Next Article in Journal
Oxytocin Receptor Single-Nucleotide Polymorphisms Are Related to Maternal–Infant Co-Occupation and Infant Sensory Processing
Previous Article in Journal
Teaching Parents via Online Asynchronous Training to Use Speech-Generating Devices with Their Autistic Children: A Pilot Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Children
Volume 11
Issue 10
10.3390/children11101193
children-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analysis of Prevalence and Related Factors of Cyberbullying–Victimization among Adolescents

by
Jun Ma
1,
Liyan Su
1,
Minhui Li
1,
Jiating Sheng
1,
Fangdu Liu
1,
Xujun Zhang
1,*,
Yaming Yang
2 and
Yue Xiao
2
1
Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
2
Yixing Center for Disease Control and Prevention, Yixing 214200, China
*
Author to whom correspondence should be addressed.
Children 2024, 11(10), 1193; https://doi.org/10.3390/children11101193 (registering DOI)
Submission received: 27 August 2024 / Revised: 23 September 2024 / Accepted: 27 September 2024 / Published: 29 September 2024
(This article belongs to the Section Pediatric Mental Health)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background/Objectives: Cyberbullying is an increasingly serious issue that negatively impacts the mental and physical health of adolescents. This study aims to report the prevalence rates of adolescent cyberbullying–victimization and its associated related factors, providing a scientific basis for targeted efforts to protect the mental and physical well-being of adolescents; Methods: From March to May 2019, there were 13 high schools and 33 middle schools in Yixing, with a student ratio of 2:1 between middle and high school. Using a random cluster sampling method, we selected four high schools and three middle schools based on this ratio, resulting in a total of 13,258 students. We conducted a survey using a self-designed questionnaire to investigate the experiences of adolescents with cyberbullying and victimization, comparing the differences in cyberbullying–victimization based on various demographic characteristics. Additionally, we employed a multifactorial logistic regression model to analyze the associated factors; Results: The rate of adolescents who declared themselves as cyberbully-victims is 2.9%. The results of the logistic regression analysis indicate that being male, having both parents working outside the home, experiencing occasional or large conflicts among family members, being subjected to punishment-and-abuse child discipline, always or often using social software (websites), enjoying playing single or multiplayer games, self-smoking, and self-drinking were associated with a higher likelihood of being a cyberbully-victim (p < 0.05); Conclusions: Adolescent cyberbullying–victimization is affected by personal, family, and social factors. Therefore, comprehensive strategies and measures are needed to intervene in this problem.

1. Introduction

The rapid development of the Internet has facilitated many activities in our daily lives, supporting activities such as learning and communication. However, it has also led to negative phenomena, such as cyberbullying [1,2]. Cyberbullying refers to the deliberate and sustained use of electronic devices or other forms of digital communication technology to harm or attack others [3,4,5]. Approximately one-third of global Internet users are under the age of 18 [6,7], making them vulnerable to both experiencing and perpetrating cyberbullying [8,9,10]. Studies [11,12,13] have reported that the prevalence of cyberbullying among adolescents in China ranges from 31.4% to 59.0%. Similarly, international studies [2,14,15] have found that the prevalence among Canadian adolescents is between 38.0% and 48.0%, and in the United States it ranges from 13.9% to 57.5%. A 2023 systematic review [3] suggested that the COVID-19 pandemic may have influenced the prevalence of cyberbullying and cybervictimization among adolescents worldwide, with some studies reporting an increase in certain regions, such as Asia and Australia [16], while others observed a decline in Western countries [17]. These changes could be attributed to varying contextual factors, such as school closures and increased online activity during the pandemic.
Unlike traditional bullying, cyberbullying is both virtual and covert, posing significant threats to the physical and mental development of children and adolescents, and has become a critical public health concern [18,19]. Previous research on cyberbullying has predominantly focused on the perpetrators, with limited attention given to victims, particularly those who are both bullies and victims [2,20,21]. The underlying reasons for and influencing factors of this phenomenon have not been adequately discussed. This group simultaneously plays dual roles as both victims and perpetrators, leading to greater psychological stress and distress [22,23,24]. Therefore, studying cyberbullying–victimization can help elucidate the mechanisms behind such behaviors and provide a theoretical basis for developing more targeted intervention strategies.
This study is based on Bronfenbrenner’s [25] social ecological theory, which posits that cyberbullying is not a singular phenomenon but rather the result of multiple factors interacting, including individual characteristics, family environment, peer relationships, and school climate. Previous studies [26,27,28] have shown that factors such as gender, age, academic performance, and behavioral habits (e.g., smoking and drinking) are closely associated with the occurrence of bullying behavior. For example, male adolescents are more likely to become bullies than female adolescents [29,30,31,32], although females are more inclined to engage in indirect forms of bullying [29,30,31,32]. Furthermore, the family environment plays a crucial role in preventing bullying. Close relationships with parents, perceived emotional support, parental supervision and involvement, and the establishment of family rules are all considered important protective factors against both cyberbullying and traditional bullying [33,34,35,36]. At the school level, positive peer relationships and an inclusive school climate are also recognized as key factors in preventing bullying behavior [37,38]. The objective of this study is to examine the prevalence of cyberbullying and victimization, along with their associated factors, to provide a scientific basis for safeguarding the physical and mental well-being of adolescents.

Definition of the Indicator

Cyberbullying can manifest in the following forms [39,40,41,42]: ① experiencing verbal insults, threats, and other forms of virtual violence in online spaces; ② being subjected to harassing messages, images, and other disruptive content in virtual environments; ③ having private photos or videos leaked online to humiliate, threaten, or mock the individual. In this study, the term “cyberbullying victimization” is defined as experiencing any of the aforementioned forms of cyberbullying at least once in the past year, without having engaged in any such behaviors towards others.
Perpetration of cyberbullying [39,40] can take the following forms: ① sending insulting language, threats, or other forms of virtual violence to others in online spaces; ② sending harassing messages, images, or other disruptive content to others in virtual environments; ③ leaking private photos or videos of the victim online to humiliate, threaten, or mock them. In this study, the term “cyberbullying perpetration” is defined as having engaged in any of the aforementioned forms of cyberbullying at least once in the past year, without being subjected to any such behaviors.
The term “ cyberbullying–victimization” is defined as experiencing and perpetrating cyberbullying in the past year, characterized by encountering any of the aforementioned forms of cyberbullying at least once and engaging in any of these behaviors at least once.

