Blurring the Lines: Factors Influencing Victim–perpetrator Overlap in Korean Adolescent Cyberbullying Behaviors

Blurring the Lines: Factors Influencing Victim–perpetrator Overlap in Korean Adolescent Cyberbullying Behaviors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blurring the Lines: Factors Influencing Victim–perpetrator Overlap in Korean Adolescent Cyberbullying Behaviors Seoung Won Choi, Youngsub Lee, Julak Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7707750/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cyberbullying has proliferated globally with rapid digitalization and has emerged as a serious social problem in South Korea. Existing studies have approached cyberbullying through a binary structure of perpetrators and victims, but this approach inadequately explains the complex interactions in digital spaces. Therefore, this study focuses on the role overlap phenomenon in cyberbullying to analyze these multifaceted characteristics. This study analyzed survey data from 9,479 Korean adolescents collected in 2024, applying routine activity theory alongside descriptive statistics, correlation analysis, and multinomial logistic regression. The results showed that 16.4% of participants were classified as the victim–perpetrator type, experiencing both victimization and perpetration in cyberbullying incidents. Among all predictors, having friends involved in cyberbullying emerged as the strongest factor. Parental and school guardianship exhibited distinct predictive patterns for the victim–perpetrator type relative to other involvement categories (non-involved, victim-only, and perpetrator-only). These findings provide empirical support for the complex characteristics of cyberbullying and demonstrate the necessity of multilayered intervention strategies beyond simple binary approaches. Based on these findings, this study recommends family-centered prevention programs with complementary school-based interventions, given that parental guardianship serves as the primary buffer against cyberbullying role overlap. Cyberbullying Victim–perpetrator Overlap Routine Activity Theory Cyberbullying Friends Guardianship Introduction With the expansion of digital technologies and the growing involvement of youth in online spaces, cyberbullying has become a significant public concern (Bochaver and Khlomov 2014 ; Foody et al. 2017 ). It entails the use of digital means—such as social networks and messaging platforms—to cause harm through intimidation or harassment (Grigg 2010 ). Unlike traditional bullying, it is not limited by time or place and may be more damaging due to anonymity and the speed at which content spreads (Bochaver and Khlomov 2014 ; Sticca and Perren 2013 ). During the COVID-19 pandemic, adolescents became more dependent on digital communication, which intensified the reach and effects of cyberbullying and drew greater academic attention (Alfarizy et al. 2024 ; Kuhn et al. 2021 ). Globally, approximately 15% of adolescents report having experienced cyberbullying, although rates vary by country (World Health Organization 2024 ). South Korea presents a particularly concerning case. With 96% of young people owning smartphones—far higher than the global average of 71%—37% of Korean adolescents report being cyberbullied in the past three years (Statista 2025a; Statista 2025b; Korea Communications Commission and National Information Society Agency 2025 ). This raises concerns about negative effects on adolescents’ mental health, academic achievement, and social development (Bottino et al. 2015 ; Hase et al. 2015 ), highlighting the urgent need for fundamental research into cyberbullying characteristics to develop effective prevention and intervention strategies. This research need has led to growing scholarly attention on cases where the same adolescent is both a victim and a perpetrator—a phenomenon known as victim–perpetrator overlap (Ranjith et al. 2023 ; Tintori et al. 2025 ). This group shows distinct characteristics: poorer peer relationships, more aggressive reactions to violence, and higher emotional instability and risky online behavior (Tintori et al. 2025 ). While the issue of role overlap in cyberbullying has gained more attention, research in this area remains limited and often lacks a clear theoretical foundation (Xu 2024 ). To fill this gap, the study applies routine activity theory (RAT; Cohen and Felson 1979 ) to investigate what drives victim–perpetrator overlap among Korean adolescents. As a framework centered on exposure and behavioral routines, RAT is well suited to analyzing cyberbullying in online spaces (Paek et al. 2022 ). In doing so, the study seeks to refine theoretical understanding and contribute to more integrated approaches to intervention. Theoretical Background Cyberbullying and Routine Activity Theory Cyberbullying is defined as the intentional and repeated use of information and communication technologies to harm others (Smith et al. 2008 ; Tarigan 2019 ). While sharing traditional bullying’s characteristics of intentionality, repetitiveness, and power imbalance, cyberbullying also features anonymity, unlimited reach across time and space, and mass distribution (Boniel-Nissim, 2019 ; Smith, 2012 ). Anonymity, in particular, reduces perpetrators’ inhibitions and amplifies victims’ anxiety about not being able to identify the perpetrator (Dooley et al. 2012 ; Kowalski et al. 2014 ). Cyberbullying takes many forms, including verbal abuse, defamation, stalking, sexual violence, information leakage, exclusion, extortion, and duress (Paek et al., 2022 ), and has significant negative effects on youths’ emotional stability, psychological health, and academic performance (Bottino et al. 2015 ; Hase et al. 2015 ). RAT offers a structural framework for analyzing cyberbullying by identifying the conditions under which deviant behavior occurs. According to RAT, crime occurs when three key elements converge: a motivated offender, a suitable target, and the absence of capable guardians (Cohen and Felson 1979 ). RAT has been applied to various crimes, and for cybercrime, its core elements are adapted as “exposure to motivated offenders,” “suitable online targets,” and “absence of capable guardians” (Marcum et al., 2010 ; Morillo Puente and Ríos Hernández 2022 ). These components significantly predict cyberbullying victimization, both individually and in combination (Aizenkot 2022 ). In the context of cyberbullying, these three factors apply as follows. First, “exposure to a motivated offender” refers to the degree to which an individual is exposed to a potential cyber perpetrator online and is associated with having friends who engaged in cyberbullying, exposure to harmful content, and witnessing cyberbullying. Associating with delinquent peers and learning deviant behaviors increases adolescents’ likelihood of perpetrating cyberbullying (Skinner and Fream 1997 ; Becker and Clement 2006 ). Moreover, exposure to harmful content and witnessing cyberbullying are environmental factors that encourage youth to imitate risky and criminal behaviors (Motyka and Al-Imam 2021 ; Paek et al. 2022 ; Tercova and Smahel 2025 ). Second, “suitable online target” refers to the degree to which an individual becomes a potential target for cyber perpetrators and is measured through Internet usage and online interaction (Leukfeldt and Yar 2016 ; Paek et al. 2022 ; Kabiri et al. 2022 ). South Korea has high digital accessibility, with 99.97% household Internet access and 93.8% mobile Internet usage (National Information Society Agency 2025 ). The "digital native” generation actively shares their daily lives through social media (Jang et al. 2022 ), increasing their exposure and suitability as cyberbullying targets (Riya and Caeiro 2024 ). Thus, assessments of “suitable online target” should include both quantitative aspects (e.g., Internet usage duration) and qualitative aspects (e.g., nature of interaction). Third, “absence of a capable guardian” refers to the absence of protective mechanisms to curb cyberbullying and is related to parental guardianship and school guardianship. Korean Educational Development Institute ( 2021 ) recognized both as vital to cyberbullying prevention, and Oh and Kwak ( 2013 ) highlighted the necessity for comprehensive protection systems through joint efforts. Thus, the concept of “absence of a capable guardian” in cyberbullying needs to be understood from a perspective that encompasses the roles of parents and schools. However, parental and school guardianship may demonstrate different protective effects depending on cyberbullying involvement types. In East Asian cultures, children tend to perceive stringent parental oversight as a sign of concern for their social achievements (Chung and Choi 2008 ), and Korea’s Confucian heritage underscores the parental role as a safeguard (Park and Cheah 2005 ). Paek et al. ( 2022 ) found that parental guardianship significantly reduced overall and non-violent victimization among Korean adolescents, whereas school guardianship had no significant effect on cyberbullying victimization. However, because most prior studies have examined guardianship in either victim or perpetrator roles separately, how these protective factors function for victim–perpetrator adolescents remains unclear. Cyberbullying victim–perpetrator overlap While criminological research has traditionally examined victims and perpetrators as distinct categories (Jensen and Brownfield 1986 ), recent studies on cyberbullying underscore the significance of victim–perpetrator overlap, highlighting the complex interaction between aggression and vulnerability in digital settings (Chan and Wong 2020 ; Foody et al. 2017 ; Xu 2024 ). Adolescents’ need for peer approval, coupled with underdeveloped impulse control, makes them susceptible to not only perpetrating but also experiencing cyberbullying (Walrave and Heirman 2011 ). Vandebosch and Van Cleemput ( 2009 ) explain that from a social learning perspective, victimization experiences can trigger subsequent perpetrator behavior. A substantial number of cyberbullying perpetrators have previously been victims, and their aggressive behavior increases with repeated victimization experiences (Song 2021 ; Walrave and Heirman 2011 ). Online platforms enable individuals to conceal their identities and shift power dynamics, making it easier for offline victims to transition into online perpetrators (Estévez et al. 2020 ). Consequently, rates of victim–perpetrator overlap are often higher in cyberbullying than traditional bullying (Mishna et al. 2012 ). Cyberbullying victim–perpetrators have diminished guilt and empathy due to their inability to observe their behavior’s impact on others, and they tend to exhibit the most negative outcomes in psychological health, physical health, and academic performance (Kowalski and Limber 2013 ). In particular, the nature of digital spaces facilitates retaliation and continued harassment, which can cause widespread harm (Mishna et al. 2009 ), and the accumulation of victimization experiences increases the likelihood of perpetration, creating a vicious cycle (Park and Shim 2015 ). However, existing cyberbullying research has primarily focused on either the victim or perpetrator aspect, leaving cyberbullying victim–perpetrators insufficiently addressed despite their experience of serious negative consequences. Therefore, this study aims to apply RAT to identify the variables that best predict adolescent cyberbullying victim–perpetrator types and to analyze the differential predictive patterns of guardianship variables. Against this background, this study established the following research questions: RQ1: What is the distribution of adolescents’ cyberbullying involvement types (non-involved, victim-only, perpetrator-only, victim–perpetrator)? RQ2: Among the variables measuring key components of routine activity theory, which best predicts the likelihood of adolescents being classified as the victim–perpetrator type? RQ3: Do parental guardianship and school guardianship show different patterns in predicting the likelihood of belonging to non-involved, victim-only, and perpetrator-only types when compared against the victim–perpetrator type as the reference category? Methodology Data collection and research setting This study analyzed data obtained from the Cyberviolence Survey 2024 (Korea Communications Commission and National Information Society Agency 2025 ). The survey aimed to clarify the experiences and perceptions of cyberbullying in online spaces among adolescents, thereby providing foundational