a Numbers (percentages) of students within each country, (traditional bully-victim categories defined according to involvement in bullying behaviours once a week or more often in the past 3 months).
Across both samples, students' self-reported frequency of traditional bullying perpetration/victimisation were used to categorize participants (cut-off: at least once a week): traditional victims (10.0%), bully-victim (3.6%), perpetrators (9.2%), and non-involved (77.2%). In addition, significant gender differences were found with more boys reporting they were frequently perpetrators (12.9%) than girls (5.9%), χ 2 = 31.1, N = 1666, p < .001. When country specific frequencies were examined (Table (Table1), 1 ), significantly more Swiss participants reported bullying others than did their Australian counterparts (14.5% versus 7.7%), χ 2 = 20.9, N = 1666, p < .001.
Country and gender differences regarding the other variables are reported in the multivariable analyses below.
Both types of bullying and victimisation were significantly associated with each other (see Table Table2 2 and Table Table3). 3 ). These relationships remained statistically significant (all p < .01) when examined by country, with stronger associations observed in the Australian sample. When comparing the traditional bully-victim categories, 41% of (traditional) bullies, 59% of bully-victims, 30% of victims and 16% of non-involved students reported perpetrating cyber-bullying behaviours at least once or twice. Thirty-nine percent of (traditional) victims, 50% of bully-victims, 22% of bullies and 17% of non-involved students were exposed to cyber-bullying behaviours at least once or twice. The association between bullying behaviour and mental health revealed some interesting results with depressive symptoms being most strongly correlated with traditional victimisation (Spearman's rho = .26 Australian sample, rho = .24 Swiss sample) and cyber-victimisation (rho = .22 Australian sample, rho = .12 Swiss sample).
Bivariate associations between study variables: Complete sample
Gender (female) | .00 | -.04 | -.13** | .09* | .01 | .07** |
Age | -- | .00 | .13** | .02 | .14** | .14** |
Being a victim | -- | .16** | .24** | .18** | .26** | |
Being a bully | -- | .10** | .28** | .12** | ||
Cyber-victimisation | -- | .35** | .18** | |||
Cyber-bullying | -- | .24** |
Note: Spearman's rho calculated for correlations involving cyber-victimization, cyber-bullying and depressive symptoms, Pearson's correlation calculated for all others
*p < .05, **p < .01
Bivariate associations between study variables: Australian versus Swiss sample
| |||||||
---|---|---|---|---|---|---|---|
Gender | -- | .06 | -.07 | -.12* | .00 | -.16** | .24** |
Age | -.03 | -- | -.15** | .07 | -.06 | .04 | .09 |
Being a victim | -.03 | .05 | -- | .06 | .14** | .07 | .24** |
Being a bully | -.14** | .13** | .20** | -- | .00 | .19** | .05 |
Cyber-victimisation | .06* | .08** | .27** | .14** | -- | .35** | .12* |
Cyber-bullying | .00 | .06* | .21** | .32** | .46** | -- | .02 |
Depressive symptoms | -.01 | .10 | .26** | .11** | .22** | .24** | -- |
Note: Spearman's rho calculated for correlations involving cyber-victimisation, cyber-bullying and depressive symptoms, Pearson's correlation calculated for all others
*p < .05, **p < .01 two sided tests
Next, two tobit regression analyses were conducted to analyse differences between those who use traditional methods to bully, those who are victimised, the combined group (hereafter bully-victims for brevity) and non-involved students in terms of their tendency to cyber-bully others and be cyber-victimised (as log-transformed linear dependent variables). Age and gender and country were entered as control variables. As we were interested in whether country moderates the associations, location (i.e., Switzerland or Australia) was entered as an interaction effect in a first model.
The bully/victim categorization interaction effect with country was found not to be significant (χ 2 [3]= 6.3, p = .098) and was thus dropped from the model The subsequent analysis yielded significant main effects for the bully/victim categorization, gender and country (see Table Table4). 4 ). As is evidenced by the positive sign for the Z statistic, girls reported higher levels of cyber-victimisation than boys (z = 4.75, p < .001). The Australian students reported being more frequently cyber-victimised than the Swiss students (z = 4.46, p < .001). All of the traditional bully/victim behaviour categories differed significantly from each other (see also Table Table5). 5 ). Bully-victims and victims reported higher levels of cyber-victimisation than non-involved students and bullies, of these victims had lower scores on cyber-victimisation than the bully-victims. Students who indicated they bullied others by traditional means reported higher levels of being cyber-victimised than those non-involved in traditional bullying behaviours.
