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Article

The Impact of Cyberbullying Victimization on Adolescents’ School-Related Distress Across Nine Countries: Examining the Mitigating Role of Teacher Support

by
Shaghayegh Sheri McVay
*,
Jonathan Santo
and
Hannah Lydiatt
Department of Developmental Psychology, University of Nebraska at Omaha, Omaha, NE 68182, USA
*
Author to whom correspondence should be addressed.
Soc. Sci. 2025, 14(9), 559; https://doi.org/10.3390/socsci14090559
Submission received: 29 May 2025 / Revised: 4 September 2025 / Accepted: 15 September 2025 / Published: 18 September 2025
(This article belongs to the Section Childhood and Youth Studies)
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Abstract

The pervasive integration of technology into the daily lives of children and adolescents, coupled with the popularity and extensive use of social media by this age group, has raised significant concerns, highlighting cyberbullying victimization as a serious global public health issue that requires further investigation. The aim of the present study was to examine the impact of cyberbullying victimization above and beyond traditional forms of peer victimization on adolescents’ school-related distress using a sample of 28,883 adolescents across nine countries. This study also assessed the moderating role of teacher support on the association between cyberbullying victimization and school-related distress. The results of structural equation modeling suggested that adolescents who experienced higher levels of victimization (both traditional and cyber) scored significantly higher on school-related distress. In addition, the main effect of cyberbullying victimization above and beyond traditional forms of peer victimization on school-related distress was significant. More importantly, cyberbullied victims who perceived their teachers as supportive reported lower school-related distress compared to their peers with low teacher support. As technology continues to become more accessible in both homes and educational settings, the findings of this study underscore the need to address cyberbullying as a distinct phenomenon posing unique challenges for adolescents and requiring targeted intervention strategies. In addition, the findings contribute to understanding a more comprehensive portrait of adolescent development and how social support influences adolescents’ well-being.

1. Introduction

Adolescence, marked by shifting social dynamics, constant biological changes, and heightened emotions, is a critical period when young individuals are particularly vulnerable to the effects of peer victimization (Pellegrini et al. 1999; Zhang et al. 2022). Peer victimization is a prevalent issue among children and adolescents worldwide, with rates as high as 44% reported in Eastern Mediterranean and African countries, and as low as about 8% in European nations (Biswas et al. 2020). This construct encompasses any form of physical, psychological, or verbal harassment directed at an individual by their peers (Juvonen and Graham 2001; van Geel et al. 2018).
Not surprisingly, peer victimization is not restricted to in-person interactions. The rapid advancement of Information and Communication Technologies (ICTs) and the widespread use of social media and its popularity among adolescents have also created major social concerns about the dangers of cyberbullying victimization and its impact on children and adolescents’ well-being (Cole et al. 2016; Gardella et al. 2017). Cyberbullying victimization involves intentional hostile or aggressive acts perpetrated through digital and electronic media to cause harm to others (Patchin and Hinduja 2006; Tokunaga 2010). This construct stands apart from traditional peer victimization as it constitutes an extreme invasion of personal space (Kwan et al. 2020). Some scholars even argue that cyberbullying experiences are more stressful than traditional forms of peer victimization (Juvonen and Gross 2008; Perren et al. 2010) as their impact is exacerbated by the anonymity of the perpetrator and the ability to inflict harm at any time and from any place (Kwan et al. 2020).
Although experiences of cyberbullying victimization are not new, inconsistencies in terminology and measurement tools used in previous studies have resulted in substantial variability in the reported prevalence rates, ranging from 13% to 57% (Sorrentino et al. 2023; Zhu et al. 2021). Moreover, insufficient attention has been given to comparing the impacts of traditional forms of peer victimization and cyberbullying (Jiang and Shi 2024), impeding a comprehensive understanding of the overlapping and unique effects of these distinct but related constructs. In an effort to address these gaps, the current study aimed to examine the association between cyberbullying victimization and school-related distress above and beyond traditional forms of peer victimization among 28,883 adolescents across nine countries.
In addition, although individual behavior patterns may increase the likelihood of peer victimization, these negative interactions are ultimately part of a broader social system (Pepler et al. 1999). Therefore, it is important to examine protective factors that may reduce the impact of these harmful experiences on adolescents’ well-being. Bronfenbrenner’s social–ecological model emphasizes that individuals exist within interconnected systems such as family, peers, schools, and communities, and the dynamic and complex interactions between these contextual levels shape development (Bronfenbrenner 1977, 1979). Although numerous theoretical models can be utilized to assess the influence of interpersonal relationships on adolescents’ adjustment (Kochenderfer-Ladd and Ladd 2010; Sameroff (2000), the social–ecological framework is arguably the most recognized model (Gini et al. 2018). In addition, the stress buffering hypothesis (Cohen and Wills 1985) suggests that social support mitigates emotional stress by helping individuals adjust their emotional responses to adverse and challenging experiences. This support fosters a sense of control, alleviates feelings of loneliness, and offsets the impact of psychological distress on the individual (Dijkstra and Veenstra 2011; Heerde and Hemphill 2018; Thoits 1986). Hence, this study drew on the tenets of Bronfenbrenner’s (1979) social–ecological framework and examined the moderating role of teacher support on the association between cyberbullying victimization and school-related distress using structural equation modeling (SEM).