data for developing and establishing cyberbullying prevention and response policies. A stratified two-stage cluster sampling method was used to select schools in a region, select classes from the selected schools, and survey all students in the selected classes. The survey was completed by 9,479 adolescents enrolled in the fourth grade of elementary school to the third grade of high school in South Korea. The baseline period of the survey was September 1, 2023, to August 31, 2024, and the survey was conducted from September 2, 2024, to November 24, 2024, using in-person group interviews and online surveys. The survey consisted of the following items: experiences of victimization, perpetration, and witnessing cyberbullying in online spaces; cyberbullying awareness; cyberbullying prevention and response; digital hate speech; digital sexual offense prevention; social environment factors; and online activities. Given the purpose of the study, the survey items related to cyberbullying experiences, demographic factors, proximity to offenders, online engagement, and guardianship were analyzed. Measurement This study measured the variables as shown in Table 1 . First, cyberbullying victimization measured the experience of victimization in the online space in the past year. Eight types of cyberbullying victimization were assessed: verbal abuse, defamation, stalking, sexual violence, information leakage, exclusion, extortion, and duress. Responses to each item ranged from “never,” “1–2 times per year,” “1–2 times per 6 months,” “1–2 times per month,” and “almost every day,” but the majority of respondents chose “never,” which we coded as a dichotomous variable (0 = no, 1 = yes). Cyberbullying perpetration was also measured across the same eight types of cyberbullying perpetration behaviors and dichotomized in the same way as the victimization variable. Then, for this study, the cyberbullying involvement type was constructed from the cyberbullying victimization and cyberbullying perpetration variables. Specifically, based on the victimization and perpetration experiences of the eight types of cyberbullying, participants were categorized into four categories: 0) non-involved, 1) victim-only, 2) perpetrator-only, and 3) victim–perpetrator. The study also included gender and school level to account for demographic characteristics. Gender was dichotomously coded as 0: female, 1: male, and school level was coded as 1: elementary, 2: middle, and 3: high. Furthermore, this study used the variables of having friends who engaged in cyberbullying, harmful content exposure, and witnessing cyberbullying to measure proximity to offenders. Having friends who engaged in cyberbullying measured the presence of friends who bully others online within participants’ close social networks. Specifically, the original scale was “none,” “1–3,” “4–6,” and “7 or more,” but was dichotomously coded as 0: no and 1: yes. This coding shift was made for ease of analysis and statistical interpretation due to the high proportion of “none” responses. Harmful content exposure measured the experience of exposure to harmful content in online spaces. Specifically, this assessed the frequency of respondents’ exposure to five types of content: violent and gory content, sexually explicit content, celebrity bashing, content about illegal behavior such as Internet gambling or fraud, and false information. The response scale consisted of “never,” “very rarely,” “rarely,” “often,” and “very often,” and the scores for each item were summed to create a final score. Internal consistency was assessed using Cronbach’s α coefficient (α = .803). Cyberbullying witness measured the experience of witnessing cyberbullying in online spaces in the past year. Specifically, respondents were asked to indicate whether they had witnessed a cyberbullying situation in an online space on a scale of “witnessed perpetration,” "witnessed victimization," "witnessed both," "never witnessed." For the purposes of the study, this was dichotomously coded as 0: no and 1: yes. In addition, this study used Internet usage and online interaction to measure online engagement. Internet usage measured the average number of hours per day spent on the Internet in the past year, and the original scale was “less than 1 hour”, “more than 1 hour to less than 2 hours”, “more than 2 hours to less than 3 hours”, “more than 3 hours to less than 5 hours”, and “more than 5 hours.” However, due to the uneven distribution of respondents and the ease of interpretation of the results, we coded this variable as 0: low (< 3 hours/day) and 1: high (≥ 3 hours/day). Online interaction consisted of three items measuring participants' perceptions of online social support from friends. Items assessed whether friends (1) express approval on posts, (2) respond immediately to messages, and (3) send supportive messages during difficult times in online spaces. The response options were "strongly disagree," "disagree," "agree," and "strongly agree." The online interaction level was calculated by summing the scores of each item, and internal consistency was assessed using Cronbach's α coefficient (α = .775). Regarding the guardianship concept, we used two key mechanisms that protect adolescents in digital environments: parental guardianship and school guardianship variables. Parental guardianship measured parental digital monitoring, specifically whether monitoring or content-blocking apps were installed to check Internet or smartphone usage (0 = no, 1 = yes). School guardianship measured school cyberbullying policy, specifically whether policies containing provisions for monitoring and punishment of cyberbullying existed (0 = no, 1 = yes). Table 1 Variable Measurement Variable Category Variable Name Coding Description Cyberbullying Behavior Cyberbullying Victimization 0: no, 1: yes Experience of cyberbullying victimization Cyberbullying Perpetration 0: no, 1: yes Experience of cyberbullying perpetration Cyberbullying Involvement Type 0: non-involved, 1: victim-only, 2: perpetrator-only, 3: victim–perpetrator Categorized based on victimization and perpetration experiences Demographics Gender 0: female, 1: male Participant's gender School Level 1: elementary, 2: middle, 3: high Educational level of participant Proximity to Offenders Cyberbullying Friends 0: no, 1: yes Presence of friends who engage in cyberbullying Harmful Content Exposure Composite score (α = .803) Frequency of exposure to harmful online content Cyberbullying Witness 0: no, 1: yes Experience of witnessing cyberbullying in online spaces Online Engagement Internet Usage 0: low, 1: high Level of daily Internet usage Online Interaction Composite score (α = .775) Level of online interaction with friends Guardianship Parental Guardianship 0: no, 1: yes Presence of parental digital monitoring School Guardianship 0: no, 1: yes Presence of school cyberbullying policy Analysis methods This study employed multinomial logistic regression to identify factors associated with adolescents' cyberbullying involvement types. This statistical technique is appropriate when the dependent variable consists of three or more nominal categories. The analysis classified cyberbullying involvement into four types: non-involved, victim-only, perpetrator-only, and victim–perpetrator, with victim–perpetrator serving as the reference category. Odds ratios were calculated to determine the likelihood of belonging to each involvement type relative to the reference group. Independent variables were selected based on RAT’s three components: (1) proximity to offenders (cyberbullying friends, harmful content exposure, cyberbullying witness), (2) online engagement (Internet usage, online interaction), and (3) guardianship (parental and school guardianship). Demographic variables (gender and school level) were also included as covariates. Multicollinearity among predictors was examined using tolerance and variance inflation factor (VIF) values prior to the main analysis. All statistical analyses were conducted using SPSS 26.0. Results Descriptive analysis A total of 9,479 students were included in the final analysis. As shown in Table 2 , the most common type of cyberbullying involvement was non-involved (57.9%), followed by victim-only (20.0%), victim–perpetrator (16.4%), and perpetrator-only (5.8%). Overall, 36.3% of students were victims of cyberbullying, while 22.2% were perpetrators. In terms of demographics, gender was relatively evenly distributed, with 51.3% male and 48.7% female. Participants were also evenly distributed across school levels: 35.3% elementary, 32.7% middle school, and 32.0% high school. In terms of proximity to offenders, 7.0% of students reported having friends who engage in cyberbullying, and 7.8% reported being a witness to cyberbullying. Harmful content exposure averaged 4.99 (SD = 4.23) on a scale of 0–20. In terms of online engagement, more than half (51.0%) of the respondents reported an average of 3 or more hours of Internet usage per day. In addition, the level of online interaction averaged 8.47 (SD = 2.11) on a scale of 3–12, indicating that most students had moderate to high levels of online interaction. In terms of guardianship, 26.0% of students reported having parental guardianship, and 47.1% reported having school guardianship. Table 2 Descriptive Statistics of Study Variables (N = 9,479) Variables and Categories n % Range Mean (SD) Cyberbullying Involvement Type 0–3 0.81 (1.12) Non-involved 5,487 57.9 Victim-only 1,892 20.0 Perpetrator-only 549 5.8 Victim–perpetrator 1,551 16.4 Cyberbullying Victimization 0–1 0.36 (0.48) No 6,036 63.7 Yes 3,443 36.3 Cyberbullying Perpetration 0–1 0.22 (0.42) No 7,379 77.8 Yes 2,100 22.2 Gender 0–1 0.51 (0.50) Female 4,613 48.7 Male 4,866 51.3 School Level 1–3 1.97 (0.82) Elementary 3,343 35.3 Middle 3,098 32.7 High 3,038 32.0 Cyberbullying Friends 0–1 0.07 (0.25) No 8,817 93.0 Yes 662 7.0 Harmful Content Exposure 0–20 4.99 (4.23) Cyberbullying Witness 0–1 0.08 (0.27) No 8,735 92.2 Yes 744 7.8 Internet Usage 0–1 0.51 (0.50) Low 4,643 49.0 High 4,836 51.0 Online Interaction 3–12 8.47 (2.11) Parental Guardianship 0–1 0.26 (0.44) No 7,017 74.0 Yes 2,462 26.0 School Guardianship 0–1 0.47 (0.50) No 5,013 52.9 Yes 4,466 47.1 Correlation analysis The correlation analysis results are presented in Table 3 , revealing complex relationships between cyberbullying experiences and various individual, social, and environmental factors. First, a moderate positive correlation existed between cyberbullying victimization and perpetration (r = 0.416, p < 0.001). Both cyberbullying victimization and perpetration showed significant positive correlations with having friends who engage in cyberbullying (victimization: r = 0.229, p < 0.001; perpetration: r = 0.285, p < 0.001) and harmful content exposure (victimization: r = 0.227, p < 0.001; perpetration: r = 0.188, p < 0.001). Witnessing cyberbullying showed a significant positive correlation with victimization (r = 0.193, p < 0.001) and perpetration (r = 0.146, p < 0.001). Notably, harmful content exposure showed a significant positive correlation with school level (r = 0.275, p < 0.001), while witnessing cyberbullying showed the strongest correlation specifically with having friends who engage in cyberbullying (r = 0.248, p < 0.001). Meanwhile, Internet usage and online interaction both showed positive correlations with cyberbullying victimization (r = 0.097, p < 0.001; r = 0.075, p < 0.001) and perpetration (r = 0.096, p < 0.001; r = 0.042, p < 0.001). In particular, both Internet usage and online interaction showed significant correlations with school level (r = 0.234, p < 0.001; r = 0.272, p < 0.001). Parental guardianship was negatively correlated with cyberbullying perpetration (r = -0.030, p < 0.01) but showed no correlation with victimization (r = -0.004, p = 0.688). Conversely, school guardianship was positively correlated with cyberbullying victimization (r = 0.030, p < 0.01) while showing no significant relationship with perpetration (r = -0.004, p = 0.714). Notably, the two guardianship variables showed a strong correlation with each other (r = 0.180, p < 0.001). Regarding demographic variables, gender showed a positive correlation with cyberbullying victimization (r = 0.045, p < 0.001) and perpetration (r = 0.106, p < 0.001). School level showed a weak negative correlation with both victimization (r = -0.039, p < 0.001) and perpetration (r = -0.027, p < 0.01). Table 3 Correlation Coefficients Between Study Variables Variables 1 2 3 4 5 6 7 8 9 10 11 1. Cyberbullying Victimization — 