Results of the tobit regression predicting cyber-victimisation and cyber-bullying
Z | Sig | Z | Sig | Z | Sig | Z | Sig | |
---|---|---|---|---|---|---|---|---|
Gender - female | 4.75 | < .001 | 1.02 | .307 | 3.14 | .002 | 2.79 | .005 |
Age | 1.48 | .138 | .67 | .502 | 3.58 | < .001 | 3.31 | .001 |
Country - Australia | 4.46 | < .001 | 4.11 | < .001 | -3.46 | .001 | -4.36 | < .001 |
Trad. bully/victim behaviors | ||||||||
Bullies vs non-involved | 2.50 | .012 | 9.32 | < .001 | 2.47 | .014 | 1.86 | .063 |
Victims vs non-involved | 8.31 | < .001 | 4.79 | < .001 | 9.89 | < .001 | 8.38 | < .001 |
Bully-victims vs non-involved | 8.96 | < .001 | 10.6 | < .001 | 8.89 | < .001 | 5.60 | < .001 |
Bullies vs victims | -3.83 | < .001 | 3.64 | < .001 | -5.18 | < .001 | -4.53 | < .001 |
Bullies vs bully-victims | -5.88 | < .001 | -3.48 | .001 | -6.18 | < .001 | -4.00 | < .001 |
Victims vs bully-victims | -3.02 | .002 | -6.31 | < .001 | -2.33 | .020 | -0.68 | .496 |
Cyber-victimisation | 4.83 | < .001 | ||||||
Cyber-bullying | 1.52 | .127 |
Note: Cyber-victimisation: R 2 = 14.0%; Cyber-bullying: R 2 = 16.5%; Depressive symptoms (M1): R 2 = 12.8%; Depressive symptoms (M2): R 2 = 16.1%
Summary statistics for cyber-victimisation, cyber-bullying and depressive symptoms by traditional bully/victim categorization
Bully-victims | 0.86 | 1.309 | 0.86 | 1.174 | 1.09 | 1.040 |
Victims | 0.37 | 0.716 | 0.14 | 0.328 | 0.79 | 0.894 |
Bullies | 0.10 | 0.250 | 0.37 | 0.705 | 0.42 | 0.647 |
Non-involved | 0.07 | 0.215 | 0.06 | 0.171 | 0.28 | 0.507 |
A non-significant interaction was also found for cyber-bullying between country and bully/victim categorization (χ 2 [3] = 4.7, p = .192) Further analysis yielded significant effects for the bully/victim categorization (with all comparisons between categories significant) and country (see Table Table4). 4 ). Those who bullied using traditional methods (bullies and bully-victims) reported higher levels of cyber-bullying than those victimised or not involved, with bully-victims reporting higher frequencies than bullies (see also Table Table5). 5 ). Additionally, the Australian students tended to report more frequently engaging in cyber-bullying behaviours than the Swiss students.
To analyse differences between traditional bullies, victims, and bully-victims in relation to depressive symptoms, the same modelling procedure as described above was used. In the first analysis, only traditional bully/victim categorization was used (including a test of the interaction with country) with age and gender entered as control variables. In the second analysis, cyber-bullying and cyber-victimisation (as well as their interactions with country) were entered as additional independent variables.
The analysis found that the effect of bully-victim categorization was not moderated by country (χ 2 [3] = 6.0, p = .113). The interaction term was dropped from the analyses. However, bully-victim categorization was a significant predictor of depressive symptoms. In addition, significant gender and country effects emerged (see Table Table4). 4 ). Female students reported higher levels of depressive symptoms (z = 3.14, p = .002) whilst the Australian students had lower scores on average than the Swiss (z = -3.46, p = .001). When comparing the traditional bully-victim categories, all were significantly different from each other, with bully-victims having the highest levels of symptoms, followed by victims, then bullies; non-involved students had the least depressive symptoms (see also Table Table5 5 ).