1.1. Peer Victimization

Although experiences of being victimized by peers have been studied in various contexts, such as the workplace and among college students, the literature on peer victimization is primarily focused on adolescents (Troop-Gordon 2017). This could be partially attributed to the fact that adolescents prioritize interpersonal relationships more than any other age group, making their perception of the impact of such experiences more pronounced (van Geel et al. 2018).
Similarly to bullying (a narrower construct defined by intentional and repeated harmful actions perpetrated by individuals or groups who possess actual or perceived power over their targets, Olweus 1993), the consequences of being victimized by peers are substantial. Peer-victimized adolescents report internalizing problems such as depressed affect, emotional maladjustment, depression, lower self-esteem, increased anxiety, and loneliness (Aboagye et al. 2021; Kljakovic and Hunt 2016; Modecki et al. 2014; Moore et al. 2017; Nakamoto and Schwartz 2010; Reijntjes et al. 2010; Santo et al. 2018; Wolke et al. 2000; Wu et al. 2015; Yang et al. 2022). Additionally, the psychological distress resulting from these experiences puts the victimized individuals at a greater risk for developing suicidal ideation, aggressive behavior, and physical health problems such as sleeping issues, bedwetting, headaches, and stomachaches (Rigby 2001; Williams et al. 1996). In addition, peer victimization is related to a host of school-related issues, such as lower grade point average, school absenteeism, lower academic achievement, and poor classroom performance (Lopez and DuBois 2005; Nakamoto and Schwartz 2010). These consequences hinder the healthy development of adolescents and impose major societal costs through increased demand for mental health services and interventions. In addition, as digital technologies become more embedded in adolescents’ daily lives, peer victimization has expanded into online spaces. This shift has given rise to cyberbullying victimization, a closely related construct that shares many of the same consequences as traditional forms of bullying and peer victimization, while also introducing distinct challenges.

1.2. Cyberbullying Victimization

In a random sample of adolescents aged 13–17, almost half report using the internet multiple times a day, and nine out of ten report using the internet at least once per day (Pew Research Center 2023). Indeed, this dramatic growth in internet usage appears to be a worldwide phenomenon. Benvenuti et al. (2023) reported that approximately 71% of young people worldwide were internet users. Notably, internet usage among adolescents ages 13–17, particularly via smartphones, does not significantly vary as a function of household income (Pew Research Center 2023). Furthermore, communication via such methods is largely unsupervised, which adds complexity to instances of cyberbullying victimization (Gardella et al. 2017).
In addition, victims can be subjected to such behaviors through various channels, including chat rooms, emails, instant messaging, mobile phones, text messaging, websites, and images or video clips (Dooley et al. 2009; Smith et al. 2008).
It is also important to mention that although cyberbullying can be viewed as an extension of traditional victimization (Slonje et al. 2013), the repetitive aspect of these behaviors may not hold the same significance as it does in traditional forms of victimization (Fauman 2008). For instance, a single humiliating image can lead to ongoing ridicule and embarrassment for the victim. Thus, even though the harmful act may not occur repeatedly, the damage inflicted by it is sustained through ongoing humiliation. In addition, the anonymity, the breadth of the potential audience, and the ability to reach victims beyond physical spaces set cyberbullying apart from traditional forms of peer victimization (Dooley et al. 2009; Fauman 2008).
Ultimately, the existing literature collectively indicates that the adverse effects of cyberbullying include internalizing problems, diminished self-esteem, depressive affect, depression, psychological distress, social anxiety, loneliness, suicidal ideation, hostility, hyperactivity, and substance abuse (Aoyama et al. 2011; Cole et al. 2016; Juvonen and Gross 2008; Nixon 2014; Smith et al. 2008; Ybarra et al. 2006). These consequences extend beyond the individual, imposing substantial costs on society as a whole.