2. Cyberbullying Perpetration 0.416*** — 3. Gender 0.045*** 0.106*** — 4. School Level -0.039*** -0.027** 0.027** — 5. Cyberbullying Friends 0.229*** 0.285*** 0.034*** -0.094*** — 6. Harmful Content Exposure 0.227*** 0.188*** 0.006 0.275*** 0.085*** — 7. Cyberbullying Witness 0.193*** 0.146*** -0.005 -0.123*** 0.248*** 0.089*** — 8. Internet Usage 0.097*** 0.096*** -0.064*** 0.234*** 0.037*** 0.229*** 0.008 — 9. Online Interaction 0.075*** 0.042*** -0.229*** 0.272*** 0.025* 0.200*** 0.029** 0.161*** — 10. Parental Guardianship -0.004 -0.030** -0.002 -0.334*** 0.033** -0.137*** 0.049*** -0.224*** -0.102*** — 11. School Guardianship 0.030** -0.004 0.017 -0.149*** 0.013 -0.025* 0.033** -0.067*** 0.025* 0.180*** — Note. *p < .05. **p < .01. ***p < .001. Multinomial logistic regression results The effects of key RAT components on cyberbullying involvement types were examined. All variables showed tolerance values above 0.746 and VIF values below 1.340, meeting the multicollinearity criteria (tolerance > 0.10, VIF < 10), confirming no multicollinearity issues among predictors. The multinomial logistic regression results are presented in Table 4 . Table 4 Multinomial Logistic Regression Results for Cyberbullying Role Types Variables Non-involved Victim-only Perpetrator-only B(SE) OR 95% CI B(SE) OR 95% CI B(SE) OR 95% CI Intercept -0.638(0.247)** - - -0.513(0.254)* - - -1.415(0.369)*** - - Demographic Factors Gender a 0.644(0.067)*** 1.904 [1.671, 2.169] 0.481(0.074)*** 1.617 [1.398, 1.870] -0.105(0.108) 0.901 [0.729, 1.113] School Level b Elementary School -0.821(0.093)*** 0.440 [0.367, 0.528] -0.440(0.104)*** 0.644 [0.526, 0.790] -0.630(0.149)*** 0.532 [0.398, 0.713] Middle School -0.721(0.081)*** 0.486 [0.415, 0.570] -0.392(0.090)*** 0.676 [0.566, 0.807] -0.302(0.124)* 0.739 [0.580, 0.942] Proximity to Offenders Cyberbullying Friends c 2.554(0.127)*** 12.859 [10.019, 16.504] 1.330(0.112)*** 3.780 [3.036, 4.705] 0.681(0.153)*** 1.975 [1.465, 2.664] Harmful Content Exposure -0.141(0.008)*** 0.868 [0.855, 0.882] -0.037(0.009)*** 0.964 [0.947, 0.980] -0.044(0.012)*** 0.957 [0.934, 0.980] Cyberbullying Witness d 1.173(0.113)*** 3.232 [2.590, 4.034] 0.091(0.106) 1.095 [0.890, 1.348] 0.632(0.181)*** 1.881 [1.320, 2.680] Online Engagement Internet Usage e 0.458(0.067)*** 1.580 [1.385, 1.803] 0.252(0.075)*** 1.287 [1.110, 1.492] 0.183(0.107) 1.201 [0.973, 1.481] Online Interaction -0.069(0.017)*** 0.933 [0.903, 0.964] -0.004(0.019) 0.996 [0.959, 1.033] -0.012(0.027) 0.988 [0.937, 1.041] Guardianship Parental Guardianship f -0.165(0.080)* 0.848 [0.726, 0.991] -0.247(0.088)** 0.781 [0.658, 0.928] -0.129(0.128) 0.879 [0.684, 1.129] School Guardianship g -0.013(0.065) 0.987 [0.870, 1.120] -0.148(0.072)* 0.863 [0.749, 0.993] 0.045(0.103) 1.046 [0.855, 1.279] Note . The reference category is the victim–perpetrator type. B = coefficient; SE = standard error; OR = odds ratio; CI = confidence interval. a Reference category = male. b Reference category = high school. c Reference category = having friends who engage in cyberbullying. d Reference category = having witnessed cyberbullying. e Reference category = high Internet usage. f Reference category = presence of parental guardianship. g Reference category = presence of school guardianship. *p < .05. **p < .01. ***p < .001. Proximity to offenders Compared to students with friends who engage in cyberbullying, students without such friends had significantly higher odds of being classified as non-involved type (OR = 12.859, B = 2.554, p < 0.001), victim-only type (OR = 3.780, B = 1.330, p < 0.001), and perpetrator-only type (OR = 1.975, B = 0.681, p < 0.001) rather than victim–perpetrator type. Higher levels of harmful content exposure significantly decreased the odds of belonging to non-involved type (OR = 0.868, B = -0.141, p < 0.001), victim-only type (OR = 0.964, B = -0.037, p < 0.001), and perpetrator-only type (OR = 0.957, B = -0.044, p < 0.001) compared to victim–perpetrator type. Students who had not witnessed cyberbullying had significantly higher odds of being classified as non-involved type (OR = 3.232, B = 1.173, p < 0.001) and perpetrator-only type (OR = 1.881, B = 0.632, p < 0.001) compared to the victim–perpetrator type relative to those who had witnessed cyberbullying. However, no significant association was found for the victim-only type (B = 0.091, p = 0.390). Online engagement Compared to students with higher Internet usage, students with lower Internet usage had significantly higher odds of being classified as the non-involved type (OR = 1.580, B = 0.458, p < 0.001) and victim-only type (OR = 1.287, B = 0.252, p < 0.01) in comparison with the victim–perpetrator type. However, no significant association was found for the perpetrator-only type (B = 0.183, p = 0.088). Online interaction showed a significant negative association only with the non-involved type. With each unit increase in online interaction, the odds of belonging to the non-involved type (OR = 0.933, B = -0.069, p < 0.001) decreased compared to the victim–perpetrator type. No significant associations were found for the victim-only type (B = -0.004, p = 0.821) and perpetrator-only type (B = -0.012, p = 0.645). Guardianship Compared to students with parental guardianship, those without parental guardianship had significantly lower odds of being classified as the non-involved type (OR = 0.848, B = -0.165, p < 0.05) and the victim-only type (OR = 0.781, B = -0.247, p < 0.01) than the victim–perpetrator type. However, no significant association was found for the perpetrator-only type (B = -0.129, p = 0.313). Compared to students with school guardianship, those without school guardianship had significantly lower odds of being categorized as the victim-only type (OR = 0.863, B = -0.148, p < 0.05) in comparison with the victim–perpetrator type. No notable associations were found for the non-involved (B = -0.013, p = 0.840) and perpetrator-only (B = 0.045, p = 0.663) types. Demographic factors Female students had higher odds of being classified as the non-involved type (OR = 1.904, B = 0.644, p < 0.001) and the victim-only type (OR = 1.617, B = 0.481, p < 0.001) compared to the victim–perpetrator type relative to male students. No distinct association was found for the perpetrator-only type (B = -0.105, p = 0.333). Compared to high school students, elementary school students had significantly lower odds of being classified as the non-involved type (OR = 0.440, B = -0.821, p < 0.001), victim-only type (OR = 0.644, B = -0.440, p < 0.001), and perpetrator-only type (OR = 0.532, B = -0.630, p < 0.001) than victim–perpetrator type. Middle school students had significantly lower odds of being classified as the non-involved type (OR = 0.486, B = -0.721, p < 0.001), victim-only type (OR = 0.676, B = -0.392, p < 0.001), and perpetrator-only type (OR = 0.739, B = -0.302, p < 0.05) compared to victim–perpetrator type relative to high school students. Discussion This study analyzed the overlap between victimization and perpetration in adolescent cyberbullying. The main findings are discussed according to the research questions posed. Cyberbullying role distribution (RQ1) The 9,479 participants were categorized as non-involved (57.9%), victim-only (20.0%), perpetrator-only (5.8%), and victim–perpetrator (16.4%). The victim–perpetrator type comprised approximately 38.9% of those involved in cyberbullying, aligning with Mishna et al.'s ( 2012 ) findings that victim–perpetrator overlap rates are markedly higher in cyberbullying than in traditional bullying. This prevalence indicates that cyberbullying transcends simple unidirectional aggression, representing instead an interactive process where roles shift fluidly within digital environments (Bochaver and Khlomov 2014 ). Digital spaces enable identity concealment and power dynamic reconstruction, thereby facilitating the transformation of offline victims into online perpetrators, supporting Estévez et al.'s ( 2020 ) research. The existence of the victim–perpetrator type—those who do not fit single role categories—exposes limitations in dichotomous approaches to cyberbullying victimization and perpetration. Hence, more accurate and precise role distinction becomes essential when developing prevention and intervention strategies. Predictors of victim–perpetrator classification (RQ2) Having friends who engaged in cyberbullying emerged as the strongest predictor of victim–perpetrator classification. Specifically, students without friends who engage in cyberbullying were 12.9 times more likely to be the non-involved type than the victim–perpetrator type, supporting the social learning theory perspective (Skinner and Fream 1997 ) that online deviant behavior is learned and reinforced through peer networks. Besides, exposure to harmful content and witnessing cyberbullying significantly increased the likelihood of being classified as a victim–perpetrator. This finding aligns with existing research showing that recurrent exposure to violent or sexual material and instances of cyberbullying diminishes psychological barriers to aggressive behavior by fostering the normalization of violence and inducing moral desensitization (Motyka and Al-Imam 2021 ; Tercova and Smahel 2025 ). These findings imply that preventive measures should go beyond individual behavior and adopt an ecological perspective that encompasses peer networks and digital contexts. Meanwhile, compared to students with higher Internet usage, those with lower Internet usage had significantly higher odds of being classified as the non-involved type and victim-only type relative to the victim–perpetrator type, supporting previous studies (Leukfeldt and Yar 2016 ; Park and Shim 2015 ; Walrave and Heirman 2011 ) showing that excessive Internet use among adolescents can lead to a cycle of victimization and retaliation. Meanwhile, higher levels of online interaction are associated with increased odds of adolescents being classified as victim–perpetrator type rather than non-involved type. Adolescents are “digital natives” who have been exposed to digital environments since childhood and are familiar with online interaction via social media (Jang et al. 2022 ). These results suggest that cyberbullying prevention should focus not only on limiting Internet usage time but also on educating adolescents about healthy online interaction methods. Guardianship differences (RQ3) Parental guardianship and school guardianship showed different predictive patterns. Parental guardianship significantly increased adolescents' likelihood of belonging to non-involved or victim-only types rather than the victim–perpetrator type. However, school guardianship showed significant effects only in distinguishing between victim-only and victim–perpetrator types. Specifically, in distinguishing victim-only type from victim–perpetrator type, both guardianship types showed associations with cyberbullying involvement type classification but differed in odds ratio magnitudes. Adolescents with parental guardianship had significantly higher odds of belonging to the victim-only type versus the victim–perpetrator type (1.280 times, calculated as 1/0.781) compared to those without. Similarly, adolescents with school guardianship had significantly higher odds of belonging to the victim-only versus the victim–perpetrator type (1.159 times, calculated as 1/0.863) compared to those without. Unlike school guardianship, parental guardianship was also statistically significant in distinguishing the non-involved type from the victim–perpetrator type. Adolescents with parental guardianship had significantly higher odds of belonging to the non-involved type versus the victim–perpetrator type (1.179 times, calculated as 1/0.848) compared to those without. These results demonstrate that parental guardianship is associated with distinguishing a broader range of cyberbullying role types than school guardianship. Furthermore, while parental guardianship was associated with both preventing cyberbullying participation itself and preventing victim-to-perpetrator transformation, school guardianship appears primarily related to preventing victim-to-perpetrator transformation. From a cultural perspective, East Asian adolescents tend to accept strict parental control as an expression of parental concern for their social success, unlike Western adolescents (Chung and Choi 2008 ). Because parental guardianship roles are particularly valued in Korea's collectivistic value system (Park and Cheah 2005 ), adolescents may be more responsive to parental oversight. These findings suggest that while