First, the interactions between each of cyber-bullying and cyber-victimisation and country were tested to assess whether their association with depressive symptoms differed in Australia and Switzerland. As neither of these interaction effects reached significance (cyber-victimisation*country: z = .39, p = .697; cyber-bullying*country: z = 1.76, p = .078), they were dropped from the final model. Upon entering cyber-bullying and cyber-victimisation as additional independent variables, the main effects of traditional bully-victim behaviours remained the same (see Table Table4), 4 ), except that the comparison between bullies and non-involved students and the comparison between victims and bully-victims were no longer significant. In addition, cyber-victimisation was a significant predictor of depressive symptoms, the more frequent the victimisation the higher the level of depressive symptoms (z = 4.83, p < .001).
This study examined the relationship between bullying and victimisation and symptoms of depression in adolescents from two different countries, Switzerland and Australia. Particular attention was paid to different forms of bullying behaviour - specifically traditional forms of bullying (including physical or verbal harassment) and cyber-bullying (using the Internet and/or mobile phone). While the association between traditional and cyber forms of bullying is established [ 49 ], to date it remains unclear if being cyber-victimised (over and above traditional victimisation) is associated with increased symptom endorsement.
Although in its relative infancy, the emergent research literature describing the outcomes associated with cyber-bullying/cyber-victimisation is largely consistent with the traditional bullying literature illustrating the robust negative relationship between all forms of bullying/victimisation and mental health. However, what has not yet been clearly described is the cumulative effect of being bullied via traditional and cyber means on the mental health of young people [ 6 ]. Thus, the third aim of this study was to investigate whether in adolescents, cyber-victimisation is an independent predictor of depressive symptoms, after accounting for self-reported traditional bullying victimisation and to determine the influence of study location (i.e., country) on this association.
The first hypothesis, which proposed a relationship between traditional and cyber forms of bullying and victimisation, was supported with statistically significant relationships between traditional and cyber forms of bullying perpetration and victimisation in the expected direction. Importantly, significant correlations were found between cyber-victimisation and gender (female), age, traditional bullying perpetration and victimisation. Furthermore, as participants aged, their self-reported bullying perpetration (traditional and cyber) increased, a relationship that remained significant only in the Australian sample when country-specific report was examined. Overall, all associations were stronger in the Australian sample.
These results add to the theoretical [ 5 ] and other empirical evidence [ 1 , 4 , 36 - 39 ] demonstrating the relationship between traditional and cyber forms of bullying perpetration and victimisation. In accordance with other studies, our findings suggest that traditional and cyber-bullying form part of the same cluster of socially inappropriate behaviours and argue for a behavioural versus technical approach to intervention programs.
It was also hypothesized that those victimised using traditional methods (victims and bully-victims) would endorse more symptoms of depression than those who only reported bullying perpetration. Support for this hypothesis was found demonstrating that students who reported being victimised and bullying others as well as those only victimised were more likely to report depressive symptoms than were those who reported bullying perpetration only. This result was not moderated by country, indicating that the associations were comparable in both countries.
Finally, it was hypothesized that cyber-victimisation would represent an additional risk factor - independent of traditional victimisation - for the development of symptoms of depression. Strong support was found for the independent association that cyber-victimisation has with symptoms of depression over and above traditional bullying victimisation i.e. cyber-victimisation accounts for a significant amount of the variation in depressive symptoms even after controlling for possible effects of traditional victimisation. Importantly, this association was not moderated by country, which suggests that the relationship is not culturally dependent.
However, several differences between countries were found. For example, while Swiss students were more likely to report bullying others, the Australian students who bully others were more likely to report also using cyber-strategies. Despite these differences, it was demonstrated that cyber-victimisation was a significant predictor of depressive symptoms - a result that was culturally independent. This result suggests an additional negative mental health status associated with being exposed to bullying via technology, over and above that of being victimised by traditional means. Although fewer students reported being cyber-bullied via technology than traditional methods in both countries, clearly the inclusion of technology represents a risk factor for significantly higher rates of internalizing disorders for those victimised using both cyber and traditional methods.