1.3. School-Related Distress and Peer Victimization

In the context of negative peer relationships, bullying and peer victimization have been established as risk factors for adolescents’ mental health (Koyanagi et al. 2019). In the United States alone, over 25% of adolescents reported being harassed by their peers at least once in the past year (Wang et al. 2018). Yet, peer victimization is a global concern rather than one limited to Western contexts. In a longitudinal study conducted over 15 years on adolescents aged 12–15 in 29 countries, almost 40% of all respondents reported being bullied at least once (Smith et al. 2023). In some countries, this number was even higher: more than half of teens in the Seychelles reported bullying victimization across multiple years (Smith et al. 2023). School-related distress resulting from such challenging experiences may involve feelings of loneliness, emotional and social maladjustment, disengagement, a reduced sense of belonging, and difficulties forming close peer relationships (Flaspohler et al. 2009; Nansel et al. 2001). School-related psychological distress associated with peer victimization can also include social dysfunction and somatic or psychosomatic symptoms and clinical depression (Rigby 2001). Considering these emotional challenges and the potential for lasting negative impacts on adolescents, examining school-related distress within the context of cyber victimization is essential for effectively implementing preventive interventions.

1.4. Teacher Support

Even though, being well-liked by peers and having friends has been shown to hinder the effects of peer victimization on young individuals (Pellegrini et al. 1999), the significant amount of time children and adolescents spend interacting with their teachers highlights the need to explore these relationships and their influence on adjustment (Gini et al. 2018). Teacher support may enhance students’ sense of safety and decrease the likelihood of negative behaviors and conflicts (Murdock 1999). In addition, the previous literature demonstrates that teacher support plays a key role in reducing school-related stress and fostering higher levels of academic engagement (Correia and Dalbert 2007; Danielsen et al. 2010). Moreover, teacher support has been shown to have a negative correlation with depression and a positive association with self-esteem and social skills (Colarossi and Eccles 2003). Adolescents who perceive their teachers as supportive also show lower tendencies to experiment with alcohol, cigarettes, and marijuana (McNeely and Falci 2004).
In the context of bullying and peer victimization, the role of teachers may be understood as preventative (Flaspohler et al. 2009). For instance, compared to students who experience peer victimization, those who are not bullied perceive their teachers as more supportive and report having teachers they can confide in about their problems (Demaray and Malecki 2003). Additionally, it is important to highlight that although cyberbullying victimization often occurs outside of school, research demonstrates that victims know their bullies from school (Juvonen and Gross 2008; Smith and Slonje 2009). This underscores the crucial role that teachers can play in supporting adolescents who are victims of cyberbullying (Hellfeldt et al. 2020).
However, although social support has been demonstrated as an effective coping strategy to reduce the likelihood of poor psychosocial outcomes among adolescents (for a meta-analysis, refer to Heerde and Hemphill 2018), the role of teacher support in helping cyber victimized adolescents remains inconsistent (Nagar and Talwar 2023). This lack of clarity could contribute to students’ non-disclosure and teacher inaction (Nagar and Talwar 2023).

1.5. The Current Study

The aim of the current study was to enhance global representation and evaluate the impact of cyberbullying victimization on school-related distress above and beyond traditional forms of peer victimization.
Additionally, since exploring the presence or absence of protective factors such as social support can provide valuable insights into further elucidating the impact of these experiences (Demaray and Malecki 2003), another goal was to examine the moderating role of teacher support on the association between cyberbullying victimization and school-related distress.
To do so, we analyzed self-reported data from the Study on Social and Emotional Skills (SSES) conducted by the Organization for Economic Cooperation and Development (OECD) in nine countries. The sample consisted of 28,883 adolescents (M (age) = 15.43) from Bogotá and Manizales (Colombia), Daegu (Korea), Helsinki (Finland), Houston (USA), Istanbul (Türkiye), Moscow (Russia), Ottawa (Canada), Sintra (Portugal) and Suzhou (China) representing diverse cultural backgrounds across Asia, Americas and Europe. Structural equation modeling (SEM, M-Plus, ver. 7.00; Muthén and Muthén 2015) was used to test the following hypotheses:
Traditional forms of peer victimization will be positively associated with experiencing school-related distress among adolescents (Hypothesis 1). Cyberbullying victimization will be positively associated with experiencing school-related distress above and beyond the impact of traditional peer victimization (Hypothesis 2). Teacher support will buffer the impact of cyberbullying victimization on school-related distress. Specifically, victimized adolescents who report their teachers as supportive experience less school-related distress compared to their counterparts (Hypothesis 3). Refer to Figure 1 for a conceptual model depicting these hypotheses.