schools play a supportive role, parental guardianship is a primary buffer against role overlap in cyberbullying. This may explain why cyberbullying victims typically turn to parents, while traditional bullying victims seek school support (Boniel-Nissim 2019 ). However, when parents lack digital literacy or monitoring capabilities, school supervision becomes essential. Therefore, effective prevention requires integrated approaches prioritizing parental involvement with institutional support. Conclusion Cyberbullying is characterized by an ambiguous boundary between the roles of victim and perpetrator. This boundary ambiguity suggests the need for a paradigm shift in cyberbullying prevention and response. This study employed RAT as an analytical framework to identify factors that predict victim–perpetrator categorization and analyzed the differential predictive aspects of parental and school supervision among Korean adolescents. Thus, this study provides insights for developing effective cyberbullying prevention and response policies in South Korea. It also enhances scholarly understanding of cyberbullying dynamics in an East Asian context, extending beyond existing Western-centered research, and provides a foundation for cross-cultural comparative studies. Future research should rely on longitudinal designs to trace how roles in cyberbullying shift over time. Using a variety of theoretical frameworks could help clarify distinctions in type and severity that single models might overlook. Cross-cultural comparisons, especially between collectivist and individualist cultures, could expose how sociocultural norms shape patterns of victim–perpetrator overlap. In parallel, closer attention to how cyberbullying plays out across platforms like Instagram, TikTok, and Discord—and how schools and parents collaborate in real-world settings—would offer more grounded insight. These lines of inquiry can support the development of more comprehensive frameworks for safeguarding adolescents in digital contexts. Declarations Ethical Statement Not applicable. Informed Consent Statement Not applicable. Funding Statement This research did not receive funding. 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Asian Journal of Criminology 20: 47-67. https://doi.org/10.1007/s11417-024-09449-7 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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digital technologies and the growing involvement of youth in online spaces, cyberbullying has become a significant public concern (Bochaver and Khlomov \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Foody et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It entails the use of digital means\u0026mdash;such as social networks and messaging platforms\u0026mdash;to cause harm through intimidation or harassment (Grigg \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Unlike traditional bullying, it is not limited by time or place and may be more damaging due to anonymity and the speed at which content spreads (Bochaver and Khlomov \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sticca and Perren \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). During the COVID-19 pandemic, adolescents became more dependent on digital communication, which intensified the reach and effects of cyberbullying and drew greater academic attention (Alfarizy et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kuhn et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobally, approximately 15% of adolescents report having experienced cyberbullying, although rates vary by country (World Health Organization \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). South Korea presents a particularly concerning case. With 96% of young people owning smartphones\u0026mdash;far higher than the global average of 71%\u0026mdash;37% of Korean adolescents report being cyberbullied in the past three years (Statista 2025a; Statista 2025b; Korea Communications Commission and National Information Society Agency \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This raises concerns about negative effects on adolescents\u0026rsquo; mental health, academic achievement, and social development (Bottino et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hase et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), highlighting the urgent need for fundamental research into cyberbullying characteristics to develop effective prevention and intervention strategies.\u003c/p\u003e\u003cp\u003eThis research need has led to growing scholarly attention on cases where the same adolescent is both a victim and a perpetrator\u0026mdash;a phenomenon known as victim\u0026ndash;perpetrator overlap (Ranjith et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tintori et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This group shows distinct characteristics: poorer peer relationships, more aggressive reactions to violence, and higher emotional instability and risky online behavior (Tintori et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While the issue of role overlap in cyberbullying has gained more attention, research in this area remains limited and often lacks a clear theoretical foundation (Xu \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo fill this gap, the study applies routine activity theory (RAT; Cohen and Felson \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) to investigate what drives victim\u0026ndash;perpetrator overlap among Korean adolescents. As a framework centered on exposure and behavioral routines, RAT is well suited to analyzing cyberbullying in online spaces (Paek et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In doing so, the study seeks to refine theoretical understanding and contribute to more integrated approaches to intervention.\u003c/p\u003e"},{"header":"Theoretical Background","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCyberbullying and Routine Activity Theory\u003c/h2\u003e\u003cp\u003eCyberbullying is defined as the intentional and repeated use of information and communication technologies to harm others (Smith et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tarigan \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While sharing traditional bullying\u0026rsquo;s characteristics of intentionality, repetitiveness, and power imbalance, cyberbullying also features anonymity, unlimited reach across time and space, and mass distribution (Boniel-Nissim, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Smith, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Anonymity, in particular, reduces perpetrators\u0026rsquo; inhibitions and amplifies victims\u0026rsquo; anxiety about not being able to identify the perpetrator (Dooley et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kowalski et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Cyberbullying takes many forms, including verbal abuse, defamation, stalking, sexual violence, information leakage, exclusion, extortion, and duress (Paek et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and has significant negative effects on youths\u0026rsquo; emotional stability, psychological health, and academic performance (Bottino et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hase et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRAT offers a structural framework for analyzing cyberbullying by identifying the conditions under which deviant behavior occurs. According to RAT, crime occurs when three key elements converge: a motivated offender, a suitable target, and the absence of capable guardians (Cohen and Felson \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). RAT has been applied to various crimes, and for cybercrime, its core elements are adapted as \u0026ldquo;exposure to motivated offenders,\u0026rdquo; \u0026ldquo;suitable online targets,\u0026rdquo; and \u0026ldquo;absence of capable guardians\u0026rdquo; (Marcum et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Morillo Puente and R\u0026iacute;os Hern\u0026aacute;ndez \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These components significantly predict cyberbullying victimization, both individually and in combination (Aizenkot \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the context of cyberbullying, these three factors apply as follows. First, \u0026ldquo;exposure to a motivated offender\u0026rdquo; refers to the degree to which an individual is exposed to a potential cyber perpetrator online and is associated with having friends who engaged in cyberbullying, exposure to harmful content, and witnessing cyberbullying. Associating with delinquent peers and learning deviant behaviors increases adolescents\u0026rsquo; likelihood of perpetrating cyberbullying (Skinner and Fream \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Becker and Clement \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Moreover, exposure to harmful content and witnessing cyberbullying are environmental factors that encourage youth to imitate risky and criminal behaviors (Motyka and Al-Imam \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Paek et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tercova and Smahel \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, \u0026ldquo;suitable online target\u0026rdquo; refers to the degree to which an individual becomes a potential target for cyber perpetrators and is measured through Internet usage and online interaction (Leukfeldt and Yar \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Paek et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kabiri et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). South Korea has high digital accessibility, with 99.97% household Internet access and 93.8% mobile Internet usage (National Information Society Agency \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The \"digital native\u0026rdquo; generation actively shares their daily lives through social media (Jang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), increasing their exposure and suitability as cyberbullying targets (Riya and Caeiro \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, assessments of \u0026ldquo;suitable online target\u0026rdquo; should include both quantitative aspects (e.g., Internet usage duration) and qualitative aspects (e.g., nature of interaction).\u003c/p\u003e\u003cp\u003eThird, \u0026ldquo;absence of a capable guardian\u0026rdquo; refers to the absence of protective mechanisms to curb cyberbullying and is related to parental guardianship and school guardianship. Korean Educational Development Institute (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) recognized both as vital to cyberbullying prevention, and Oh and Kwak (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) highlighted the necessity for comprehensive protection systems through joint efforts. Thus, the concept of \u0026ldquo;absence of a capable guardian\u0026rdquo; in cyberbullying needs to be understood from a perspective that encompasses the roles of parents and schools.\u003c/p\u003e\u003cp\u003eHowever, parental and school guardianship may demonstrate different protective effects depending on cyberbullying involvement types. In East Asian cultures, children tend to perceive stringent parental oversight as a sign of concern for their social achievements (Chung and Choi \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and Korea\u0026rsquo;s Confucian heritage underscores the parental role as a safeguard (Park and Cheah \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Paek et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that parental guardianship significantly reduced overall and non-violent victimization among Korean adolescents, whereas school guardianship had no significant effect on cyberbullying victimization. However, because most prior studies have examined guardianship in either victim or perpetrator roles separately, how these protective factors function for victim\u0026ndash;perpetrator adolescents remains unclear.