The implications of these findings are important (e.g., for intervention programs) and demonstrate the scope of negative impact associated with cyber-victimisation. It is suggested that certain features of cyber-bullying (e.g., anonymity of perpetrator, accessibility of victim) present additional and difficult challenges for young people who are victimised [ 49 ]. It is often assumed that these challenges could contribute to a worsened mental health state for those victimised and the results of this study provide evidence in support of this.
Furthermore, some of the cyber-bullying strategies employed (e.g., nasty comments on SNS profiles) [ 4 ] mean that the audience potentially aware of the harassment is significantly larger. For example, if mean and nasty comments are posted on a SNS profile (social networking sites) or if an embarrassing picture is posted and the victim is identified in the picture by name (i.e., being tagged ), all people in their network, in addition to other networks, can potentially see that humiliating content. Therefore, strategies against cyber-bullying should also include educating students about privacy settings and safe internet/mobile practices. Given the difficulty in removing comments or pictures from the Internet and the permanence of information shared online, it is not surprising that cyber-victimisation represent an additional and independent risk factor for the development of depressive symptomatology. Further investigation is needed to clarify if specific elements of cyber-victimisation that are associated with poorer mental health outcomes for young people. For example, what is the impact of bullying via social networking sites given comments, pictures, and video can be viewed by a larger network (i.e., more students). Nonetheless, the results of this study raise important questions, as well as concerns, for those young people experiencing mental health issues in addition to bullying via traditional and cyber methods.
There were a number of strengths to this study. This was the first study to describe cultural similarities in relation to the impact of cyber-victimisation on depressive symptom endorsement. Despite some cultural differences (e.g., more Australian students reported using multiple strategies to bully (traditional and cyber) compared to Swiss students), the evidence demonstrating the additive effect of cyber-victimisation on mental health is an important result. Furthermore, the (culturally independent) predictive nature of cyber-victimisation on depressive symptoms provides an important insight into the influence of technology on young people.
Overall, there were some limitations with this study. For example, some items that assessed bullying and victimisation were worded differently between the two data collection countries. Moreover, there were certain differences in the wording of response categories and number of items in both samples. Regarding cyber-bullying/victimisation, we found a significant difference between Swiss and Australian students regarding their use of cyberstrategies to bully others (Australians reporting higher levels of cyber-bullying/victimisation). This finding has important methodological implications. Swiss students reported on two rather global items on cyber-bullying, whereas Australian students reported on five different behavioural descriptors of cyber-bullying. This might have lead to an underreporting of cyber-bullying in Swiss students. Studies in traditional bullying research have shown that global items result in lower prevalence rates of bullying than specific behavioural items [ 50 ].
Regarding depressive symptoms, it is important to know that although Australian students reported on more items than Swiss students, the same number of symptoms were assessed (i.e. the Australian students reported on two items for each symptom, Swiss students on 1-2 items). Nevertheless, we found a significant country effect on depressive symptoms. We assume that these country differences are mainly due to methodological differences. It is unlikely that the differences are culturally-based given the similarities between Switzerland and Australia in relation to the prevalence of depressive symptomatology [ 51 , 52 ].
There were some sample limitations (Swiss sample comprised students whose teachers volunteered while the Australian sample is comprised of students at religious-affiliated schools only), however, we do not anticipate that the associations examined would differ markedly from those in the general student population. Although there were some differences in sample demographics (e.g. age), these did not have an impact on the relationship between cyber-victimisation and self-reported depressive symptoms. Moreover, samples were highly similar regarding their access to technology. Other limitations concern the nature of the data collected. First, all measures were self-reports. Second, as with all cross-sectional studies the causal direction of the relationships cannot be determined, and thus our focus has been on associations between the variables involved.
In conclusion, this study provided evidence of a significant association between traditional and cyber forms of bullying behaviours. We demonstrated that, although several cultural differences exist between Swiss and Australian participants in relation to bullying and victimisation, the relationship between cyber-victimisation and increased endorsement of depression symptoms was culturally independent.
The authors declare that they have no competing interests.