2. Materials and Methods

2.1. Sample

The present study analyzed self-reported data from the Study on Social and Emotional Skills (SSES) conducted between 2019 and 2020 by the Organization for Economic Cooperation and Development (OECD). This international cross-sectional survey aimed to assess variations in adolescents’ social and emotional skills and their impact on academic performance and well-being (OECD 2021). Although the original SSES questionnaire consists of various measures, only the items relevant to the current study are described here. In the context of this study, the sample included 28,883 adolescents (M = 15.43) from the following countries: Canada (7.12%), China (12.33%), Colombia (23.39%), Finland (8.17%), Portugal (5.45%), Russia (11.62%), South Korea (11.22%), Turkey (10.68%) and the United States (10.02%). Of the participants, 51.85% identified themselves as girls and 48.15% as boys. The breakdown of the participants by country and gender is depicted in Table 1

2.2. Measures

2.2.1. School-Related Distress

School-related distress was measured using three items from the survey. Adolescents were asked to indicate to what extent they agreed with each statement (e.g., I feel awkward and out of place in my school). Each item was assessed using a 4-point Likert-type scale ranging from 1 (strongly disagree) to 4 (strongly agree). The scale’s internal consistency indicated by Cronbach’s alpha was 0.766.

2.2.2. Peer Victimization

Traditional peer victimization was assessed using four items designed to examine this construct over the preceding 12 months. Adolescents were asked to indicate how often they experienced victimization using a 4-point Likert scale anchored by 1 (never or almost never) to 4 (once a week or more). “Other students made fun of me” is an example of the questions asked.
The scale demonstrated the reliability of α = 0.775.

2.2.3. Cyberbullying Victimization

This construct was measured using two items to capture how often the adolescents experienced harassment while using social media (e.g., Facebook, Instagram, Snapchat). Each item was assessed using a 5-point Likert-type scale ranging from 1 (never or almost never) to 5 (I don’t use social media). “I have been threatened by people” is an example of items used. The inter-item correlation between the two items was 0.59, p < 0.000.

2.2.4. Teacher Support

Teacher Support was measured using three statements. Participants responded to each question using a 4-point Likert scale anchored by 1 (never or almost never) to 4 (once a week or more). Higher values indicated a greater perception of teacher support, while lower values reflected less teacher support. “Most of my teachers are interested in my well-being” is an example of the items included. This scale demonstrated internal consistency of α = 0.750.

2.2.5. Approach to Data Analysis

Prior to conducting the primary analyses, data screening was performed, and the distributional characteristics of the variables were assessed. This allowed us to identify potential outliers, check for any missing data, and ensure that the assumptions of linearity and homoscedasticity were not violated. Additionally, although SEM assumes that the relationship between the variables is linear (Kline 2005), a visual screening of scatterplots was also obtained. The normality test indicated that the assumption of normality was partially violated. Therefore, the models were estimated using Maximum Likelihood Estimation with Robust Standard Errors (MLR) to handle data more effectively.
Descriptive statistics for the variables were then computed using SPSS. Subsequently, hypothesis testing was performed using Structural Equation Modeling. To accomplish this, a hybrid model was created with school-related distress (i.e., the criterion) being treated as a latent factor and teacher support, cyberbullying victimization, and traditional peer victimization (i.e., the predictors) as observed variables. Confirmatory factor analysis (CFA) was then conducted using structural equation modeling in M-plus to ensure that the observed indicators adequately captured the latent construct of school-related distress. All of the factor loadings of the indicators were above 0.67 (p < 0.001), and the overall factor structure was reliable (ω = 0.77). Additionally, the model’s fit to the data was assessed following Hu and Bentler’s (1999) guidelines using the following criteria: root mean square error of approximation (RMSEA) less than 0.08, confirmatory fit index (CFI) above 0.90, and standardized root mean square residual (SRMR) less than 0.08. The potential moderating effect of teacher support on the association between school-related distress and cyber victimization was then examined.

