers or groups. With the possibility of remaining anonymous (by creating an avatar – a fake profile) and the possibility of exposing private information that remains recorded, students who use instant messaging become easy targets for cyberbullying. IM apps such as WhatsApp are extremely popular as they allow messages, photos, videos, and recordings to be shared and spread widely and in real time.

Students use the Internet as a medium and use it with great frequency in their everyday lives. As more aspects of students' lives and daily affairs are conducted online, coupled with the fact that excessive use may have consequences, it is important for researchers and academic policy makers to study the phenomenon of cyberbullying more deeply.

Sexual orientation is also a significant factor that increases the risk of victimization. Similarly, Rivers (2016) documented the rising incidence of homophobic and transphobic bullying at university and argues strongly for universities to be more active in promoting tolerance and inclusion on campus. It is worth noting that relationships and sexual orientation probably play a huge role in bullying among university students due to their age and the fact that the majority of students are away from home and experiencing different forms of relationships for the first time. Faucher et al. (2014) actually found that same sex cyberbullying was more common at university level than at school. Nonetheless, the research is just not there yet to make firm conclusions.

Finally, cyberbullying is not only an adolescent issue. Although its existence has been proven, studies of cyberbullying among undergraduate students have not been fully developed. This particular population needs special attention in future research.

The results of this study indicate that cyberbullying has an influence on the academic, social, and emotional development of undergraduate students.

In the academic field, findings revealed a statistically significant correlation between cyberbullying perpetrated by email and academic problems. Relationships between academic problems and cyberbullying perpetrated by other media were not found. This suggests that cyberbullying through instant messaging, chat room, text messaging, and social networking sites, have not influenced academic abilities, motivation to learn, and general satisfaction with the academic environment. However, cyberbullying perpetrated by email has an influence on academics, perhaps because of the high use of this medium among undergraduate students.

With regard to career problems, correlations with cyberbullying were not found. This indicates that cyberbullying has no influence on career problems, perhaps because these kinds of problems are related to future career inspirations, and not to the day-to-day aspects of a student's life.

In the social field, it was found that interpersonal problems such as integration into the social environment, forming a support network, and managing new social freedoms, were related to cyberbullying via social networking sites. This finding is consistent with the high use of social networking sites, the purpose of the medium, and the reported episodes of cyberbullying in that medium.

Family problems were also related to cyberbullying perpetrated by all kinds of media. This may indicate that as cyberbullying through the use of email, instant messaging, chat rooms, text messaging, and social networking sites increases, so do family problems. This could be due to the strong influence that cyberbullying generates in all the frameworks of students, including their families.

Finally, in the emotional field, correlations between cyberbullying perpetrated by all kinds of media and substance abuse were found. This may indicate that as cyberbullying through the use of email, instant messaging, chat rooms, text messaging, and social networking sites increases, so does substance abuse. This is important because cyberbullying may be another risk factor for increasing the probability of substance abuse.

Depression and suicidal ideation were significantly related to the same media – email instant messaging and chat cyberbullying – suggesting that depression may lead to a decision of suicide as a solution to the problem. Previous findings support the above that being an undergraduate student – a victim of cyberbullying emerges as an additional risk factor for the development of depressive symptoms ( Myers and Cowie, 2017 ). Also Selkie et al. (2015) reported among 265 female college students, being engaged in cyberbullying as bullies, victims, or both led to higher rates of depression and alcohol use.

Relationships between anxiety and cyberbullying, through all the media, were not found although Schenk and Fremouw (2012) found that college student victims of cyberbullying scored higher than matched controls on measures of depression, anxiety, phobic anxiety, and paranoia. This may be because it was demonstrated that anxiety is one of the most common reported mental health problems in all undergraduate students, cyberbullied or not.

Self-esteem problems were significantly related to cyberbullying via instant messaging, social networking sites, and text messaging. This may suggest that as cyberbullying through instant messaging, social networking sites, and text messaging increases, so do self-esteem problems. This is an important finding, given that these were the media with more reported episodes of cyberbullying.

5. Conclusions

This findings of this study revealed that cyberbullying exists in colleges and universities, and it has an influence on the academic, social, and emotional development of undergraduate students.

It was shown that cyberbullying is perpetrated through multiple electronic media such as email, instant messaging, chat rooms, text messaging, and social networking sites. Also, it was demonstrated that students exposed to cyberbullying experience academic problems, interpersonal problems, family problems, depression, substance abuse, suicidal ideation, and self-esteem problems.

Students have exhibited clear preferences towards using the Internet as a medium and utilize it with great frequency in their everyday lives. As more and more aspects of students' lives are conducted online, and with the knowledge that excessive use may have consequences for them, it is important to study the phenomenon of cyberbullying more deeply.

Because college students are preparing to enter the workforce, and several studies have indicated a trend of cyberbullying behavior and victimization throughout a person's lifetime ( Watts et al., 2017 ), the concern is these young adults are bringing these attitudes into the workplace.

Finally, cyberbullying is not only an adolescent issue. Given that studies of cyberbullying among undergraduate students are not fully developed, although existence of the phenomenon is proven, we conclude that the college and university population needs special attention in future areas of research. As it has been indicated by Peled et al. (2012) that firm policy in regard to academic cheating reduces its occurrence, colleges should draw clear guidelines to deal with the problem of cyberbullying, part of it should be a safe and if needed anonymous report system as well as clear punishing policy for perpetrators.

As there's very little research on the effect of cyberbullying on undergraduates students, especially in light of the availability of hand held devices (mainly smartphones) and the dependence on the internet for basically every and any activity, the additional data provided in this research adds to the understanding of the effect of cyberbullying on the welfare of undergraduate students.

Declarations

Author contribution statement.

Yehuda Peled: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

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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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Published : 12 August 2022

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DOI : https://doi.org/10.1007/s42380-022-00139-5

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