2. Materials and Methods

2.1. Subject of Investigation

From 1 March to 31 May 2019, there were 13 high schools and 33 junior high schools in Yixing, with a student distribution ratio of 2:1 between junior high and high school students. This study employed a random cluster sampling method, selecting 4 high schools from the 13 in Yixing and 3 junior high schools from the 33, resulting in a total of 7 schools and 13,258 students as study participants. A total of 13,258 questionnaires were distributed and, after excluding invalid responses, 12,770 valid questionnaires were obtained, yielding a response rate of 96.3%.
Prior to the study, informed consent was obtained from the participants and their parents or guardians. The research protocol was approved by the Ethics Review Committee of the Jiangsu Provincial Center for Disease Control and Prevention (Approval No.: JSJK2018-B025-02).

2.2. Method

The research team reviewed the primary manifestations of cyberbullying, referencing key studies, such as the China Education Tracking Survey (CEPS) project and the American Youth Risk Behavior Survey [43]. Additionally, a comprehensive literature review was conducted both domestically and internationally [44,45,46], and a self-administered questionnaire was developed. To ensure the reliability and validity of this questionnaire, it underwent rigorous evaluation, modification, and validation by relevant scholars, achieving high ratings throughout the process. Cronbach’s alpha was used to measure internal consistency, yielding a coefficient of 0.71. Content validity was assessed through correlation analysis, with coefficients ranging from 0.69 to 0.82. Prior to conducting the survey, the group provided specialized training for all relevant staff. During the survey, participants were informed about the study and participated with full consent. To address potential psychological issues among students, counselors were present in each class. Upon completion of the survey, data entry was conducted using a dual-entry system with two operators, and logical checks were performed to exclude any non-compliant questionnaires.
Participants mainly responded to the following questions regarding the frequency of different forms of cyberbullying they had experienced or perpetrated in the past 12 months. The responses were recorded on a scale from 1 to 5, where 1 represents “never”, 2 represents “once”, 3 represents “twice”, 4 represents “three times” and 5 represents “more than three times”.
General demographic characteristics: gender (male/female), ethnicity (Han/other), and educational level (middle school/high school).
Personal characteristics: smoking (yes/no), drinking (yes/no), serving as class monitor (yes/no), academic performance (excellent/above average/average/below average), preferred game type (board games/competitive games/strategy simulation games/role-playing games/do not play games), internet use time (<0.5 h/0.5–1 h/1–1.5 h/1.5–2 h/≥2 h), frequency of using social media (always/often/occasionally/never).
Family characteristics: family relationship (harmonious/average/occasional conflict/high conflict), parenting style (punishment and scolding/punishment and persuasion/scolding/persuasion/freedom), parental smoking (yes/no), parental drinking (yes/no), and whether both parents work outside of their hometown (yes/no).
School characteristics: peer acceptance (very popular/popular/somewhat unpopular/unpopular).

2.3. Statistical Analysis

Data entry was conducted using Epidata 3.0, with double entry by two individuals on separate machines to establish the database. Statistical descriptions of rates and composition ratios were performed using SPSS 25.0 software. The chi-square (χ2) test was applied to compare rates or composition ratios between groups, and a multivariate Logistic regression model was employed to analyze associated risk factors. The R software version 4.3.1 was used to create a nomogram prediction model, and the concordance index (C-index) was calculated to assess the model’s discrimination ability. Additionally, ROC curves and calibration curves were plotted to evaluate the predictive performance of the model. The significance level was set at α = 0.05.

3. Results

This study included a total of 12,770 middle and high school students, of whom 6550 were boys (51.3%) and 6220 were girls (48.7%). The cohort consisted of 8001 middle school students (62.7%) and 4769 high school students (37.3%). Among them, 7639 were only children (59.8%) and 5131 were non-only children (40.2%). Within this population, 10,723 students (84.0%) neither engaged in nor experienced cyberbullying, 1562 students (12.2%) were victims of cyberbullying only, 115 students (0.9%) were perpetrators of cyberbullying only, and 370 students (2.9%) both perpetrated and were victims of cyberbullying. Among those involved in both perpetrating and experiencing cyberbullying, 263 were boys (71.1%) and 107 were girls (28.9%); 213 were middle school students (57.6%) and 157 were high school students (42.4%).
As shown in Table 1, the prevalence of cyberbullying–victimization involvement was slightly higher among male students, those in higher grades, those who smoked or drank alcohol, those with below average academic performance, and those who were less popular among their peers (p < 0.05). In terms of media usage, adolescents who preferred playing single-player or multiplayer games, or engaging in role-playing games, exhibited a higher prevalence of cyberbullying–victimization involvement (p < 0.05). The prevalence of cyberbullying–victimization involvement also increased with longer internet usage and more frequent use of social networking or chat software (p < 0.05). Regarding family factors, adolescents from disharmonious families, those whose parents employed physical punishment or a combination of punishment and communication, those whose parents worked away from home, and those whose mothers smoked or drank alcohol had a relatively higher prevalence of cyberbullying–victimization (p < 0.05).
With adolescent cyberbully-victims as the dependent variable (0 = No; 1 = Yes), a logistic regression analysis was conducted using the following independent variables: gender (0 = Female; 1 = Male), school level (0 = Junior High; 1 = Senior High), personal alcohol consumption (0 = No; 1 = Yes), personal smoking (0 = No; 1 = Yes), academic performance (0 = Excellent; 1 = Above Average; 2 = Average; 3 = Below Average), level of peer popularity (0 = Very Popular; 1 = Quite Popular; 2 = Less Popular; 3 = Not Popular), preferred type of video game (0 = Never Play Video Games; 1 = Casual Games; 2 = Single or Multiplayer Battle Games; 3 = Simulation Strategy Games; 4 = Role-Playing Games), online time (h) (0~<0.5; 0.5~<1; 1~<1.5; 1.5~<2; ≥2), frequency of using social networking or chat apps (0 = Never; 1 = Always; 2 = Often; 3 = Occasionally), family relationship (0 = Harmonious; 1 = Average; 2 = Occasionally Conflictual; 3 = Highly Conflictual), parenting style (0 = Permissive; 1 = Persuasive; 2 = Punitive; 3 = Combined Punitive and Persuasive), father’s smoking status (0 = No; 1 = Yes), father’s alcohol consumption (0 = No; 1 = Yes), mother’s smoking status (0 = No; 1 = Yes), mother’s alcohol consumption (0 = No; 1 = Yes), and whether both parents work out of town (0 = No; 1 = Yes).
The results showed that factors associated with a higher risk of adolescent cyberbully-victims include being male, having both parents working out of town, having an average or occasionally conflictual family relationship, experiencing a punitive parenting style, personal smoking, personal alcohol consumption, always using social networking apps, and preferring single or multiplayer battle games. See Table 2.
A nomogram predictive model for adolescent cyberbully-victims was constructed using variables with statistically significant effect sizes in the multivariate analysis (p < 0.05).
As shown in Figure 1, the nomogram model was established based on the factors identified through multivariate analysis. Each value of a factor corresponds to a score on the first row of the nomogram labeled “Individual Score”. The scores for all factors are summed to yield a total score, which then corresponds to a probability of cyberbully-victims’ risk on the bottom row of the nomogram. After 1000 bootstrap internal validations, the model demonstrated a concordance index (C-index) of 0.82 (95% CI: 0.80–0.84), indicating good discriminatory ability.
As shown in Figure 2, the calibration curve indicates that the predicted probabilities of cyberbully-victims closely match the observed values, with an average absolute error of 0.01. In Figure 3, the black solid line represents the ROC curve of the predictive model, with an area under the curve (AUC) of 0.82 (95% CI: 0.80–0.84), demonstrating strong predictive capability.