3. Results

Please refer to Table 2 for descriptive statistics for the primary variables of the study. The first hypothesis aimed to test the main effect of traditional forms of peer victimization on school-related distress. To accomplish this, school-related distress was regressed on peer victimization and the resulting model was a good fit the data (χ2(2) = 13.26, p < 0.05, CFI = 1.00, RMSEA = 0.01, 90% CI [0.00, 0.02], SRMR = 0.00). The results indicated that the main effect of peer victimization on school-related distress was significant and positive (b = 0.41, SE = 0.01, β = 0.41, p < 0.001). In interpreting the results, a positive standardized beta (β) indicates the amount that the outcome variable increases as the predictor increases by 1 standard deviation. In this case, higher levels of peer victimization were associated with greater school-related distress. To explain, adolescents who were victimized by their peers reported significantly higher levels of school-related distress compared to others. Thus, Hypothesis 1 was supported.
The relationship between cyberbullying victimization and experiencing school-related distress above and beyond traditional forms of peer victimization was examined next by adding this construct to the structural regression model. The new model remained a good fit to the data, (χ2(4) = 4.61, p > 0.05, CFI = 1.00, RMSEA = 0.00, 90% CI [0.00, 0.01], SRMR = 0.00). The results suggested that cyber victimization was significantly and positively associated with school-related distress controlling for traditional forms of peer victimization (b = 0.17, SE = 0.01, β = 0.13, p < 0.001). Adolescents who experienced higher levels of cyber victimization scored higher on school-related distress measure. As such, the results supported Hypothesis 2, and the model explained 10.7% of the variability in school-related distress.
Next, teacher support was added, and school-related distress regressed on this observed predictor. This new model also remained a good fit to the data (χ2(6) = 69.77, p < 0.05, CFI = 0.99, RMSEA = 0.02, 90% CI [0.01, 0.02], SRMR = 0.01). As expected, the results suggested that teacher support was a significant negative predictor of school-related distress (b = −0.14, SE = 0.01, β = −0.18, p < 0.001). To test Hypothesis 3, the potential moderating effect of teacher support on the association between cyberbullying victimization and school-related distress was assessed. The results demonstrated that the interaction was significant (b = 0.04, SE = 0.01, β = 0.12, p < 0.001), such that the relationship between cyber victimization and school-related distress varied as a function of teacher support, indicating that adolescents who experienced higher levels of cyber victimization reported lower school-related distress if they perceived their teachers as supportive (refer to Figure 2). In addition, simple slopes analyses indicated that the effect of cybervictimization on school-related distress was more pronounced among adolescents reporting low teacher support (b = 0.31, SE = 0.04, z = 7.42, p < 0.05) in compared to those reporting high teacher support (b = 0.20, SE = 0.03, z = 5.83, p < 0.05. Therefore, Hypothesis 3 was supported. The updated model explained 13.4% of the variability in school-related distress. Table 3 depicts the results discussed above.
Please note that in the figure below, the y-axis has been truncated for better visualization of the effects. Error bars represent the 95% confidence intervals around the means.
Exploratory analyses were also conducted to examine the potential buffering effect of teacher support on the association between traditional peer victimization and school-related distress. Interestingly, this interaction was not significant (b = 0.02, SE = 0.01, β = 0.06, p > 0.05), suggesting that teacher support did not have a buffering effect on the association between traditional forms of peer victimization and school-related distress in this sample (χ2(10) = 67.20, p < 0.05, CFI = 0.99, RMSEA = 0.01, 90% CI [0.01, 0.02], SRMR = 0.01). This final model is presented in Figure 3. In addition, we specified and tested an alternative model with different directionalities. In this model, cyber and traditional victimization were regressed on school-related distress. This alternative model demonstrated a good fit to the data, consistent with Kline’s (2005) guidelines that multiple models can fit the same data equally well. However, we retained our original model as it provided a clearer narrative. According to Kline (2005), when multiple models fit the data equally well, there is no statistical justification for favoring one over the other; thus, the decision to retain the original model was guided by its interpretive clarity.