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCyberbullying victim–perpetrator overlap\u003c/h3\u003e\n\u003cp\u003eWhile criminological research has traditionally examined victims and perpetrators as distinct categories (Jensen and Brownfield \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), recent studies on cyberbullying underscore the significance of victim\u0026ndash;perpetrator overlap, highlighting the complex interaction between aggression and vulnerability in digital settings (Chan and Wong \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Foody et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xu \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Adolescents\u0026rsquo; need for peer approval, coupled with underdeveloped impulse control, makes them susceptible to not only perpetrating but also experiencing cyberbullying (Walrave and Heirman \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Vandebosch and Van Cleemput (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) explain that from a social learning perspective, victimization experiences can trigger subsequent perpetrator behavior. A substantial number of cyberbullying perpetrators have previously been victims, and their aggressive behavior increases with repeated victimization experiences (Song \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Walrave and Heirman \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Online platforms enable individuals to conceal their identities and shift power dynamics, making it easier for offline victims to transition into online perpetrators (Est\u0026eacute;vez et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, rates of victim\u0026ndash;perpetrator overlap are often higher in cyberbullying than traditional bullying (Mishna et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCyberbullying victim\u0026ndash;perpetrators have diminished guilt and empathy due to their inability to observe their behavior\u0026rsquo;s impact on others, and they tend to exhibit the most negative outcomes in psychological health, physical health, and academic performance (Kowalski and Limber \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In particular, the nature of digital spaces facilitates retaliation and continued harassment, which can cause widespread harm (Mishna et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and the accumulation of victimization experiences increases the likelihood of perpetration, creating a vicious cycle (Park and Shim \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, existing cyberbullying research has primarily focused on either the victim or perpetrator aspect, leaving cyberbullying victim\u0026ndash;perpetrators insufficiently addressed despite their experience of serious negative consequences. Therefore, this study aims to apply RAT to identify the variables that best predict adolescent cyberbullying victim\u0026ndash;perpetrator types and to analyze the differential predictive patterns of guardianship variables. Against this background, this study established the following research questions:\u003c/p\u003e\u003cp\u003eRQ1: What is the distribution of adolescents\u0026rsquo; cyberbullying involvement types (non-involved, victim-only, perpetrator-only, victim\u0026ndash;perpetrator)?\u003c/p\u003e\u003cp\u003eRQ2: Among the variables measuring key components of routine activity theory, which best predicts the likelihood of adolescents being classified as the victim\u0026ndash;perpetrator type?\u003c/p\u003e\u003cp\u003eRQ3: Do parental guardianship and school guardianship show different patterns in predicting the likelihood of belonging to non-involved, victim-only, and perpetrator-only types when compared against the victim\u0026ndash;perpetrator type as the reference category?\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData collection and research setting\u003c/h2\u003e\u003cp\u003eThis study analyzed data obtained from the Cyberviolence Survey 2024 (Korea Communications Commission and National Information Society Agency \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The survey aimed to clarify the experiences and perceptions of cyberbullying in online spaces among adolescents, thereby providing foundational data for developing and establishing cyberbullying prevention and response policies. A stratified two-stage cluster sampling method was used to select schools in a region, select classes from the selected schools, and survey all students in the selected classes. The survey was completed by 9,479 adolescents enrolled in the fourth grade of elementary school to the third grade of high school in South Korea. The baseline period of the survey was September 1, 2023, to August 31, 2024, and the survey was conducted from September 2, 2024, to November 24, 2024, using in-person group interviews and online surveys.\u003c/p\u003e\u003cp\u003eThe survey consisted of the following items: experiences of victimization, perpetration, and witnessing cyberbullying in online spaces; cyberbullying awareness; cyberbullying prevention and response; digital hate speech; digital sexual offense prevention; social environment factors; and online activities. Given the purpose of the study, the survey items related to cyberbullying experiences, demographic factors, proximity to offenders, online engagement, and guardianship were analyzed.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasurement\u003c/h3\u003e\n\u003cp\u003eThis study measured the variables as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. First, cyberbullying victimization measured the experience of victimization in the online space in the past year. Eight types of cyberbullying victimization were assessed: verbal abuse, defamation, stalking, sexual violence, information leakage, exclusion, extortion, and duress. Responses to each item ranged from \u0026ldquo;never,\u0026rdquo; \u0026ldquo;1\u0026ndash;2 times per year,\u0026rdquo; \u0026ldquo;1\u0026ndash;2 times per 6 months,\u0026rdquo; \u0026ldquo;1\u0026ndash;2 times per month,\u0026rdquo; and \u0026ldquo;almost every day,\u0026rdquo; but the majority of respondents chose \u0026ldquo;never,\u0026rdquo; which we coded as a dichotomous variable (0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes). Cyberbullying perpetration was also measured across the same eight types of cyberbullying perpetration behaviors and dichotomized in the same way as the victimization variable.\u003c/p\u003e\u003cp\u003eThen, for this study, the cyberbullying involvement type was constructed from the cyberbullying victimization and cyberbullying perpetration variables. Specifically, based on the victimization and perpetration experiences of the eight types of cyberbullying, participants were categorized into four categories: 0) non-involved, 1) victim-only, 2) perpetrator-only, and 3) victim\u0026ndash;perpetrator.\u003c/p\u003e\u003cp\u003eThe study also included gender and school level to account for demographic characteristics. Gender was dichotomously coded as 0: female, 1: male, and school level was coded as 1: elementary, 2: middle, and 3: high.\u003c/p\u003e\u003cp\u003eFurthermore, this study used the variables of having friends who engaged in cyberbullying, harmful content exposure, and witnessing cyberbullying to measure proximity to offenders. Having friends who engaged in cyberbullying measured the presence of friends who bully others online within participants\u0026rsquo; close social networks. Specifically, the original scale was \u0026ldquo;none,\u0026rdquo; \u0026ldquo;1\u0026ndash;3,\u0026rdquo; \u0026ldquo;4\u0026ndash;6,\u0026rdquo; and \u0026ldquo;7 or more,\u0026rdquo; but was dichotomously coded as 0: no and 1: yes. This coding shift was made for ease of analysis and statistical interpretation due to the high proportion of \u0026ldquo;none\u0026rdquo; responses.\u003c/p\u003e\u003cp\u003eHarmful content exposure measured the experience of exposure to harmful content in online spaces. Specifically, this assessed the frequency of respondents\u0026rsquo; exposure to five types of content: violent and gory content, sexually explicit content, celebrity bashing, content about illegal behavior such as Internet gambling or fraud, and false information. The response scale consisted of \u0026ldquo;never,\u0026rdquo; \u0026ldquo;very rarely,\u0026rdquo; \u0026ldquo;rarely,\u0026rdquo; \u0026ldquo;often,\u0026rdquo; and \u0026ldquo;very often,\u0026rdquo; and the scores for each item were summed to create a final score. Internal consistency was assessed using Cronbach\u0026rsquo;s α coefficient (α\u0026thinsp;=\u0026thinsp;.803).\u003c/p\u003e\u003cp\u003eCyberbullying witness measured the experience of witnessing cyberbullying in online spaces in the past year. Specifically, respondents were asked to indicate whether they had witnessed a cyberbullying situation in an online space on a scale of \u0026ldquo;witnessed perpetration,\u0026rdquo; \"witnessed victimization,\" \"witnessed both,\" \"never witnessed.\" For the purposes of the study, this was dichotomously coded as 0: no and 1: yes.\u003c/p\u003e\u003cp\u003eIn addition, this study used Internet usage and online interaction to measure online engagement. Internet usage measured the average number of hours per day spent on the Internet in the past year, and the original scale was \u0026ldquo;less than 1 hour\u0026rdquo;, \u0026ldquo;more than 1 hour to less than 2 hours\u0026rdquo;, \u0026ldquo;more than 2 hours to less than 3 hours\u0026rdquo;, \u0026ldquo;more than 3 hours to less than 5 hours\u0026rdquo;, and \u0026ldquo;more than 5 hours.\u0026rdquo; However, due to the uneven distribution of respondents and the ease of interpretation of the results, we coded this variable as 0: low (\u0026lt;\u0026thinsp;3 hours/day) and 1: high (\u0026ge;\u0026thinsp;3 hours/day).\u003c/p\u003e\u003cp\u003eOnline interaction consisted of three items measuring participants' perceptions of online social support from friends. Items assessed whether friends (1) express approval on posts, (2) respond immediately to messages, and (3) send supportive messages during difficult times in online spaces. The response options were \"strongly disagree,\" \"disagree,\" \"agree,\" and \"strongly agree.\" The online interaction level was calculated by summing the scores of each item, and internal consistency was assessed using Cronbach's α coefficient (α\u0026thinsp;=\u0026thinsp;.775).\u003c/p\u003e\u003cp\u003eRegarding the guardianship concept, we used two key mechanisms that protect adolescents in digital environments: parental guardianship and school guardianship variables. Parental guardianship measured parental digital monitoring, specifically whether monitoring or content-blocking apps were installed to check Internet or smartphone usage (0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes). School guardianship measured school cyberbullying policy, specifically whether policies containing provisions for monitoring and punishment of cyberbullying existed (0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Measurement\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyberbullying Behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyberbullying Victimization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: no, 1: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExperience of cyberbullying victimization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyberbullying Perpetration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: no, 1: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExperience of cyberbullying perpetration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyberbullying Involvement Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: non-involved, 1: victim-only, 2: perpetrator-only, 3: victim\u0026ndash;perpetrator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCategorized based on victimization and perpetration experiences\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: female, 1: male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eParticipant's gender\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSchool Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1: elementary, 2: middle, 3: high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEducational level of participant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProximity\u003c/p\u003e\u003cp\u003eto Offenders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyberbullying Friends\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: no, 1: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresence of friends who engage in cyberbullying\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHarmful Content Exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComposite score (α\u0026thinsp;=\u0026thinsp;.803)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFrequency of exposure to harmful online content\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyberbullying Witness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: no, 1: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExperience of witnessing cyberbullying in online spaces\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnline Engagement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: low, 1: high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLevel of daily Internet usage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOnline Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComposite score (α\u0026thinsp;=\u0026thinsp;.775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLevel of online interaction with friends\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuardianship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParental Guardianship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: no, 1: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresence of parental digital monitoring\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSchool Guardianship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0: no, 1: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresence of school cyberbullying policy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis methods\u003c/h2\u003e\u003cp\u003eThis study employed multinomial logistic regression to identify factors associated with adolescents' cyberbullying involvement types. This statistical technique is appropriate when the dependent variable consists of three or more nominal categories. The analysis classified cyberbullying involvement into four types: non-involved, victim-only, perpetrator-only, and victim\u0026ndash;perpetrator, with victim\u0026ndash;perpetrator serving as the reference category. Odds ratios were calculated to determine the likelihood of belonging to each involvement type relative to the reference group.