4. Discussion

This study found that the prevalence of cyberbullying–victimization status among adolescents is 2.9%. This rate is slightly lower than the findings of Chang et al. [47], who reported an 11.2% prevalence among 2992 high school students in Taiwan over the past year, and Albert et al. [48], who reported a 12% prevalence among 502 students in three middle schools in the United States. However, our results are consistent with those of Chen Jiayi et al. [49], who found a 2.8% prevalence among 2931 adolescents in Western China, and Goebert et al. [50], who reported a 3% prevalence among 677 multi-ethnic high school students in Hawaii. Additionally, Bradshaw et al. [51] conducted a 2012 online survey of 24,620 adolescents across 52 high schools in Maryland, finding a 2.3% prevalence, while Jing Wang et al. [52] reported a 1.9% prevalence among 7313 adolescents in grades 6–10 in the United States, both of which are lower than the prevalence found in our study. The variation in prevalence rates of cyberbullying–victimization status may be attributed to differences in bullying prevalence, as well as varying socioeconomic and cultural backgrounds [34]. Furthermore, the use of different assessment scales [53] and variations in the definitions and concepts of “cyberbullying” [54] may also impact study results.
Regarding gender differences in cyberbully-victims’ status, this study indicates that males are more likely to be cyberbully-victims, which aligns with some previous research findings [55]. Adolescent males often exhibit impulsive behavior and poor self-control, making them more prone to retaliate with cyberbullying after experiencing it themselves; emotional outbursts may be a significant contributing factor [56,57]. Research [58] indicates that males are more likely to exhibit aggressive behaviors in social interactions, and the anonymity and lack of constraints in online environments may further amplify these aggressive tendencies. Males are also more inclined to participate in online gaming, which could increase their chances of becoming involved in cyberbullying. However, other studies [59,60] have suggested that females may be more likely to engage in and simultaneously experience cyberbullying. This could be due to females’ tendency to use indirect forms of bullying, which the nature of cyberbullying may facilitate [61]. Additionally, females are reportedly 2.5 times more likely to experience cyberbullying than males [62], possibly because females spend more time online sharing daily activities, thereby increasing their risk of cyberbullying.
This study also shows that excessive internet use and frequent use of social media platforms are significant risk factors for cyberbullying–victimization status among adolescents. The anonymity of the internet, along with the lack of eye contact, makes adolescents more likely to engage in self-disclosure and emotional expression, which may lead to increased aggression and impulsive behavior [63]. Adolescents who frequently use social media are more likely to encounter strangers, and this high level of online interaction increases their likelihood of becoming involved in cyberbullying incidents [47]. Compared to those who are only victims of cyberbullying, cyberbully-victims spend more time online and exhibit higher levels of depression, anxiety, and loneliness [64]. While the internet provides adolescents with opportunities to acquire new knowledge and make friends [65], it also poses negative impacts, such as addiction to violent games, exposure to Pornhub, and gambling [66,67].
Adolescents who frequently use social media are more likely to encounter strangers, which may increase the likelihood of cyberbullying incidents [45]. Behaviors such as smoking and drinking can label adolescents as “troublesome”, affecting their social relationships and reducing their popularity, which may lead them to seek comfort online, thereby increasing their risk of becoming cyberbully-victims [68]. To protect adolescents, schools and parents need to implement effective preventive measures to reduce these risky behaviors and pay attention to adolescents’ social interactions to lower their risk of becoming cyberbully-victims.
In this study, adolescents who preferred single or multiplayer battle games had a higher risk of cyberbully-victim status compared to those who preferred other types of games or did not play video games at all. This may be because such games often contain elements of violence and competition, and prolonged exposure may enhance adolescents’ aggressive tendencies, making them more likely to engage in bullying or victimization behaviors in the online environment [69,70]. Additionally, the interactive and anonymous nature of these games may foster unchecked behaviors, making adolescents more prone to becoming involved in online conflicts [71]. Cheng and Lin et al. [11] found a positive correlation between exposure to violent online games and cyberbully-victims’ behavior. James et al. [72] discovered that cyberbully-victims differ from pure bullies and victims in having higher levels of family conflict, proactive/reactive aggression, lower self-control, and weaker social bonds. This supports Schwartz’s [73] hypothesis that cyberbully-victims exhibit more proactive/reactive aggression. Targeted prevention and intervention strategies should be developed for this highly aggressive cyberbullying–victimization group.
The study also suggests that poor family relationships, punitive parenting styles, and parents working away from home may increase the risk of cyberbully-victim status among adolescents. These findings are similar to those of Sourander et al. [74]. Family environment significantly impacts adolescents’ mental health and social adaptation; a lack of family cohesion, poor communication between parents and children, and family conflicts indicate a certain level of family dysfunction, leading to feelings of neglect, loneliness, and emotional distress among adolescents [75,76]. These negative experiences may drive them to seek validation and engagement in online spaces, increasing their likelihood of involvement in cyberbullying–victimization. Additionally, when parents use punitive measures, such as physical punishment or scolding, children may learn from these behaviors. According to Social Learning Theory [77], adolescents are likely to imitate their parents’ actions, viewing violence as a way to solve problems, which increases the likelihood of them engaging in cyberbullying. Without effective parental supervision, adolescents may engage in risky behaviors such as meeting online acquaintances offline, watching inappropriate videos, or managing social media accounts, which can have serious consequences. Therefore, parents need to strengthen supervision and guidance to reduce the risk of cyberbullying–victimization [78].
There is also a correlation between cyberbullying–victimization status and academic performance. Research [79] shows that adolescents who experience cyberbullying have lower classroom attention, which negatively impacts their academic performance. Graham et al. [80] found that cyberbully-victims perceive school as an unsafe place and have the lowest academic performance among all groups. This may be due to the negative effects of cyberbullying on adolescents’ mental health and learning attitudes. First, cyberbully-victims experience higher levels of depression, anxiety, and stress [22,23,24], and these negative emotions can impair their cognition and attention, ultimately affecting academic performance. Second, bully-victims often lack a sense of safety and belonging at school, which leads to decreased motivation to learn. Therefore, the impact of cyberbullying–victimization on adolescents is multifaceted and warrants the close attention of parents, teachers, and related professionals.
Schools can offer courses to improve students’ awareness of cyber-bullying and their ability to cope. At the same time, teachers should pay attention to students’ mental health, and identify and intervene with students who are at risk of cyber-bullying. For parents, the need is to strengthen supervision and guidance of the child’s network use, more communication, and understanding of children’s network activities and social status. Instead of using negative parenting stylesm such as punishment, positive communication and support are needed to help the child develop healthy social relationships and self-control. For the psychologist, we should provide psychological support and counseling for cyber-bullying–bullied teenagers to help them deal with negative emotions, and to improve their self-efficacy and social adaptability. For the relevant policy makers, we need to formulate and improve the relevant laws and regulations to strengthen the supervision and punishment of cyber-bullying, promote the network platform to assume more social responsibility, and establish reporting and handling mechanisms to ensure the safety of young people’s networking.
This study has several limitations. As it is cross-sectional in nature, causal relationships cannot be established, and the findings should be interpreted as a basis for future research. The retrospective data collection may introduce recall bias, and the use of self-reported measures increases the risk of social desirability bias, which may influence the accuracy of the reported data. Future research should adopt longitudinal designs and incorporate multi-source data from teachers and parents to reduce self-report bias. It is important to explore the characteristics and mechanisms of cyberbullying across different cultural contexts to develop more targeted intervention strategies. Additionally, investigating the role of social media platforms, as well as the technological methods used for the prevention and intervention of cyberbullying, represents a significant area for further study.