4. Discussion

Using a globally representative sample of adolescents, the current study examined the distinct effect of cyberbullying victimization on school-related distress, above and beyond the effects of traditional peer victimization. We also investigated the buffering effect of teacher support on the association between cyberbullying victimization and school-related distress.
In line with the first and second hypotheses, the results suggested that both traditional and cyber victimization were significant positive predictors of school-related distress among adolescents. This indicates that adolescents who experience any form of peer victimization may struggle to feel safe, supported, or emotionally regulated in school environments. Heightened school-related distress can disrupt students’ ability to engage with learning, form positive relationships with peers and teachers, and feel connected to their school community (Pate et al. 2017). Over time, these disruptions may contribute to broader negative outcomes such as academic disengagement, lower achievement, school avoidance, or even dropout (Lopez and DuBois 2005; Nakamoto and Schwartz 2010). Emotional distress tied to the school environment may also increase vulnerability to internalizing symptoms such as anxiety and depression, especially when left unaddressed (Connolly et al. 2022).
Another goal of the current study was to assess the moderating role of teacher support on the association between cyber victimization and school-related distress (Hypothesis 3). The results also supported this prediction, indicating that adolescents who experienced cyber victimization reported lower school-related distress if they perceived their teachers as supportive. This finding from an internationally diverse sample underscores the crucial role of teacher support in promoting positive mental health among adolescents. Cyberbullying often occurs outside the immediate view of adults, including in digital spaces where youth may feel isolated, targeted, and unsure how to respond. In such contexts, a strong school climate and trust in teachers may provide a rare sense of safety and connection (Nagar and Talwar 2023). Drawing on ecological systems theory, supportive teachers may function as proximal protective factors that can mitigate the stressors of cyber victimization, especially when peers are the source of harm and parents are be perceived as too distant from the online social world (Bronfenbrenner 1979).
However, it was perplexing that although having supportive teachers was associated with lower school-related distress, the results of exploratory analyses suggested that teacher support did not moderate the association between traditional peer victimization and school-related distress. This could be partially explained by the possibility that adolescents view adults as unhelpful and ineffective in situations involving traditional forms of peer victimization. A study by Rigby and Bagshaw (2003) revealed that about 40% of Australian adolescents felt that their teachers were disinterested in addressing bullying. Additionally, a comparable percentage of the students indicated that they were either reluctant or opposed to the idea of collaboration with teachers, believing they lacked the necessary conflict resolution skills to intervene and stop bullying. Furthermore, it is possible that although adolescents report having supportive adults, they may not turn to them for help when they are harassed by peers (Kim et al. 2022). In such cases, it is not general perceptions of support but the demonstrated ability to resolve conflict that determines whether adolescents seek help. For example, Aceves et al. (2010) collected data from a low-income community on the U.S. West Coast and found that adolescents who perceived their teachers as fair and effective in resolving conflict were more likely to consider seeking help from them when harassed by peers; otherwise, they used physical aggression as the appropriate response and fought back. This suggests that the teachers’ conflict resolution skills and their actions during hostile situations, rather than the perception of teacher support, may encourage adolescents to seek their assistance.
The contrasting effects of teacher support in cases of cyber versus traditional victimization may reflect differences in how adolescents perceive the visibility, controllability, and social consequences of these experiences. Cyberbullying often occurs in private digital spaces, leaving adolescents feeling isolated. In these situations, teachers may serve as a trusted adult presence who can validate the student’s experience and offer guidance. In contrast, traditional bullying is often more visible to peers and educators, but adolescents may view adult intervention as ineffective, stigmatizing, or even escalating the problem (Nagar and Talwar 2023). These findings raise important theoretical questions about adolescent help-seeking behaviors and the situational contexts in which social support is perceived as effective.

4.1. Strengths and Implications

There were a number of strengths to our study. This study is among the very few to examine the effects of cyberbullying victimization controlling for traditional forms of peer victimization, using a globally representative sample. Previous research indicates that although about 90% of children and adolescents live in nations classified as low- and middle-income countries (Zhou et al. 2020), most samples in psychological research reflect the cultural context of Western societies (Arnett 2008) such as the United States, accounting for only 5% of the world’s population (Abo-Zena et al. 2022). Such exclusionary focus on the privileged groups and assuming the universality of the results neglect the experiences of almost 89% of the global population in psychological science (Thalmayer et al. 2021). Consequently, the results of studies like ours allow for a better understanding of adolescent development and are crucial for shaping the development of social policies and practices. Importantly, the inclusion of non-Western populations also enables a more nuanced understanding of how school-related distress and teacher support function across diverse educational and cultural contexts. School systems differ widely in terms of structure, resources, and teacher–student dynamics. For example, in some countries, teachers are viewed as authoritative figures primarily responsible for academic instruction, while in others they are expected to provide socioemotional support and mentorship (Chen et al. 2019; Nelson et al. 2001). Cultural norms may also shape whether adolescents feel comfortable seeking support from teachers, particularly when issues like bullying or cyber harassment are involved (Lin et al. 2025). These contextual factors may partially explain the variation in how effective teacher support is perceived and how it interacts with students’ experiences of peer victimization. Therefore, findings from our globally representative sample are not only statistically generalizable but also theoretically valuable, helping illuminate the role of culture in adolescent help-seeking and school mental health systems.
Another notable strength of this study was utilizing structural equation modeling to test the hypotheses. Although regression analyses are typically simple to interpret, there have been concerns regarding the reliability of the variables and the results (Sechrest 1963; Shear and Zumbo 2013). In instances where predictors and/or outcome variables are subject to random errors (which is anticipated in any psychological research), regression parameter estimates may exhibit biases (Westfall and Yarkoni 2016), and the correlations may be attenuated by measurement errors (Feng and Hancock 2022). In contrast, SEM accounts for measurement error without relying on the assumption of perfect measurement or the need for manual attenuation correction (Feng and Hancock 2022). In addition, inflated type I error rates may occur in large samples when using linear regression. Latent variable approaches, such as structural equation modeling, are the most effective method for addressing this issue, as they can maintain type I error rates in large samples (Axt et al. 2024).
Furthermore, we treated school-related distress as a latent factor in this study. This enabled us to measure this theoretical construct with multiple indicators to reduce measurement error (Judd et al. 2014). Using a latent factor also allowed us to create a structural regression (i.e., hybrid) model. Unlike a traditional path model, the hybrid model includes both observed variables and latent factors, divorcing the estimation of measurement error in the indicators from direct or indirect effects among the factors. This ability is a notable advantage of a hybrid model over a path model, as it effectively controls for measurement error, leading to more accurate estimates (Kline 2005).
Moreover, statistically demonstrating the significant main effect of cybervictimization above and beyond traditional forms of victimization has important practical implications. As technology continues to become more accessible in both homes and educational settings, these findings underscore the need to address cyberbullying as a distinct phenomenon posing unique challenges for adolescents and requiring targeted intervention strategies. Enhancing adolescents’ interpersonal skills for online interactions and providing resources to schools to address cyberbullying victimization may be among the most effective prevention strategies. Furthermore, there is a need for school staff to not only offer support, but to demonstrate consistent and competent intervention practices across bullying contexts. Teacher training programs should address both the emotional and behavioral dimensions of student support, equipping educators to respond confidently and effectively to both online and offline forms of peer aggression.