\u003c/p\u003e\u003cp\u003eIndependent variables were selected based on RAT\u0026rsquo;s three components: (1) proximity to offenders (cyberbullying friends, harmful content exposure, cyberbullying witness), (2) online engagement (Internet usage, online interaction), and (3) guardianship (parental and school guardianship). Demographic variables (gender and school level) were also included as covariates.\u003c/p\u003e\u003cp\u003eMulticollinearity among predictors was examined using tolerance and variance inflation factor (VIF) values prior to the main analysis. All statistical analyses were conducted using SPSS 26.0.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive analysis\u003c/h2\u003e\u003cp\u003eA total of 9,479 students were included in the final analysis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the most common type of cyberbullying involvement was non-involved (57.9%), followed by victim-only (20.0%), victim\u0026ndash;perpetrator (16.4%), and perpetrator-only (5.8%). Overall, 36.3% of students were victims of cyberbullying, while 22.2% were perpetrators.\u003c/p\u003e\u003cp\u003eIn terms of demographics, gender was relatively evenly distributed, with 51.3% male and 48.7% female. Participants were also evenly distributed across school levels: 35.3% elementary, 32.7% middle school, and 32.0% high school.\u003c/p\u003e\u003cp\u003eIn terms of proximity to offenders, 7.0% of students reported having friends who engage in cyberbullying, and 7.8% reported being a witness to cyberbullying. Harmful content exposure averaged 4.99 (SD\u0026thinsp;=\u0026thinsp;4.23) on a scale of 0\u0026ndash;20.\u003c/p\u003e\u003cp\u003eIn terms of online engagement, more than half (51.0%) of the respondents reported an average of 3 or more hours of Internet usage per day. In addition, the level of online interaction averaged 8.47 (SD\u0026thinsp;=\u0026thinsp;2.11) on a scale of 3\u0026ndash;12, indicating that most students had moderate to high levels of online interaction.\u003c/p\u003e\u003cp\u003eIn terms of guardianship, 26.0% of students reported having parental guardianship, and 47.1% reported having school guardianship.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Study Variables (N\u0026thinsp;=\u0026thinsp;9,479)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables and Categories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyberbullying Involvement Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81 (1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-involved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVictim-only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerpetrator-only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVictim\u0026ndash;perpetrator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyberbullying Victimization\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.36 (0.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyberbullying Perpetration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22 (0.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.51 (0.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.97 (0.82)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElementary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyberbullying Friends\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07 (0.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHarmful Content Exposure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.99 (4.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyberbullying Witness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08 (0.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInternet Usage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.51 (0.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOnline Interaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.47 (2.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParental Guardianship\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26 (0.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Guardianship\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47 (0.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation analysis\u003c/h2\u003e\u003cp\u003eThe correlation analysis results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, revealing complex relationships between cyberbullying experiences and various individual, social, and environmental factors. First, a moderate positive correlation existed between cyberbullying victimization and perpetration (r\u0026thinsp;=\u0026thinsp;0.416, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eBoth cyberbullying victimization and perpetration showed significant positive correlations with having friends who engage in cyberbullying (victimization: r\u0026thinsp;=\u0026thinsp;0.229, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; perpetration: r\u0026thinsp;=\u0026thinsp;0.285, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and harmful content exposure (victimization: r\u0026thinsp;=\u0026thinsp;0.227, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; perpetration: r\u0026thinsp;=\u0026thinsp;0.188, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Witnessing cyberbullying showed a significant positive correlation with victimization (r\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and perpetration (r\u0026thinsp;=\u0026thinsp;0.146, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, harmful content exposure showed a significant positive correlation with school level (r\u0026thinsp;=\u0026thinsp;0.275, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while witnessing cyberbullying showed the strongest correlation specifically with having friends who engage in cyberbullying (r\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eMeanwhile, Internet usage and online interaction both showed positive correlations with cyberbullying victimization (r\u0026thinsp;=\u0026thinsp;0.097, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.075, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and perpetration (r\u0026thinsp;=\u0026thinsp;0.096, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.042, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In particular, both Internet usage and online interaction showed significant correlations with school level (r\u0026thinsp;=\u0026thinsp;0.234, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.272, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eParental guardianship was negatively correlated with cyberbullying perpetration (r = -0.030, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but showed no correlation with victimization (r = -0.004, p\u0026thinsp;=\u0026thinsp;0.688). Conversely, school guardianship was positively correlated with cyberbullying victimization (r\u0026thinsp;=\u0026thinsp;0.030, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) while showing no significant relationship with perpetration (r = -0.004, p\u0026thinsp;=\u0026thinsp;0.714). Notably, the two guardianship variables showed a strong correlation with each other (r\u0026thinsp;=\u0026thinsp;0.180, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eRegarding demographic variables, gender showed a positive correlation with cyberbullying victimization (r\u0026thinsp;=\u0026thinsp;0.045, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and perpetration (r\u0026thinsp;=\u0026thinsp;0.106, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). School level showed a weak negative correlation with both victimization (r = -0.039, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and perpetration (r = -0.027, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation Coefficients Between Study Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Cyberbullying Victimization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Cyberbullying Perpetration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.416***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.045***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.106***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. School Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.039***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.027**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.027**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Cyberbullying Friends\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.229***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.285***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.094***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. Harmful Content Exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.227***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.188***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.275***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.085***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7. Cyberbullying Witness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.193***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.146***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.123***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.248***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.089***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8. Internet Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.097***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.096***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.064***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.234***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.037***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.229***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9. Online Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.075***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.042***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.229***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.272***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.200***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.029**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.161***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10. Parental Guardianship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.030**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.334***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.033**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.137***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.049***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.224***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.102***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11. School Guardianship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.030**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.149***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.067***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.180***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNote.\u003c/em\u003e *p\u0026thinsp;\u0026lt;\u0026thinsp;.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;.01. ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMultinomial logistic regression results\u003c/h2\u003e\u003cp\u003eThe effects of key RAT components on cyberbullying involvement types were examined. All variables showed tolerance values above 0.746 and VIF values below 1.340, meeting the multicollinearity criteria (tolerance\u0026thinsp;\u0026gt;\u0026thinsp;0.10, VIF\u0026thinsp;\u0026lt;\u0026thinsp;10), confirming no multicollinearity issues among predictors. The multinomial logistic regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultinomial Logistic Regression Results for Cyberbullying Role Types\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-involved\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVictim-only\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePerpetrator-only\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB(SE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB(SE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eB(SE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.638(0.247)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.513(0.254)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.415(0.369)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDemographic Factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.644(0.067)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[1.671, 2.169]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.481(0.074)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[1.398, 1.870]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.105(0.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[0.729, 1.113]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool Level \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElementary School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.821(0.093)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.367, 0.528]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.440(0.104)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0.526, 0.790]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.630(0.149)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[0.398, 0.713]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.721(0.081)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.415, 0.570]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.392(0.090)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0.566, 0.807]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.302(0.124)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[0.580, 0.942]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProximity to Offenders\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyberbullying Friends \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.554(0.127)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[10.019, 16.504]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.330(0.112)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[3.036, 4.705]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.681(0.153)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[1.465, 2.664]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHarmful Content Exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.141(0.008)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.855, 0.882]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.037(0.009)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0.947, 0.980]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.044(0.012)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[0.934, 0.980]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyberbullying Witness \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.173(0.113)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[2.590, 4.034]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.091(0.106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0.890, 1.348]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.632(0.181)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[1.320, 2.680]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOnline Engagement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternet Usage \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.458(0.067)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[1.385, 1.803]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.252(0.075)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[1.110, 1.492]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.183(0.107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[0.973, 1.481]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnline Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.069(0.017)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.903, 0.964]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.004(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0.959, 1.033]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.012(0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[0.937, 1.041]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGuardianship\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParental Guardianship \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.165(0.080)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.726, 0.991]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.247(0.088)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0.658, 0.928]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.129(0.128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[0.684, 1.129]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool Guardianship \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.013(0.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.870, 1.120]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.148(0.072)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0.749, 0.993]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.045(0.103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[0.855, 1.279]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote\u003c/em\u003e. The reference category is the victim\u0026ndash;perpetrator type. B\u0026thinsp;=\u0026thinsp;coefficient; SE\u0026thinsp;=\u0026thinsp;standard error; OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval. a Reference category\u0026thinsp;=\u0026thinsp;male. b Reference category\u0026thinsp;=\u0026thinsp;high school. c Reference category\u0026thinsp;=\u0026thinsp;having friends who engage in cyberbullying. d Reference category\u0026thinsp;=\u0026thinsp;having witnessed cyberbullying. e Reference category\u0026thinsp;=\u0026thinsp;high Internet usage. f Reference category\u0026thinsp;=\u0026thinsp;presence of parental guardianship. g Reference category\u0026thinsp;=\u0026thinsp;presence of school guardianship.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;.01. ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProximity to offenders\u003c/h2\u003e\u003cp\u003eCompared to students with friends who engage in cyberbullying, students without such friends had significantly higher odds of being classified as non-involved type (OR\u0026thinsp;=\u0026thinsp;12.859, B\u0026thinsp;=\u0026thinsp;2.554, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), victim-only type (OR\u0026thinsp;=\u0026thinsp;3.780, B\u0026thinsp;=\u0026thinsp;1.330, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and perpetrator-only type (OR\u0026thinsp;=\u0026thinsp;1.975, B\u0026thinsp;=\u0026thinsp;0.681, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) rather than victim\u0026ndash;perpetrator type. Higher levels of harmful content exposure significantly decreased the odds of belonging to non-involved type (OR\u0026thinsp;=\u0026thinsp;0.868, B = -0.141, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), victim-only type (OR\u0026thinsp;=\u0026thinsp;0.964, B = -0.037, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and perpetrator-only type (OR\u0026thinsp;=\u0026thinsp;0.957, B = -0.044, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to victim\u0026ndash;perpetrator type. Students who had not witnessed cyberbullying had significantly higher odds of being classified as non-involved type (OR\u0026thinsp;=\u0026thinsp;3.232, B\u0026thinsp;=\u0026thinsp;1.173, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and perpetrator-only type (OR\u0026thinsp;=\u0026thinsp;1.881, B\u0026thinsp;=\u0026thinsp;0.632, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the victim\u0026ndash;perpetrator type relative to those who had witnessed cyberbullying. However, no significant association was found for the victim-only type (B\u0026thinsp;=\u0026thinsp;0.091, p\u0026thinsp;=\u0026thinsp;0.390).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eOnline engagement\u003c/h2\u003e\u003cp\u003eCompared to students with higher Internet usage, students with lower Internet usage had significantly higher odds of being classified as the non-involved type (OR\u0026thinsp;=\u0026thinsp;1.580, B\u0026thinsp;=\u0026thinsp;0.458, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and victim-only type (OR\u0026thinsp;=\u0026thinsp;1.287, B\u0026thinsp;=\u0026thinsp;0.252, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in comparison with the victim\u0026ndash;perpetrator type. However, no significant association was found for the perpetrator-only type (B\u0026thinsp;=\u0026thinsp;0.183, p\u0026thinsp;=\u0026thinsp;0.088). Online interaction showed a significant negative association only with the non-involved type. With each unit increase in online interaction, the odds of belonging to the non-involved type (OR\u0026thinsp;=\u0026thinsp;0.933, B = -0.069, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) decreased compared to the victim\u0026ndash;perpetrator type. No significant associations were found for the victim-only type (B = -0.004, p\u0026thinsp;=\u0026thinsp;0.821) and perpetrator-only type (B = -0.012, p\u0026thinsp;=\u0026thinsp;0.645).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGuardianship\u003c/h2\u003e\u003cp\u003eCompared to students with parental guardianship, those without parental guardianship had significantly lower odds of being classified as the non-involved type (OR\u0026thinsp;=\u0026thinsp;0.848, B = -0.165, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the victim-only type (OR\u0026thinsp;=\u0026thinsp;0.781, B = -0.247, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) than the victim\u0026ndash;perpetrator type. However, no significant association was found for the perpetrator-only type (B = -0.129, p\u0026thinsp;=\u0026thinsp;0.313). Compared to students with school guardianship, those without school guardianship had significantly lower odds of being categorized as the victim-only type (OR\u0026thinsp;=\u0026thinsp;0.863, B = -0.148, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in comparison with the victim\u0026ndash;perpetrator type. No notable associations were found for the non-involved (B = -0.013, p\u0026thinsp;=\u0026thinsp;0.840) and perpetrator-only (B\u0026thinsp;=\u0026thinsp;0.045, p\u0026thinsp;=\u0026thinsp;0.663) types.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDemographic factors\u003c/h2\u003e\u003cp\u003eFemale students had higher odds of being classified as the non-involved type (OR\u0026thinsp;=\u0026thinsp;1.904, B\u0026thinsp;=\u0026thinsp;0.644, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the victim-only type (OR\u0026thinsp;=\u0026thinsp;1.617, B\u0026thinsp;=\u0026thinsp;0.481, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the victim\u0026ndash;perpetrator type relative to male students. No distinct association was found for the perpetrator-only type (B = -0.105, p\u0026thinsp;=\u0026thinsp;0.333). Compared to high school students, elementary school students had significantly lower odds of being classified as the non-involved type (OR\u0026thinsp;=\u0026thinsp;0.440, B = -0.821, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), victim-only type (OR\u0026thinsp;=\u0026thinsp;0.644, B = -0.440, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and perpetrator-only type (OR\u0026thinsp;=\u0026thinsp;0.532, B = -0.630, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than victim\u0026ndash;perpetrator type. Middle school students had significantly lower odds of being classified as the non-involved type (OR\u0026thinsp;=\u0026thinsp;0.486, B = -0.721, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), victim-only type (OR\u0026thinsp;=\u0026thinsp;0.676, B = -0.392, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and perpetrator-only type (OR\u0026thinsp;=\u0026thinsp;0.739, B = -0.302, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to victim\u0026ndash;perpetrator type relative to high school students.