5. Conclusions

In summary, this study involved 12,770 middle and high school students, providing an in-depth analysis of cyberbullying–victimization among adolescents, along with their associated factors. The results showed that male gender, smoking and drinking behaviors, frequency of social media use, and the quality of family relationships were significantly related to the risk of cyberbullying and victimization, indicating that these variables play a critical role in the occurrence of cyberbullying–victimization.
Based on these findings, we recommend implementing multi-level intervention strategies. First, schools should develop specialized cyberbullying education programs, particularly targeting high-risk groups, to enhance their awareness of potential risks in the online environment. Second, improving family communication and support is essential to create a healthier family environment, which could reduce adolescents’ vulnerability to cyberbullying. Additionally, we suggest establishing guidelines for social media use to encourage responsible and healthy internet usage, thereby reducing the incidence of cyberbullying.
This study not only provides important empirical evidence for understanding cyberbullying–victimization among adolescents but also offers theoretical support for the development of related policies and interventions. Future research should continue to explore other potential influencing factors and evaluate the effectiveness of interventions to promote the mental health and well-being of adolescents.

Author Contributions

Conceptualization, X.Z.; methodology, X.Z.; software, L.S.; formal analysis, J.M.; survey, Y.Y.; resources, Y.X.; data curation, M.L., J.S. and F.L.; writing—original draft preparation, J.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX21_0085, SJCX22_0074).

Institutional Review Board Statement

Our study protocol was approved by the Ethical Review Committee of the Jiangsu Provincial Center for Disease Control and Prevention (batch number: JSJK2018-B025-02; date 30 November 2018).