4.2. Limitations and Future Directions

Although this study is among the very few to examine the effects of cyberbullying above and beyond the traditional forms of peer victimization using an internationally representative sample, it is not exempt from some limitations. To begin, it is important to acknowledge that the interpretation of the results of the current study is constrained by limitations inherent in using a secondary dataset. For example, the SSES survey relied exclusively on adolescents’ self-reported data for all the variables, possibly introducing common method bias. Self-perceptions are susceptible to bias and inaccuracies, including tendencies toward socially desirable responses and potential underreporting of bullying experiences due to feelings of shame (Branson and Cornell 2009). Although prior research demonstrates that the results of self-reports, peer-reports, and teacher ratings are somewhat consistent (Schwartz et al. 2015), school disciplinary actions are more likely to follow when peer and teacher nominations are assessed (Cornell and Brockenbrough 2013). Additionally, another benefit of identifying students based on peer nomination is that it reduces the bias introduced by some adolescents’ tendency to report negatively about themselves, such as indicating victimization experiences (Rigby 2001). Given the complexity of peer victimization and cyberbullying and adolescents’ reluctance to acknowledge involvement as either aggressors or victims, future research should consider including both self-reports and collateral reports (e.g., peer nomination) to make the findings more compelling (Branson and Cornell 2009). This could be achieved by obtaining the predictor measure(s) from one individual and the criterion measure(s) from another. This procedure can eliminate/reduce social desirability tendencies and the effects of consistency motifs (Podsakoff et al. 2012).
Moreover, the cross-sectional nature of the data should be considered a limitation as it does not allow for any inferences of causality. Future longitudinal investigations are needed to examine the long-term impact of such experiences.
It is also important to note that this study primarily focused on victims, not perpetrators.
Olweus (1993) distinguished between victims who exhibit provocative and aggressive behaviors and those who display more passive and submissive behaviors. The term “bully-victims” describes individuals who both experience bullying and engage in bullying themselves. These individuals often exhibit reactive aggression, responding to their own victimization by targeting others (Olweus 1993; Perren and Alsaker 2006). Although both bullies and bully-victims display aggressive behaviors, bully-victims are often socially rejected and typically engage in reactive aggression (Boulton and Smith 1994; Pellegrini 1998; Perry et al. 1988). It would be fruitful for future studies to examine the perpetrators of cyberbullying and whether these individuals share characteristics with bully-victims. It may be that bully-victims with a history of experiencing traditional victimization would leverage the anonymity that information and communication technology affords to retaliate and engage in cyberbullying victimization. Gaining insight into these nuances would further support the development of targeted interventions.
Another limitation that should be discussed is how teacher support was operationalized in the survey, which leaves room for further investigation. The survey measured this variable as a broad construct. As demonstrated by House (1983) and Tardy (1985), social support can manifest in various forms, including caring or emotional support (e.g., trust, love, and empathy), informational support (e.g., giving advice), instrumental support (e.g., offering time or resources), and appraisal support (e.g., providing constructive feedback). Moreover, the perception of teacher support may differ as a function of ethnicity (Demaray and Malecki 2003; Keefe et al. 1979). Hence, future studies should expand the findings by examining the frequency and quality of teacher support and exploring which forms of social support have the most significant impact on mitigating the effects of cyberbullying victimization on adolescents’ adjustment. Furthermore, the present study examined patterns across all participating countries. Future research should consider the use of multilevel modeling to explore potential cross-country differences. This approach would make it possible to assess whether the proposed model demonstrates acceptable fit for each country and would identify country-specific variations in the strength or direction of key associations. This could be particularly valuable for understanding how cultural, educational, and policy contexts may influence the relationships between the variables.