SP and JD were responsible for the conceptual background of the paper, analyzed and interpreted the data and drafted the manuscript. TS analysed and interpreted the data. DC is grant-holder, conceived and directed the Australian study, and was actively involved in writing up the manuscript. All authors read and approved the final manuscript.
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This study aims to investigate the mechanism of peer bullying on adolescent Internet addiction and to verify the mediating role of loneliness and the moderating role of physical activity. The results of this study are utilized to propose theoretical references for the mitigation of adolescent Internet addiction. Adolescent peer bullying, loneliness, Internet addiction and physical activity were measured by scales. A total of 618 valid questionnaires were collected from eight primary and secondary schools in Shandong Province. Mediated and moderated modeling was performed using the PROCESS plug-in for SPSS. Peer bullying directly and positively predicted Internet addiction (β = 0.023, t = 4.412, p < 0.001). Peer bullying had a significant effect on loneliness (β = 0.022, p < 0.001), and loneliness had a significant effect on Internet addiction (β = 0.017, p < 0.001), establishing the mediating role of loneliness. Meanwhile, physical activity moderated the effect of peer bullying on loneliness (β = 0.033, p < 0.001) and loneliness on internet addiction (β = 0.026, p < 0.001), in addition to moderating the effect of peer bullying on internet addiction (β = 0.034, p < 0.001), establishing the moderating role of physical activity. Loneliness mediates the relationship between adolescent peer bullying and Internet addiction, while physical activity moderates the direct and indirect pathways of adolescent peer bullying and Internet addiction.
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This study was conducted by the China Social Science Foundation Program (20BYT081).
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School of Physical Education, Shandong University, Jinan, 250061, China
Chengkai Feng, Zhenguo Shi, Yuge Tian & Chao Ma
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Conceptualization: [Chengkai Feng, Zhenguo Shi]; Methodology: [Chao Ma]; Formal analysis and investigation: [Chengkai Feng,Yuge Tian]; Writing—original draft preparation: [Chengkai Feng]; Writing—review and editing: [Yuge Tian].
Correspondence to Zhenguo Shi .
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The study was conducted in accordance with the Declaration of Helsinki and informed consent was obtained from all subjects and/or their legal guardians.
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Feng, C., Shi, Z., Tian, Y. et al. Peer bullying and Internet addiction among Chinese adolescents: a moderated mediation model. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06512-5
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Published : 22 August 2024
DOI : https://doi.org/10.1007/s12144-024-06512-5
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Here, we provide an extensive empirical test of the hypothesis that bullying might carry an evolutionary advantage for perpetrators, utilizing data from three cohorts, of which two long‐term: The National Child Development Study (NCDS) has followed participants until age 55, the British Cohort Study (BCS70) has followed participants until age ...
Bullying involves repeated hurtful actions between peers where an imbalance of power exists [].Arseneault et al. [] 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 ...
Abstract. During the school years, bullying is one of the most common expressions of violence in the peer context. Research on bullying started more than forty years ago, when the phenomenon was defined as 'aggressive, intentional acts carried out by a group or an individual repeatedly and over time against a victim who cannot easily defend him- or herself'.
Most bullying prevention programs incorporate a range of strategies, but we know little about which strategies actively reduce bullying and whether some may have iatrogenic effects. Questions have been raised about programs that involve working with peer bystanders and whether some strategies stigmatize victims. In this article, I propose three ...
This article examines alternative and supplementary ways in which theorists and researchers have sought to account for bullying behavior among students in schools. Contemporary explanations acknowledge the variety, complexity, and interactivity of both person and environmental factors in determining acts of bullying in schools. Two explanatory models or frameworks are described: (i) an ...
This article provides an introductory overview of findings from the past 40 years of research on bullying among school-aged children and youth. Research on definitional and assessment issues in studying bullying and victimization is reviewed, and data on prevalence rates, stability, and forms of bullying behavior are summarized, setting the ...
Bullying victimization and perpetration, prevalent negative social events in the lives of many adolescents, may degrade the opportunity for adolescents to experience a meaningful life, but this hypothesis to date has remained untested. It is also unclear what may aid in the promotion of meaning in adolescents.
1. Psychological: Being a victim of bullying was associated with increased depression, anxiety, and psychosis. Victims of bullying reported more suicidal thinking and engaged in greater self ...