Informed Consent Statement

Informed consent was obtained from all participants and their parental guardians prior to the start of the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to legal reason.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Sarre, R.; Lau, L.Y.; Chang, L.Y.C. Responding to cybercrime: Current trends. Police Pract. Res. 2018, 19, 515–518. [Google Scholar] [CrossRef]
  2. Zhu, C.; Huang, S.; Evans, R.; Zhang, W. Cyberbullying Among Adolescents and Children: A Comprehensive Review of the Global Situation, Risk Factors, and Preventive Measures. Front. Public Health 2021, 9, 634909. [Google Scholar] [CrossRef] [PubMed]
  3. Dooley, J.J.; Pyżalski, J.; Cross, D. Cyberbullying versus face-to-face bullying: A theoretical and conceptual review. Z. Für Psychol./J. Psychol. 2009, 217, 182–188. [Google Scholar] [CrossRef]
  4. Smith, P.K.; Slonje, R. Cyberbullying: The Nature and Extent of a New Kind of Bullying, in and out of School; Jimerson, S., Swearer, S., Espelage, D., Eds.; Routledge: New York, NY, USA; Abingdon, UK, 2010; pp. 249–262. ISBN 978-0-8058-6392-5. [Google Scholar]
  5. Bayraktar, F.; Machackova, H.; Dedkova, L.; Cerna, A.; Ševčíková, A. Cyberbullying: The Discriminant Factors Among Cyberbullies, Cybervictims, and Cyberbully-Victims in a Czech Adolescent Sample. J. Interpers. Violence 2015, 30, 3192–3216. [Google Scholar] [CrossRef] [PubMed]
  6. Baldry, A.C.; Farrington, D.P.; Sorrentino, A. “Am I at risk of cyberbullying”? A narrative review and conceptual framework for research on risk of cyberbullying and cybervictimization: The risk and needs assessment approach. Aggress. Violent Behav. 2015, 23, 36–51. [Google Scholar] [CrossRef]
  7. Keeley, B.; Little, C. The State of the Worlds Children 2017: Children in a Digital World; UNICEF: New York, NY, USA, 2017. [Google Scholar]
  8. Patchin, J.W.; Hinduja, S. Bullies Move Beyond the Schoolyard: A Preliminary Look at Cyberbullying. Youth Violence Juv. Justice 2006, 4, 148–169. [Google Scholar] [CrossRef]
  9. Smith, P.K.; Mahdavi, J.; Carvalho, M.; Fisher, S.; Russell, S.; Tippett, N. Cyberbullying: Its nature and impact in secondary school pupils. J. Child Psychol. Psychiatry 2008, 49, 376–385. [Google Scholar] [CrossRef]
  10. Ahmad, A.; Khan, M.N. Factors Influencing Consumers’ Attitudes toward Social Media Marketing. MIS Rev. 2017, 22, 21–40. [Google Scholar]
  11. Lam, L.T.; Cheng, Z.; Liu, X. Violent online games exposure and cyberbullying/victimization among adolescents. Cyberpsychology Behav. Soc. Netw. 2013, 16, 159–165. [Google Scholar] [CrossRef]
  12. Fan, C.; Chu, X.; Zhang, M.; Zhou, Z.-K. Are Narcissists More Likely to Be Involved in Cyberbullying? Examining the Mediating Role of Self-Esteem. J. Interpers. Violence 2019, 34, 3127–3150. [Google Scholar] [CrossRef]
  13. Li, J.; Sidibe, A.M.; Shen, X.; Hesketh, T. Incidence, risk factors and psychosomatic symptoms for traditional bullying and cyberbullying in Chinese adolescents. Child. Youth Serv. Rev. 2019, 107, 104511. [Google Scholar] [CrossRef]
  14. Li, Y.; Liu, Q. A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments. Energy Rep. 2021, 7, 8176–8186. [Google Scholar] [CrossRef]
  15. Farrington, D.P.; Zych, I.; Ttofi, M.M.; Gaffney, H. Cyberbullying research in Canada: A systematic review of the first 100 empirical studies. Aggress. Violent Behav. 2023, 69, 101811. [Google Scholar] [CrossRef]
  16. Trompeter, N.; Jackson, E.; Sheanoda, V.; Luo, A.; Allison, K.; Bussey, K. Cyberbullying prevalence in Australian adolescents: Time trends 2015–2020. J. Sch. Violence 2022, 21, 252–265. [Google Scholar] [CrossRef]
  17. Vaillancourt, T.; Brittain, H.; Krygsman, A.; Farrell, A.H.; Landon, S.; Pepler, D. School bullying before and during COVID-19: Results from a population-based randomized design. Aggress. Behav. 2021, 47, 557–569. [Google Scholar] [CrossRef] [PubMed]
  18. Raskauskas, J.; Huynh, A. The process of coping with cyberbullying: A systematic review. Aggress. Violent Behav. 2015, 23, 118–125. [Google Scholar] [CrossRef]
  19. Bradshaw, J.; Crous, G.; Rees, G.; Turner, N. Comparing children’s experiences of schools-based bullying across countries. Child. Youth Serv. Rev. 2017, 80, 171–180. [Google Scholar] [CrossRef]
  20. Kowalski, R.M.; Morgan, C.A.; Limber, S.P. Traditional bullying as a potential warning sign of cyberbullying. Sch. Psychol. Int. 2012, 33, 505–519. [Google Scholar] [CrossRef]
  21. Patchin, J.W.; Hinduja, S. Cyberbullying: An Update and Synthesis of the Research; Routledge/Taylor & Francis Group: New York, NY, USA, 2012; pp. 13–35. ISBN 978-0-415-89237-7/978-0-415-89236-0/978-0-203-81831-2. [Google Scholar]
  22. McLoughlin, L.T.; Spears, B.A.; Taddeo, C.M.; Hermens, D.F. Remaining connected in the face of cyberbullying: Why social connectedness is important for mental health. Psychol Sch. 2019, 56, 945–958. [Google Scholar] [CrossRef]
  23. Campbell, M.; Spears, B.; Slee, P.; Butler, D.; Kift, S. Victims’ perceptions of traditional and cyberbullying, and the psychosocial correlates of their victimisation. Emot. Behav. Difficulties 2012, 17, 389–401. [Google Scholar] [CrossRef]
  24. Kowalski, R.M.; Giumetti, G.W.; Schroeder, A.N.; Lattanner, M.R. Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychol. Bull. 2014, 140, 1073–1137. [Google Scholar] [CrossRef] [PubMed]
  25. Bronfenbrenner, U. The Ecology of Human Development: Experiments by Nature and Design; Harvard University Press: Cambridge, MA, USA, 1979; ISBN 978-0-674-22456-8. [Google Scholar]
  26. Armitage, R. Bullying in children: Impact on child health. Bmj Paediatr Open 2021, 5, e000939. [Google Scholar] [CrossRef] [PubMed]