5. Conclusions

Ultimately, the results of the current study using a large globally representative sample of adolescents emphasize the critical role that teachers play in mitigating school-related distress among cyberbullied adolescents. From a social ecological perspective, schools and educational settings are central to understanding adolescent development. By underscoring the critical role of teacher support, this study emphasizes the importance of fostering supportive teacher–student relationships as a key factor in reducing school-related distress, particularly in the context of cyberbullying victimization. The findings also highlight the protective role teachers can play in creating a positive and supportive school environment that supports students in navigating the social and emotional challenges they face. The findings provide a foundation for future research to further explore the complexities of cyberbullying victimization and its unique effects on adolescents’ well-being.

Author Contributions

Conceptualization, S.S.M. and J.S.; Methodology, S.S.M. and J.S.; Software, S.S.M. and J.S.; Formal analysis, S.S.M., J.S. and H.L.; Writing—original draft, S.S.M.; Writing—review and editing, H.L.; Supervision, J.S.; Funding acquisition, S.S.M. All authors have read and agreed to the published version of the manuscript.

Funding

Research funding for this project was provided, in part, by the Graduate Research and Creative Activity (GRACA) award from the University of Nebraska’s Office of Research and Creative Activity. In addition, this material is based upon work supported by the National Science Foundation Graduate Research Program under Grant Number 2024375659. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The study analyzed self-reported data from the Study on Social and Emotional Skills (SSES), conducted by the Organization for Economic Cooperation and Development (OECD). This dataset is freely accessible at: https://www.oecd.org/en/data/datasets/SSES-Round-1-Database.html. Alternatively, the data can be requested by contacting the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Socsci 14 00559 g001
Figure 1. The conceptual model depicting the hypotheses.
Figure 1. The conceptual model depicting the hypotheses.
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Figure 2. The moderating role of teacher support on the association between cyberbullying victimization and school-related distress.
Figure 2. The moderating role of teacher support on the association between cyberbullying victimization and school-related distress.
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Figure 3. The hybrid model depicting the results with * indicating significant associations.
Figure 3. The hybrid model depicting the results with * indicating significant associations.
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Table 1. The breakdown of participants by country, gender, and age.
Table 1. The breakdown of participants by country, gender, and age.
CountryGirlsBoysTotalMageSDage
Canada10201019203915.400.60
USA15421346288815.410.51
Colombia34473302674915.400.62
Finland12491083233215.300.50
Russia16591716337515.490.52
Turkey18181287310515.450.50
South Korea16671579324615.700.45
Portugal831746157715.500.52
China17451827357215.300.50
Total14,97813,90528,88315.430.52
Table 2. Correlations among the variables of the study.
Table 2. Correlations among the variables of the study.
Variable12345678
1. I feel like an outsider (or left out) at school-
2. I feel awkward and out of place in my school0.50 **-
3. I feel lonely at school0.56 **0.51 **-
4. Traditional peer victimization0.30 **0.27 **0.32 **-
5. Cyberbullying victimization0.19 **0.16 **0.18 **0.42 **-
6. Teacher support−0.16 **−0.19 **−0.18 **−0.13 **−0.13 **-
7. Mean (M)1.751.851.731.391.223.26-
8. Standard Deviation (SD)0.790.820.820.580.430.77 -
Note. N = 28,883. ** p < 0.01.
Table 3. Results of the structural regression model.
Table 3. Results of the structural regression model.
VariablebβS.E.p
Traditional peer victimization0.410.410.01<0.001
Cyberbullying victimization0.170.130.01<0.001
Teacher support−0.14−0.180.01<0.001
Interaction0.040.120.01<0.001
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McVay, S.S.; Santo, J.; Lydiatt, H. The Impact of Cyberbullying Victimization on Adolescents’ School-Related Distress Across Nine Countries: Examining the Mitigating Role of Teacher Support. Soc. Sci. 2025, 14, 559. https://doi.org/10.3390/socsci14090559

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McVay SS, Santo J, Lydiatt H. The Impact of Cyberbullying Victimization on Adolescents’ School-Related Distress Across Nine Countries: Examining the Mitigating Role of Teacher Support. Social Sciences. 2025; 14(9):559. https://doi.org/10.3390/socsci14090559

Chicago/Turabian Style

McVay, Shaghayegh Sheri, Jonathan Santo, and Hannah Lydiatt. 2025. "The Impact of Cyberbullying Victimization on Adolescents’ School-Related Distress Across Nine Countries: Examining the Mitigating Role of Teacher Support" Social Sciences 14, no. 9: 559. https://doi.org/10.3390/socsci14090559

APA Style

McVay, S. S., Santo, J., & Lydiatt, H. (2025). The Impact of Cyberbullying Victimization on Adolescents’ School-Related Distress Across Nine Countries: Examining the Mitigating Role of Teacher Support. Social Sciences, 14(9), 559. https://doi.org/10.3390/socsci14090559

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