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed the overlap between victimization and perpetration in adolescent cyberbullying. The main findings are discussed according to the research questions posed.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eCyberbullying role distribution (RQ1)\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe 9,479 participants were categorized as non-involved (57.9%), victim-only (20.0%), perpetrator-only (5.8%), and victim\u0026ndash;perpetrator (16.4%). The victim\u0026ndash;perpetrator type comprised approximately 38.9% of those involved in cyberbullying, aligning with Mishna et al.'s (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) findings that victim\u0026ndash;perpetrator overlap rates are markedly higher in cyberbullying than in traditional bullying. This prevalence indicates that cyberbullying transcends simple unidirectional aggression, representing instead an interactive process where roles shift fluidly within digital environments (Bochaver and Khlomov \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Digital spaces enable identity concealment and power dynamic reconstruction, thereby facilitating the transformation of offline victims into online perpetrators, supporting Est\u0026eacute;vez et al.'s (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) research. The existence of the victim\u0026ndash;perpetrator type\u0026mdash;those who do not fit single role categories\u0026mdash;exposes limitations in dichotomous approaches to cyberbullying victimization and perpetration. Hence, more accurate and precise role distinction becomes essential when developing prevention and intervention strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of victim\u0026ndash;perpetrator classification (RQ2)\u003c/h2\u003e\u003cp\u003eHaving friends who engaged in cyberbullying emerged as the strongest predictor of victim\u0026ndash;perpetrator classification. Specifically, students without friends who engage in cyberbullying were 12.9 times more likely to be the non-involved type than the victim\u0026ndash;perpetrator type, supporting the social learning theory perspective (Skinner and Fream \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) that online deviant behavior is learned and reinforced through peer networks. Besides, exposure to harmful content and witnessing cyberbullying significantly increased the likelihood of being classified as a victim\u0026ndash;perpetrator. This finding aligns with existing research showing that recurrent exposure to violent or sexual material and instances of cyberbullying diminishes psychological barriers to aggressive behavior by fostering the normalization of violence and inducing moral desensitization (Motyka and Al-Imam \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tercova and Smahel \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings imply that preventive measures should go beyond individual behavior and adopt an ecological perspective that encompasses peer networks and digital contexts.\u003c/p\u003e\u003cp\u003eMeanwhile, compared to students with higher Internet usage, those with lower Internet usage had significantly higher odds of being classified as the non-involved type and victim-only type relative to the victim\u0026ndash;perpetrator type, supporting previous studies (Leukfeldt and Yar \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Park and Shim \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Walrave and Heirman \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) showing that excessive Internet use among adolescents can lead to a cycle of victimization and retaliation. Meanwhile, higher levels of online interaction are associated with increased odds of adolescents being classified as victim\u0026ndash;perpetrator type rather than non-involved type. Adolescents are \u0026ldquo;digital natives\u0026rdquo; who have been exposed to digital environments since childhood and are familiar with online interaction via social media (Jang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results suggest that cyberbullying prevention should focus not only on limiting Internet usage time but also on educating adolescents about healthy online interaction methods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eGuardianship differences (RQ3)\u003c/h2\u003e\u003cp\u003eParental guardianship and school guardianship showed different predictive patterns. Parental guardianship significantly increased adolescents' likelihood of belonging to non-involved or victim-only types rather than the victim\u0026ndash;perpetrator type. However, school guardianship showed significant effects only in distinguishing between victim-only and victim\u0026ndash;perpetrator types.\u003c/p\u003e\u003cp\u003eSpecifically, in distinguishing victim-only type from victim\u0026ndash;perpetrator type, both guardianship types showed associations with cyberbullying involvement type classification but differed in odds ratio magnitudes. Adolescents with parental guardianship had significantly higher odds of belonging to the victim-only type versus the victim\u0026ndash;perpetrator type (1.280 times, calculated as 1/0.781) compared to those without. Similarly, adolescents with school guardianship had significantly higher odds of belonging to the victim-only versus the victim\u0026ndash;perpetrator type (1.159 times, calculated as 1/0.863) compared to those without. Unlike school guardianship, parental guardianship was also statistically significant in distinguishing the non-involved type from the victim\u0026ndash;perpetrator type. Adolescents with parental guardianship had significantly higher odds of belonging to the non-involved type versus the victim\u0026ndash;perpetrator type (1.179 times, calculated as 1/0.848) compared to those without.\u003c/p\u003e\u003cp\u003eThese results demonstrate that parental guardianship is associated with distinguishing a broader range of cyberbullying role types than school guardianship. Furthermore, while parental guardianship was associated with both preventing cyberbullying participation itself and preventing victim-to-perpetrator transformation, school guardianship appears primarily related to preventing victim-to-perpetrator transformation. From a cultural perspective, East Asian adolescents tend to accept strict parental control as an expression of parental concern for their social success, unlike Western adolescents (Chung and Choi \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Because parental guardianship roles are particularly valued in Korea's collectivistic value system (Park and Cheah \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), adolescents may be more responsive to parental oversight. These findings suggest that while schools play a supportive role, parental guardianship is a primary buffer against role overlap in cyberbullying. This may explain why cyberbullying victims typically turn to parents, while traditional bullying victims seek school support (Boniel-Nissim \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, when parents lack digital literacy or monitoring capabilities, school supervision becomes essential. Therefore, effective prevention requires integrated approaches prioritizing parental involvement with institutional support.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCyberbullying is characterized by an ambiguous boundary between the roles of victim and perpetrator. This boundary ambiguity suggests the need for a paradigm shift in cyberbullying prevention and response.\u003c/p\u003e\u003cp\u003eThis study employed RAT as an analytical framework to identify factors that predict victim\u0026ndash;perpetrator categorization and analyzed the differential predictive aspects of parental and school supervision among Korean adolescents. Thus, this study provides insights for developing effective cyberbullying prevention and response policies in South Korea. It also enhances scholarly understanding of cyberbullying dynamics in an East Asian context, extending beyond existing Western-centered research, and provides a foundation for cross-cultural comparative studies.\u003c/p\u003e\u003cp\u003eFuture research should rely on longitudinal designs to trace how roles in cyberbullying shift over time. Using a variety of theoretical frameworks could help clarify distinctions in type and severity that single models might overlook. Cross-cultural comparisons, especially between collectivist and individualist cultures, could expose how sociocultural norms shape patterns of victim\u0026ndash;perpetrator overlap. In parallel, closer attention to how cyberbullying plays out across platforms like Instagram, TikTok, and Discord\u0026mdash;and how schools and parents collaborate in real-world settings\u0026mdash;would offer more grounded insight. These lines of inquiry can support the development of more comprehensive frameworks for safeguarding adolescents in digital contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical Statement\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eThis research did not receive funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSeoung Won Choi (conceptualization, analysis, and writing); Youngsub Lee (analysis and writing); Julak Lee (data collection and analysis)\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAizenkot, D. 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Offending-victimization overlap in cyberbullying among Chinese youths: Theories, gender differences, and methodological innovation. \u003cem\u003eAsian Journal of Criminology \u003c/em\u003e20: 47-67. https://doi.org/10.1007/s11417-024-09449-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cyberbullying, Victim–perpetrator Overlap, Routine Activity Theory, Cyberbullying Friends, Guardianship","lastPublishedDoi":"10.21203/rs.3.rs-7707750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7707750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCyberbullying has proliferated globally with rapid digitalization and has emerged as a serious social problem in South Korea. Existing studies have approached cyberbullying through a binary structure of perpetrators and victims, but this approach inadequately explains the complex interactions in digital spaces. Therefore, this study focuses on the role overlap phenomenon in cyberbullying to analyze these multifaceted characteristics. This study analyzed survey data from 9,479 Korean adolescents collected in 2024, applying routine activity theory alongside descriptive statistics, correlation analysis, and multinomial logistic regression. The results showed that 16.4% of participants were classified as the victim\u0026ndash;perpetrator type, experiencing both victimization and perpetration in cyberbullying incidents. Among all predictors, having friends involved in cyberbullying emerged as the strongest factor. Parental and school guardianship exhibited distinct predictive patterns for the victim\u0026ndash;perpetrator type relative to other involvement categories (non-involved, victim-only, and perpetrator-only). These findings provide empirical support for the complex characteristics of cyberbullying and demonstrate the necessity of multilayered intervention strategies beyond simple binary approaches. Based on these findings, this study recommends family-centered prevention programs with complementary school-based interventions, given that parental guardianship serves as the primary buffer against cyberbullying role overlap.\u003c/p\u003e","manuscriptTitle":"Blurring the Lines: Factors Influencing Victim–perpetrator Overlap in Korean Adolescent Cyberbullying Behaviors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 07:14:45","doi":"10.21203/rs.3.rs-7707750/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56eba301-ae5b-4cde-9995-638fb337b829","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T07:14:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 07:14:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7707750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7707750","identity":"rs-7707750","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}