  27. Chen, L.; Lu, R.; Duan, J.; Ma, J.; Zhu, G.; Song, Y.; Lau, P.W.C.; Prochaska, J.J. Combined Associations of Smoking and Bullying Victimization with Binge Drinking among Adolescents in Beijing, China. Front Psychiatry 2021, 12, 698562. [Google Scholar] [CrossRef] [PubMed]
  28. Hong, J.S.; Kim, D.H.; Hunter, S.C.; Cleeland, L.R.; Lee, C.A.; Lee, J.J.; Kim, J. Racial/Ethnic Bullying Subtypes and Alcohol, Tobacco, and Marijuana Use Among US Adolescents. J. Racial Ethn. Health. 2022, 9, 1443–1453. [Google Scholar] [CrossRef]
  29. Viding, E.; Simmonds, E.; Petrides, K.V.; Frederickson, N. The contribution of callous-unemotional traits and conduct problems to bullying in early adolescence. J. Child Psychol. Psychiatry 2009, 50, 471–481. [Google Scholar] [CrossRef]
  30. Atik, G.; Güneri, O.Y. Bullying and victimization: Predictive role of individual, parental, and academic factors. Sch. Psychol. Int. 2013, 34, 658–673. [Google Scholar] [CrossRef]
  31. Nocentini, A.; Menesini, E.; Salmivalli, C. Level and change of bullying behavior during high school: A multilevel growth curve analysis. J. Adolesc. 2013, 36, 495–505. [Google Scholar] [CrossRef]
  32. Li, Y.J. Effect of Parent-adolescent Conflict on Cyberbullying: The Chain Mediating Effect and Its Gender difference. Chin. J. Clin. Psychol. 2020, 28, 605–610. [Google Scholar]
  33. Lereya, S.T.; Samara, M.; Wolke, D. Parenting behavior and the risk of becoming a victim and a bully/victim: A meta-analysis study. Child Abus. Negl. 2013, 37, 1091–1108. [Google Scholar] [CrossRef]
  34. Mehari, K.R.; Moore, W.; Waasdorp, T.E.; Varney, O.; Berg, K.; Leff, S.S. Cyberbullying prevention: Insight and recommendations from youths, parents, and paediatricians. Child Care Health Dev. 2018, 44, 616–622. [Google Scholar] [CrossRef]
  35. Wang, H.; Zhou, X.; Lu, C.; Wu, J.; Deng, X.; Hong, L.; Gao, X.; He, Y. Adolescent Bullying Involvement and Psychosocial Aspects of Family and School Life: A Cross-Sectional Study from Guangdong Province in China. PLoS ONE 2012, 7, e38619. [Google Scholar] [CrossRef] [PubMed]
  36. Wang, J.; Iannotti, R.J.; Nansel, T.R. School Bullying among Adolescents in the United States: Physical, Verbal, Relational, and Cyber. J. Adolesc. Health 2009, 45, 368–375. [Google Scholar] [CrossRef] [PubMed]
  37. Varela, J.J.; Sánchez, P.A.; De Tezanos-Pinto, P.; Chuecas, J.; Benavente, M. School Climate, Bullying and Mental Health among Chilean Adolescents. Child Indic. Res. 2021, 14, 2249–2264. [Google Scholar] [CrossRef]
  38. Healy, K.L.; Scott, J.G.; Thomas, H.J. The Protective Role of Supportive Relationships in Mitigating Bullying Victimization and Psychological Distress in Adolescents. J. Child Fam. Stud. 2024, 35, 1069–1080. [Google Scholar] [CrossRef]
  39. Stevens, F.; Nurse, J.; Arief, B. Cyber Stalking, Cyber Harassment, and Adult Mental Health: A Systematic Review. Cyberpschology Behav. Soc. Netw. 2021, 24, 367–376. [Google Scholar] [CrossRef] [PubMed]
  40. Hutson, E. Cyberbullying in Adolescence: A Concept Analysis. ANS Adv. Nurs. Sci. 2016, 39, 60–70. [Google Scholar] [CrossRef] [PubMed]
  41. Dilmac, B. Psychological Needs as a Predictor of Cyber Bullying: A Preliminary Report on College Students. Educ. Sci. Theory Pract. 2009, 9, 1307–1325. [Google Scholar]
  42. Sampasa-Kanyinga, H.; Roumeliotis, P.; Xu, H. Associations between cyberbullying and school bullying victimization and suicidal ideation, plans and attempts among Canadian schoolchildren. PLoS ONE 2014, 9, e102145. [Google Scholar] [CrossRef]
  43. David-Ferdon, C.; Clayton, H.B.; Dahlberg, L.L.; Simon, T.R.; Holland, K.M.; Brener, N.; Matjasko, J.L.; D’inverno, A.S.; Robin, L.; Gervin, D. Vital Signs: Prevalence of Multiple Forms of Violence and Increased Health Risk Behaviors and Conditions Among Youths—United States, 2019. MMWR-Morb. Mortal. Wkly. Rep. 2021, 70, 167–173. [Google Scholar] [CrossRef]
  44. NolenHoeksema, S.; Olweus, D. School Bullying: Development and Some Important Challenges. Annu Rev Clin Psychol. 2013, 9, 751–780. [Google Scholar] [CrossRef]
  45. Guo, X.; Gu, Z.; Guo, X.; Wu, L. Characteristics of Cyber Bullying/Victimization in Adolescents. China J. Health Psychol. 2015, 23, 1523–1527. [Google Scholar] [CrossRef]
  46. Peter, I.; Petermann, F. Cyberbullying: A concept analysis of defining attributes and additional influencing factors. Comput. Hum. Behav. 2018, 86, 350–366. [Google Scholar] [CrossRef]
  47. Chang, F.; Lee, C.; Chiu, C.; Hsi, W.; Huang, T.; Pan, Y. Relationships among cyberbullying, school bullying, and mental health in Taiwanese adolescents. J. Sch. Health 2013, 83, 454–462. [Google Scholar] [CrossRef] [PubMed]
  48. Bettencourt, A.F.; Farrell, A.D. Individual and Contextual Factors Associated with Patterns of Aggression and Peer Victimization During Middle School. J. Youth Adolesc. 2013, 42, 285–302. [Google Scholar] [CrossRef]
  49. Chen, J.; Zhao, Y.; Li, K.; Tang, C.; Yan, M.; Zhang, Q.; Chen, D.; Jiang, J.; Liu, Q. Status quo of cyberbullying among children and adolescents and its correlation with anxiety and depressive symptoms. Mod. Prev. Med. 2022, 49, 808–813. [Google Scholar]
  50. Goebert, D.; Else, I.; Matsu, C.; Chung-Do, J.; Chang, J.Y. The Impact of Cyberbullying on Substance Use and Mental Health in a Multiethnic Sample. Matern. Child Health J. 2011, 15, 1282–1286. [Google Scholar] [CrossRef]
  51. Bradshaw, C.P.; Waasdorp, T.E.; Johnson, S.L. Overlapping Verbal, Relational, Physical, and Electronic Forms of Bullying in Adolescence: Influence of School Context. J. Clin. Child Adolesc. Psychol. 2015, 44, 494–508. [Google Scholar] [CrossRef] [PubMed]