Bullying is a complex social dynamic that can best be understood by using various theoretical frameworks. The current article uses social capital theory, dominance theory, the theory of humiliation, and organizational culture theory to better understand the motivations behind bullying behavior, bullying's negative effects on victims, and how school culture and climate play a role in the ...
WASHINGTON — A special issue of American Psychologist ® provides a comprehensive review of over 40 years of research on bullying among school age youth, documenting the current understanding of the complexity of the issue and suggesting directions for future research. "The lore of bullies has long permeated literature and popular culture. Yet bullying as a distinct form of interpersonal ...
Reviewing school bullying research: empirical. findings and methodical considerations. Hsi-Sheng W ei ∗ Chung-Kai Huang ∗∗. Abstract. This article provides a comprehensive review of previous ...
Bullying in school is a common experience for many children and adolescents. Such experiences relate to a range of adverse outcomes, including poor mental health, poorer academic achievement, and anti-social behaviour (Gini et al., 2018; Nakamoto & Schwartz, 2010; Valdebenito et al., 2017).Bullying research has increased substantially over the past 60 years, with over 5000 articles published ...
Abstract. The current study examined the relations among self-esteem, approving normative beliefs about bullying, school climate, and bullying perpetration using a large, longitudinal sample of children from elementary, middle, and high school.
Bullying is a complex social dynamic that can best be understood by using various theoretical frameworks. The current article uses social capital theory, dominance theory, the theory of ...
1. Introduction. Bullying is a type of aggressive behavior that implies intentionality, repetition and an imbalance of power between the aggressor and the victim, in a way that the victim is incapable of defending against the aggressor [].Cyberbullying occurs when bullying behaviors are carried out using electronic devices [], situation in which the repetition, the imbalance of power and the ...
For example, although bullying may be partly explained by a lack of tolerance for racially and sexually diverse youth, a campaign to change the attitudes of school children may have little effect in the short term on the plight of these youth; what is required is theory that helps to understand and ameliorate the effects on the actual children ...
4. Psychological bullying: harassment, threats and intimidation, humiliation and rejection from the group. 5. Bullying in social relations: preventing some individuals from exercising certain activities or reject their friendship or spreading rumors about others. 6. Properties Bullying: taking other people's things and dispose, or destroy. 2.
The first hypothesis, which proposed a relationship between traditional and cyber forms of bullying and victimisation, was supported with statistically significant relationships between traditional and cyber forms of bullying perpetration and victimisation in the expected direction.
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 meth-ods limits understanding of the contexts and experiences of bullying (Hong & Espelage, 2012; Patton et al., 2017).
Regarding academic performance, statistically significant differences were obtained that confirm the hypothesis that performance or average grade varies according to the category of bullying in ...
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Violence and bullying increased 2% and 4%, respectively, from 2021 to 2023, with about one in ten avoiding school for safety concerns and two in ten being bullied.
Sixth, our central aim was to test Volk's hypothesis of bullying as evolutionary adaptation (Volk et al., 2012) and we selected three cohorts where outcomes were assessed at different ages. Whereas we examined moderation by popularity and victimization - assessed at the same time as bullying perpetration, we did not explore potential ...
The nature of the relation between victimization of bullying and social information processing is unclear. The prevention hypothesis predicts that victims focus more on negative social cues to prevent further escalation. In contrast, the reaffiliation hypothesis predicts that victims focus more on positive social cues to restore the social situation. Alternatively, the desensitization ...
Hypothesis - Free download as Word Doc (.doc), PDF File (.pdf), Text File (.txt) or read online for free. The document outlines 5 hypotheses regarding bullying behaviors. Hypothesis 1 proposes that gender is not differentially related to involvement in physical, verbal, and cyber bullying or victimization. Hypothesis 2 proposes that emotional and behavioral adjustment factors like depression ...
Theoretical framework. This study constructs its theoretical framework based on the S-O-R theory, which emphasizes how external stimuli can elicit behavioral responses by influencing individuals' psychological and cognitive processes (Kini et al., 2024).In this context, peer bullying and physical activity serve as stimuli, which will affect the generation of loneliness and ultimately ...