  52. Wang, J.; Nansel, T.R.; Iannotti, R.J. Cyber Bullying and Traditional Bullying: Differential Association with Depression. J. Adolesc. Health 2011, 48, 415–417. [Google Scholar] [CrossRef]
  53. Selkie, E.M.; Fales, J.L.; Moreno, M.A. Cyberbullying Prevalence Among US Middle and High School-Aged Adolescents: A Systematic Review and Quality Assessment. J. Adolesc. Health 2016, 58, 125–133. [Google Scholar] [CrossRef]
  54. Berne, S.; Frisén, A.; Schultze-Krumbholz, A.; Scheithauer, H.; Naruskov, K.; Luik, P.; Katzer, C.; Erentaite, R.; Zukauskiene, R. Cyberbullying assessment instruments: A systematic review. Aggress. Violent Behav. 2013, 18, 320–334. [Google Scholar] [CrossRef]
  55. Nation, M.; Vieno, A.; Perkins, D.D.; Santinello, M. Bullying in school and adolescent sense of empowerment: An analysis of relationships with parents, friends, and teachers. J. Community Appl. Soc. Psychol. 2008, 18, 211–232. [Google Scholar] [CrossRef]
  56. Ak, Ş.; Özdemir, Y.; Kuzucu, Y. Cybervictimization and cyberbullying: The mediating role of anger, don’t anger me! Comput. Hum. Behav. 2015, 49, 437–443. [Google Scholar] [CrossRef]
  57. Dou, G.; Xiang, Y.; Sun, X.; Chen, L. Link Between Cyberbullying Victimization and Perpetration Among Undergraduates: Mediating Effects of Trait Anger and Moral Disengagement. Psychol. Res. Behav. Manag. 2020, 13, 1269–1276. [Google Scholar] [CrossRef]
  58. Archer, J. The reality and evolutionary significance of human psychological sex differences. Biol. Rev. 2019, 94, 1381–1415. [Google Scholar] [CrossRef] [PubMed]
  59. Chung, J.Y.; Lee, S. Are bully-victims homogeneous? Latent class analysis on school bullying. Child. Youth Serv. Rev. 2020, 112, 104922. [Google Scholar] [CrossRef]
  60. Kowalski, R.M.; Limber, S.P. Electronic Bullying Among Middle School Students. J. Adolesc. Health 2007, 41, S22–S30. [Google Scholar] [CrossRef] [PubMed]
  61. Janis, W.J.D.; Mitchell, K.J.; Finkelhor, D. Does online harassment constitute bullying? An exploration of online harassment by known peers and online-only contacts. J. Adolesc. Health 2007, 41, S51–S58. [Google Scholar]
  62. Mateu, A.; Pascual-Sanchez, A.; Martinez-Herves, M.; Hickey, N.; Nicholls, D.; Kramer, T. Cyberbullying and post-traumatic stress symptoms in UK adolescents. Arch. Dis. Child. 2020, 105, 951. [Google Scholar] [CrossRef] [PubMed]
  63. Lapidot-Lefler, N.; Barak, A. The benign online disinhibition effect: Could situational factors induce self-disclosure and prosocial behaviors? Cyberpsychology J. Psychosoc. Res. Cyberspace 2015, 9, 3. [Google Scholar] [CrossRef]
  64. Larrañaga, E.; Yubero, S.; Ovejero, A.; Navarro, R. Loneliness, parent-child communication and cyberbullying victimization among Spanish youths. Comput. Hum. Behav. 2016, 65, 1–8. [Google Scholar] [CrossRef]
  65. Borca, G.; Bina, M.; Keller, P.S.; Gilbert, L.R.; Begotti, T. Internet use and developmental tasks: Adolescents’ point of view. Comput. Hum. Behav. 2015, 52, 49–58. [Google Scholar] [CrossRef]
  66. Paat, Y.; Markham, C. Digital crime, trauma, and abuse: Internet safety and cyber risks for adolescents and emerging adults in the 21st century. Soc. Work Ment. Health 2021, 19, 18–40. [Google Scholar] [CrossRef]
  67. Ybarra, M.L.; Mitchell, K.J.; Korchmaros, J.D. National trends in exposure to and experiences of violence on the Internet among children. Pediatrics 2011, 128, e1376–e1386. [Google Scholar] [CrossRef] [PubMed]
  68. Vieno, A.; Gini, G.; Santinello, M. Different forms of bullying and their association to smoking and drinking behavior in Italian adolescents. J. Sch. Health 2011, 81, 393–399. [Google Scholar] [CrossRef] [PubMed]
  69. Prescott, A.T.; Sargent, J.D.; Hull, J.G. Metaanalysis of the relationship between violent video game play and physical aggression over time. Proc. Natl. Acad. Sci. USA 2018, 115, 9882–9888. [Google Scholar] [CrossRef] [PubMed]
  70. Teng, Z.; Nie, Q.; Guo, C.; Zhang, Q.; Liu, Y.; Bushman, B.J. A longitudinal study of link between exposure to violent video games and aggression in Chinese adolescents: The mediating role of moral disengagement. Dev. Psychol. 2019, 55, 184–195. [Google Scholar] [CrossRef] [PubMed]
  71. Kowalski, R.M.; Limber, S.P.; Agatston, P.W. Cyberbullying: Bullying in the Digital Age, 2nd ed.; Wiley Blackwell: Hoboken, NJ, USA, 2012; p. 282. ISBN 978-1-4443-3481-4. [Google Scholar]
  72. Unnever, J.D. Bullies, Aggressive Victims, and Victims: Are They Distinct Groups? Aggress. Behav. 2005, 31, 153–171. [Google Scholar] [CrossRef]
  73. Schwartz, D.; Proctor, L.J.; Chien, D.H. The Aggressive Victim of Bullying: Emotional and Behavioral Dysregulation as a Pathway to Victimization by Peers; The Guilford Press: New York, NY, USA, 2001; pp. 147–174. ISBN 978-1-57230-627-1. [Google Scholar]
  74. Sourander, A.; Brunstein Klomek, A.; Ikonen, M.; Lindroos, J.; Luntamo, T.; Koskelainen, M.; Ristkari, T.; Helenius, H. Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study. Arch. Gen. Psychiatry 2010, 67, 720–728. [Google Scholar] [CrossRef]
  75. Hood, M.; Duffy, A.L. Understanding the relationship between cyber-victimisation and cyber-bullying on Social Network Sites: The role of moderating factors. Personal. Individ. Differ. 2018, 133, 103–108. [Google Scholar] [CrossRef]
  76. Buelga, S.; Martínez Ferrer, B.; Cava, M.J. Differences in family climate and family communication among cyberbullies, cybervictims, and cyber bully–victims in adolescents. Comput. Hum. Behav. 2017, 76, 164–173. [Google Scholar] [CrossRef]
  77. Rumjaun, A.; Narod, F. Social Learning Theory—Albert Bandura; Akpan, B., Kennedy, T.J., Eds.; Springer International Publishing: Berlin/Heidelberg, Germany, 2020; pp. 85–99. ISBN 978-3-030-43620-9. [Google Scholar]
  78. Vale, A.; Pereira, F.; Gonçalves, M.; Matos, M. Cyber-aggression in adolescence and internet parenting styles: A study with victims, perpetrators and victim-perpetrators. Child. Youth Serv. Rev. 2018, 93, 88–99. [Google Scholar] [CrossRef]
  79. Chen, S. The Research on the Current Situation of Teenager Cyberbullying. J. Guizhou Norm. Univ. (Soc. Sci.) 2020, 3, 36–44. [Google Scholar] [CrossRef]
  80. Graham, S.; Bellmore, A.D.; Mize, J. Peer victimization, aggression, and their co-occurrence in middle school: Pathways to adjustment problems. J. Abnorm. Child Psychol. 2006, 34, 363–378. [Google Scholar] [CrossRef] [PubMed]
Children 11 01193 g001
Figure 1. Nomogram Predictive Model for Adolescent Cyberbully-Victims’ Risk.
Figure 1. Nomogram Predictive Model for Adolescent Cyberbully-Victims’ Risk.
Children 11 01193 g001
Children 11 01193 g002
Figure 2. Calibration Curve for Adolescent Cyberbully-Victims’ Risk.
Figure 2. Calibration Curve for Adolescent Cyberbully-Victims’ Risk.
Children 11 01193 g002
Children 11 01193 g003
Figure 3. ROC Curve of the Nomogram Predictive Model for Adolescent Cyberbully-Victims’ Risk.
Figure 3. ROC Curve of the Nomogram Predictive Model for Adolescent Cyberbully-Victims’ Risk.
Children 11 01193 g003
Table 1. Prevalence of cyberbullying–victimization Among Adolescents in Different Groups.
Table 1. Prevalence of cyberbullying–victimization Among Adolescents in Different Groups.
CategorySubgroupNumberCyberbullying–Victimization Numberχ2p
GenderMale6550263 (4.0)59.727<0.001
Female6220107 (1.7)
School levelMiddle school8001213 (2.7)4.2140.040
High school4769157 (3.3)
smokingYes36558 (15.9)225.457<0.001
No12,405312 (2.5)
drinkingYes3110203 (6.5)192.542<0.001
No9660167 (1.7)
Class monitor roleYes9819285 (2.9)0.0040.950
No295185 (2.9)
Academic performanceExcellent163739 (2.4)13.3920.004
Above Average4097118 (2.9)
Average4174103 (2.5)
Below Average2862110 (3.8)
Popularity among peers *Highly Popular192661 (3.2)22.306<0.001
Moderately Popular9016228 (2.5)
Less Popular138560 (4.3)
Not Popular26415 (5.7)
Favorite Type of Video Games Board/Leisure Games93921 (2.2)69.790<0.001
Single/Multiplayer Battle Games6419258 (4.0)
Strategy/Simulation Games128222 (1.7)
Role-playing Story Games174847 (2.7)
Never Play Video Games238222 (0.9)
Internet Usage Time (h)0~<0.57218138 (1.9)131.156<0.001 a
0.5~<1267271 (2.7)
1~<1.5114539 (3.4)
1.5~<265636 (5.5)
≥2107986 (8.0)
Frequency of Social Media UseAlways1855159 (8.6)245.766<0.001 a
Often4380143 (3.3)
Occasionally553560 (1.1)
Never10007 (0.7)
Only Child 1.2370.266
Yes7639211 (2.8)
No5131159 (3.1)
Family Relationship * 113.804<0.001
Harmonious8836169 (1.9)
Average185377 (4.2)
Occasional Conflicts168296 (5.7)
Significant Conflicts35026 (7.4)
Parenting Style * 64.061<0.001
Punishment-Based34730 (8.6)
Punishment and Persuasion Combined3822141 (3.7)
Liberal206362 (3.0)
Persuasion-Based6487136 (2.1)
Father’s Smoking Status 4.4520.035
Yes7995251 (3.1)
No4775119 (2.5)
Father’s Drinking Status 5.3920.020
Yes7997253 (3.2)
No4773116 (2.4)
Mother’s Smoking Status 14.247<0.001
Yes14912 (8.1)
No12,621358 (2.8)
Mother’s Drinking Status 20.526<0.001
Yes149771 (4.7)
No11,273299 (2.7)
Parents Working Away from Home 20.724<0.001
Yes133764 (4.9)
No11,433306 (2.7)
a linear trend chi-square test is used; the number in () is cyberbullying–victimization-reported rate (%); * indicates missing data.
Table 2. Multivariate Logistic Regression Analysis of Cyberbully-Victims Among Adolescents (n = 12,770).
Table 2. Multivariate Logistic Regression Analysis of Cyberbully-Victims Among Adolescents (n = 12,770).
VariableOptionsβStandard ErrorWald ValuepOR (95%CI)
Genderfemale a 1.00
male0.69 0.1329.230 <0.0012.00 (1.55~2.54)
Parents Working Away from HomeNo a 1.00
yes0.52 0.15 11.890 0.0011.68 (1.25~2.25)
Family RelationshipHarmonious a 1.00
Average0.44 0.15 8.6980.0031.55 (1.16~2.08)
Occasional Conflicts0.73 0.15 25.021 <0.0012.07 (1.56~2.75)
Significant Conflicts0.62 0.25 6.4240.0111.86 (1.15~3.02)
Parenting StyleLiberal a 1.00
Persuasion-Based−0.110.16 0.4590.498 0.91 (0.65~1.23)
Punishment-Based0.56 0.26 4.6230.0321.75 (1.05~2.91)
Punishment and Persuasion Combined0.24 0.16 2.2020.1381.27 (0.93~1.76)
Drinkingno a 1.00
yes0.71 0.12 35.353 <0.0012.04 (1.61~2.58)
Smokingno a 1.00
yes0.840.18 23.032<0.0012.32 (1.64~3.27)
Frequency of Social Media Usenever a 1.00
Always2.05 0.40 26.588<0.0017.77 (3.56~16.93)
Often1.35 0.40 11.5530.0013.84 (1.77~8.36)
Occasionally0.41 0.41 1.0140.3141.51 (0.68~3.33)
Favorite Type of Video GamesNever Play Video Games a 1.00
Board/Leisure Games0.40 0.33 1.5390.215 1.50 (0.79~2.84)
Single/Multiplayer Battle Games0.58 0.24 5.608 0.018 1.78 (1.10~2.87)
Strategy/Simulation Games0.07 0.32 0.046 0.830 1.07 (0.57~2.00)
Role-playing Story Games0.460.27 2.7850.095 1.58 (0.92~2.71)
Note: Group a is the reference group.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ma, J.; Su, L.; Li, M.; Sheng, J.; Liu, F.; Zhang, X.; Yang, Y.; Xiao, Y. Analysis of Prevalence and Related Factors of Cyberbullying–Victimization among Adolescents. Children 2024, 11, 1193. https://doi.org/10.3390/children11101193

AMA Style

Ma J, Su L, Li M, Sheng J, Liu F, Zhang X, Yang Y, Xiao Y. Analysis of Prevalence and Related Factors of Cyberbullying–Victimization among Adolescents. Children. 2024; 11(10):1193. https://doi.org/10.3390/children11101193

Chicago/Turabian Style

Ma, Jun, Liyan Su, Minhui Li, Jiating Sheng, Fangdu Liu, Xujun Zhang, Yaming Yang, and Yue Xiao. 2024. "Analysis of Prevalence and Related Factors of Cyberbullying–Victimization among Adolescents" Children 11, no. 10: 1193. https://doi.org/10.3390/children11101193

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop