Cybervictimization in Chilean Schools: An Intersectional Multilevel Study

Even though research has established the prevalence and predicting factors of cybervictimization in the school context, studies have not considered their nested nature, which might lead to differences within and between schools. Research has only just begun to recognize the intersectionality of various cultural labels and marginalized identities, but most studies have been qualitative. Using a quantitative approach that considers the nested nature of the data and inspired by an intersectional theoretical framework, we performed a multilevel analysis of cybervictimization in a sample of 34,959 students enrolled in fifth to eighth grade in 410 Chilean schools with low socioeconomic status. Findings showed that 14.3% of students reported some type of cybervictimization during the last month. Level 1 (individual) findings showed that being male, over age, of an ethnic origin, and reporting a negative perception of school climate significantly contributed to self-reported cybervictimization. Discrimination due to sexual orientation was the most critical variable at both the individual (b = 1.39, p < 0.001) and school (b = 1.26, p < 0.001) levels. Likewise, this factor interacted significantly with all variables at the individual level. We discuss the implications of this study, among them that school-related cybervictimization seems to be strongly associated with homophobia and homophobic school environments in which gender-non-conforming students feel discriminated. This calls for the incorporation of an intersectional and inclusive approach to policies and practices for preventing and diminishing cyberbullying and its different expressions, promoting heterogeneity as a virtue in schools.

Introduction

In recent decades, various agents in both the educational and social spheres have manifested their concern over a new kind of bullying associated with information and communications technology (ICT) known as cyberbullying (Slonje and Smith, 2008; Schneider et al., 2012; Smith, 2012; Kowalski and Limber, 2013; Kowalski et al., 2014; Sampasa-Kanyinga et al., 2014; Pham and Adesman, 2015). According to Bauman (2007), the term cyberbullying was first coined in 2004 by Canadian educator Bill Belsey, who posited that:

Cyberbullying involves the use of information and communication technologies such as e-mail, cell phone and pager text messages, instant messaging, defamatory personal Websites, and defamatory online personal polling Websites, to support deliberate, repeated, and hostile behavior by an individual or group, that is intended to harm others. (p. 3)

More recently, authors have described it as violent, deliberate, and repetitive behavior that involves an uneven power dynamic, wherein an individual or group makes use of ICT to harm or harass a victim (Slonje and Smith, 2008; Hinduja and Patchin, 2011; Pham and Adesman, 2015), keeping the fundamental arguments proposed by Belsey while incorporating the power difference between the bully or bullies and victim.

Although there is still debate as to whether cyberbullying is a phenomenon distinguishable from traditional school bullying, a wide theoretical framework defines cyberbullying as a separate phenomenon with its own characteristics (Del Rey et al., 2015). Five main differences distinguish cyberbullying from traditional bullying: (a) the harassment does not stop once the victim leaves the school (Slonje and Smith, 2008), because they keep receiving hostile messages or are excluded from virtual interaction out of the school; (b) the nature of social media and technologies allows for wider and anonymous audiences (Kowalski et al., 2014); (c) the invisibility or anonymity that cyberbullies may achieve (Sampasa-Kanyinga et al., 2014); (d) the lack of face-to-face contact reduces the possibility of empathy from the bully or bullies (Bauman, 2007); and (e) factors such as the age differences produced as a consequence of grade retention and other power differences may acquire particular characteristics that are different than traditional bullying (Del Rey et al., 2015). For instance, a single picture or humiliating post can be repetitive because its content can remain online and be shared or commented on endlessly, allowing new cyberbullies to join the interaction (Smith, 2012).

Ortega-Ruiz et al. (2016) posited that bullying and cyberbullying are group phenomena that should not be understood merely as aggressive behavior, asserting that various roles affect their development, such as victim, aggressor, double role, and bystander. The present research, however, focused on cybervictimization (Ortega-Ruiz et al., 2016) because of the negative impact that these behaviors may have on the affected students (Kowalski and Limber, 2013).

Numerous studies have confirmed significant negative correlations between cybervictimization and academic, social, and emotional development (Schneider et al., 2012; Kowalski and Limber, 2013; Sampasa-Kanyinga et al., 2014). A meta-analysis performed by Van Geel et al. (2014), which reviewed 36 studies, showed that cybervictimization could be a greater predictor of suicidal ideation, compared to traditional bullying. In Chile, Varela et al. (2019) found a negative association between cybervictimization and life satisfaction in adolescents. They argued that these results show that cybervictimization can negatively affect the victim’s perceived support from and sense of belonging to their community. This is relevant to the Chilean context because, according to the Ministry of Education’s Center of Studies, data from the Superintendence of Education show that reports of cyberbullying have increased 64% between 2017 and 2018 (Guevara et al., 2018).

Despite these numbers, progress on cybervictimization regulations in the Chilean school context has been slow. At the conclusion of this study, a proposed bill to prevent and punish bullying or virtual school harassment was still in process (Cámara de Diputados y Diputadas de Chile, 2020). This bill would require school staff to generate strategies and action protocols to prevent this type of aggression, proposing to address it with formative and preventive logics, but also defining clear channels and protocols to report and punish this type of behavior. This regulation also proposes that mandatory training be required on the proper use of technological means and training on online violence for students and parents.

Although myriad studies have contributed to raising awareness of the impact of cybervictimization, most of them have only studied isolated predictors—such as gender, race, and sexual orientation (Kowalski et al., 2014; Varela et al., 2014)—without acknowledging the interaction between these factors and their capacity to estimate the occurrence of cybervictimization. For example, Varela et al. (2014) studied cyberbullying in Santiago de Chile in the student population and determined its prevalence based on the criteria of gender, grade, and the type of school funding (public or private), but only analyzed these factors separately, without considering the importance of context variables and interactions present in the cybervictimization.

One of the most researched variables in the field of cyberbullying and cybervictimization is gender. Results have been dissimilar and inconsistent (Pham and Adesman, 2015). For example, Sorrentino et al. (2019) analyzed cyberbullying in eight European countries and concluded that boys were more likely to be cyberbullies than girls in all countries involved in the study. However, they found no significant gender differences in cybervictimization. Findings in Colombia conclude that being male is a risk factor for becoming either a cyberbully or a cybervictim through both the internet and mobile phones (Morales Portilla and Rueda Forero, 2018). In Chile, findings from the first national survey of polyvictimization, published by MINEDUC (Guevara et al., 2018), showed that 27% of girls reported being cybervictims, as opposed to 14% of boys. Varela et al. (2014) found no significant gender differences in cybervictimization in Chile.

Another risk factor that has been studied is sexual orientation. The literature addressing students who are lesbian, gay, bisexual, trans, or queer (LGBTQ) suggests that bullying is a serious issue to any student who is part of the LGBTQ or to those who are perceived as such (Astor and Benbenishty, 2005; Fundación Todo Mejora, 2016). Compared to their gender-conforming peers; or those who identify as heterosexual, LGBTQ students are more likely to experience depression, suicidal ideation or attempts, low self-esteem, social isolation, worse academic results (Abreu and Kenny, 2018) and in general, high levels of cybervictimization (Hinduja and Patchin, 2011). In Spain, a study of cyberbullying among LGBTQ students by Benítez (2016) showed similar results, noting that 55% of LGBT students had witnessed cyberattacks to other peers who identify as part of the LGBTQ community, and 72% could identify their cyberbullies in their school.

Age is another factor included in research on cybervictimization because of the widespread access to electronic devices among students (Lenhart, 2015). The literature shows that cybervictimization reaches its highest levels around the middle years of school (Kowalski et al., 2014), rising importantly in fifth grade and peaking in eighth grade (Hinduja and Patchin, 2008)—that is, between ages 10 and 13. In contrast, Garaigordobil’s (2015) study in the Basque region found no significant differences in cybervictimization between students aged 12–18.

Nevertheless, Kowalski et al. (2019) highlighted differences in the use of ICT and the prevalence of cyberbullying, both between different age groups (childhood and adulthood) and in similar age groups (sixth and eighth grade). The authors argued that children using ICT are increasingly younger, thus implying that cyberbullying and cybervictimization may also start occurring at younger ages.

A less studied variable is the ethnic origin of the students. The main difficulty of its study is the dissimilar interpretation of the concept across cultures. This is exemplified by a study by Edwards et al. (2016), who used the concept of race and ethnicity to examine differences in cyberbullying among White, Black, Hispanic, and Asian students, not distinguishing race from ethnicity. The study found that youth of color were less likely to experience cyberbullying than White youth while reporting the same levels of suicidal ideation and attempts when cybervictimization took place.

Meanwhile, studies in Chile have defined ethnicity as belonging or not belonging to any of its ethnic groups. Gálvez and Vera (2015) argued that it is important to study ethnicity in the Chilean context, where 11 ethnic groups coexist in the educational system. One of the most recent studies of this matter (Pinto-Cortéz et al., 2019), that included Aymara students, one of the 11 ethnic groups in Chile, showed that cybervictimization among students of Aymara ethnicity reached 18.7%, which was lower than in non-Aymara students (21.3%). The authors did not provide interpretations regarding why non-ethnic background students reported higher levels of victimization than students from ethnic backgrounds. In the United States, Konold et al. (2017) found associations between positive school climate and lower reported victimization by Native American students, which might explain the differences in the prevalence rates between ethnic and non-ethnic groups. In Chile, however, no studies have included school climate or other school-related variables.

Last, socioeconomic status (SES) has been identified as making an important contribution to cyberbullying (Kowalski et al., 2014). Studies have reported positive correlations between SES and cyberbullying (Wang et al., 2009), which might be associated with greater access to ICT according to income. However, studies in Latin American countries have not found significant differences by SES (Varela et al., 2014; Garaigordobil, 2015; Morales Portilla and Rueda Forero, 2018).

Evidence suggests that income inequality influences school violence (Due et al., 2009), which may hold true to cybervictimization as well. Contreras et al. (2015) conducted a study in 52 countries, including Chile, to determine whether income inequality was associated with differences in levels of school violence. Indeed, they found that income inequality predicted higher rates of violence at the school level, whereas at the individual level, consistent significant results were not obtained.

This research examined the cybervictimization experiences of students in schools. To this end, we identified the need to employ an intersectionality approach, understood as theoretical and methodological concern for measuring multiple marginalized identities and assessing their impact (Wadsworth et al., 2020) on cyberbullying. As a sociocritical approach born in the anti-racist feminist movements and first postulated in 1991 by Kimberlé Crenshaw (Sánchez Melero and Gil Jaurena, 2015), intersectionality has a fundamental premise that people have diverse characteristics that are lived as identity traits (for example, gender, race, SES, etc.) and that intersect to create substantially different social experiences. In this framework, each predictor is considered a marker of identity, carrying the potential of being a form of oppression, because the social system and culture determine a form of normality imposed by a dominant group. In this sense, gender-non-conforming students who are also from ethnic and low-SES backgrounds might be more at risk than White middle-to-upper-class students (Superintendencia de Educación de Chile, 2018). Quantitatively, the intersectionality approach calls for the analyses of interaction effects (Bowleg, 2008).

From this perspective, discrimination cannot be understood in terms of each characteristic separately, because the interaction between different targets of discrimination generates new forms of discrimination (Sánchez Melero and Gil Jaurena, 2015). This becomes particularly relevant if we consider cybervictimization as a form of discrimination, understanding that the different categories that apply to each subject become the object of cybervictimization. In this regard and following the point made by Butler (Big Think, 2011), a feminist theorist and activist, “there are informal practices, such as bullying, that try to keep us in our generated place” (2m12s). In other words, cyberbullying is a social practice in which some subjects discriminate against others based on the characteristics that distinguish them from the normal order, such as gender, race, or sexual orientation, among others.

Despite theoretical advances regarding the intersectional approach, there is scarce research applying it to cybervictimization (Crooks, 2017). As Crooks (2017) posed, the importance of including an intersectional approach lies in the possibility of understanding how different forms of social discrimination are manifested through cyberbullying. She stressed that it is necessary to consider this intersection “if we are to move beyond the dominant frameworks on bullying, which are very often based on racist, heterosexist, and classist logics” (p. 79). Only one quantitative study developed by Stoll and Block (2015) was found in the literature addressing cyberbullying with an intersectional approach. This study concluded that the relationship between gender and cybervictimization was significantly stronger among White students than students of color. Considering these premises and partial evidence, we hypothesized that the interaction of social markers is related to increased levels of cybervictimization.

The intersections between different identity markers is a complex phenomenon that must take into account the social contexts of oppressed and privileged groups (Rouhani, 2014). Therefore, along with an intersectional approach, in this study we posited that the inconsistencies and contradictions in the cybervictimization literature may be due to the lack of contextual analysis that considers students’ individual conditions and simultaneously includes school-related experiences and the nested character of students’ characteristics in each school, allowing for the study of the effect of the socioeconomic structure of schools (Andréu Abela, 2011). From this perspective, research on cybervictimization has tended to ignore the nested nature of the school experience, which can generate differences both within and between schools. Therefore, in this study we used Benbenishty et al. (2018) ecological model of school violence and bullying, which places the characteristics of schools at the center, rather than the individual traits and characteristics of students (López et al., 2020). This approach postulates that the internal context of the school moderates individual external influences (gender, ethnicity, sexual orientation, etc.) and helps shape students’ experiences, perceptions, emotions, and behaviors. For example, a study on bullying by Goodenow et al. (2006) concluded that sexual minorities who attend large urban schools with low SES and who integrate a significant number of students from this minority exhibit a lower risk of school victimization than their peers who attend small, not sexually diverse schools. On the other hand, research by Benbenishty et al. (2018) emphasized the importance of the composition of school beyond students’ individual demographics. Based on this evidence, we hypothesized that individual variables of gender, age, sexual orientation, SES, and ethnicity, when compounded at the school level, have significant relationships with the occurrence of cybervictimization. We also included other school context variables known to affect school violence and peer victimization, such as school size (Wolke et al., 2001; Ferris and West, 2004; Benbenishty et al., 2018) and type (Varela et al., 2014).

Regarding school-related variables that depict students’ subjective experiences in school, school climate, a concept that addresses the quality and character of school life (Cohen and Moffitt, 2009), has been studied as a context variable and has shown significant effects on peer victimization (Cornell et al., 2015; López et al., 2018). There is evidence of negative associations between bullying and school climate (Nickerson et al., 2014) and between cyberbullying and school climate (Wang et al., 2019).

In summary, this research sought to address gaps in the study of cybervictimization through an intersectional and school-as-context approach, using a multilevel analysis, with a large sample of Chilean students in the second cycle of elementary school. The objective was to identify predictors of cybervictimization at the individual and school levels among students from fifth to eighth grade in low SES schools in Chile. As described by Bell et al. (2019), there has been a rise in the use of multilevel models to describe and analyze complex interactions among social characteristics. In doing so, researchers have focused on the intersecting deprivations that result from different combinations of social markers (age, sex, ethnicity, and SES) on the one hand, and how these deprivations are the result of interlocking systems of discrimination, marginalization, and exclusion on the other. The use of intersectional multilevel models that incorporate both individual characteristics and societal factors to see if they contribute to differences in outcomes has been encouraged (Spierings, 2012; Rouhani, 2014).

Materials and Methods

Participants

The participants in this study were students from schools in the “Habilidades para la Vida” (Skills for Life) program (hereinafter HPV) of the Junta Nacional de Auxílio Escolar y Becas or JUNAEB (National Board of School Aid and Scholarships), which promotes mental health among students in low-SES schools in Chile. The sample consisted of 34,959 students from fifth to eighth grade (M = 12.3 years old, SD = 1.44). All cases in which any questions corresponding to the variables analyzed were not validly answered were excluded from the sample, as were all schools whose survey responses did not reach 50% of the total enrollment. The sampling was of a census nature because the questionnaire was answered by all students attending on the day of its application, 48.9% being girls and 51.1% boys. The sample was intentional because the participants were chosen in a targeted manner from the lowest SES schools in the country, given that it was applied in the HPV framework.

Measures

A secondary analysis was performed using the “Convivencia en mi escuela” (Coexistence in my School) survey conducted in 2016; its psychometric properties were reported by López et al. (2016). The survey is part of the school climate and school violence monitoring system of the Skills for Life Programa (Programa de Habilidades para la Vida) of JUNAEB and was developed by the corresponding author and her research team, based on the systems designed and administered in Israel and California by Rami Benbenishty and Ron Avi Astor (López et al., 2013). The survey consists of seven scales that collect information on school climate, student victimization, victimization of school personnel, teacher bullying, dangerousness at school, contribution to school, and life satisfaction (Villalobos-Parada et al., 2016).

Dependent Variables

Cybervictimization

The School Victimization Scale developed by Furlong et al. (2005), modified for the Israeli context by Astor and Benbenishty (2005), and adapted for use in Chile by López et al. (2014) was used. It contains 27 questions that evaluate the frequency of violence at school during the previous month perceived by classmates. It considers physical (α = 0.71), verbal (α = 0.73), social (α = 0.75), sexual (α = 0.66), and cyber (α = 0.79) victimization. In this study, we used the cyber dimension as the dependent variable. It is formed from three items using a shared stem: “Did it happen to you in the last month?” The items are: (1) A student published a picture of you on the internet or on his/her mobile phone to hurt you; (2) You received insulting or mocking messages on the internet or on your mobile phone; and (3) You received threats from another student on the internet or on your mobile phone. The responses are given in a Likert format with three options relative to the last month: never, once or twice, and three or more times, plus the option if “I don’t know/don’t want to answer.” This variable was calculated as the sum of the scores for the three items (0 = never, 1 = 1 or 2 times, 2 = 3 or more times; M = 0.37, SD = 1.06, range = 0–6). Students who answered “I don’t know/don’t want to answer” and invalid data were excluded.

Independent Variables: Individual Characteristics

Gender

This information was obtained from the official records of the participating schools (0 = male, 50.9%; 1 = female, 49.1%).

Discrimination Based on Perceived Sexual Orientation

Students reported whether they felt discriminated against by others because of their perceived sexual orientation (0 = no, 94%; 1 = yes, 6%).

Ethnic Background

The students reported whether they belonged to any ethnic group recognized by the law of Chile. This was coded as a dichotomous (1 = yes, 19.5%; 0 = no, 80.5%).

Socioeconomic Status

The Sistema Nacional de Asignación con Equidad (National System of Allocation with Equity, SINAE by its initials in Spanish), developed by JUNAEB from the official data that each school submits to the Ministerio de Educación (Ministry of Education) was used. The SINAE is built based on JUNAEB’s vulnerability surveys, the health affiliation system (government or private), membership in a program of the SENAME network, membership in the Chile Solidario program or the Ingreso Ético Familiar (Ethical Family Income) program, and information from the Registro Civil (Civil Registry) and the Ministry of Education’s registration offices. The variable outlines the degree of socioeconomic vulnerability of each student. High scores represent a higher socioeconomic level (vulnerable SES = 80.9%, non-vulnerable SES = 19.1%).

Over Age

This variable identified students who were 2 or more years older than the expected age in a specific grade. A dichotomous variable was created (1 = over age, 16.2%; 0 = otherwise, 83.8%).

School Climate

This variable reflected students’ individual perception of school climate. This variable was formulated through modifications to the California School Climate and Safety Survey instrument (Furlong et al., 2005). Various scholars have modified this scale (Astor and Benbenishty, 2005; López et al., 2018). The instrument contains 17 items with a score of 1 to 4 on a Likert scale. It consists of four dimensions; feeling safe at school composed by three items (one sample item is “I feel safe in my school”), teacher support composed of four items (one sample item is “My relationship with teachers is close and good”), student participation composed of five items (one sample item is “The adults and teachers of this school encourage student participation in important decisions”), and school policies against violence composed of five items (one sample item is “Students in my school know well the rules against sexual harassment”). An additive index of the items was computed individually.

Independent Variables: School Characteristics

School Size

School size was coded as a continuous variable that represents the number of students enrolled in that school in the application year. This was obtained from official data from the Chilean Ministry of Education.

Type of School

This variable categorized schools according to their administrative unit (1 = municipal corporation, 32.2%, 2 = Department of Municipal Education Administration, 54.8%, 3 = private subsidized, 13%). While 1 and 2 refer to publicly funded schools run by local municipalities (through a municipal corporation, or through a department of education), code 3 refers to private-subsidized schools, which in Chile are administered by private parties but funded through public funds.

Aggregated Variables

To create these variables, individual characteristics were aggregated at the school level by calculating the percentage of students reporting each variable at the school. These variables were gender, discrimination based on perceived sexual orientation, ethnicity, SES, and school climate.

Data Analysis

A two-level hierarchical analysis was performed using Stata 13 software, considering both individual (Level 1) and school (Level 2) factors. To test the intersectionality hypothesis, interactions between variables at the individual and school levels were also included. Three models were constructed for this purpose, with the level of cybervictimization as the dependent variable. Individual factors included gender, socioeconomic level, over age, level (grade), ethnicity, sexual orientation discrimination, and school climate. School-level variables were obtained by averaging the individual factors reported, which also included school size and type. Model 1 analyzed the effects of the variables at the individual level. Model 2 included the school-level variables. Finally, Model 3 included the interaction factors between the individual-level variables. Figure 1 summarizes the expected directions of the contributions of the individual and school-level variables to cybervictimization.

FIGURE 1

Figure 1. Theoretical model.

Ethical Considerations

For this secondary analysis of data, a document was signed by the Centro de Investigación para la Educación Inclusiva de la Pontificia Universidad Católica de Valparaíso, in which the research team committed to using the database exclusively for academic purposes and potential publication of the document produced in this process. The data collection work complied with the ethical safeguards of research with human beings, authorized by the bioethics committee of the affiliated in the framework of the FONDEF IT 14i10119 project. Informed consent was requested from parents at the beginning of the school year by the HPV program, and informed assent was obtained from students before they answered the survey. The questionnaire was answered by all students present on the day of the application through an online survey. Participants’ confidentiality was safeguarded at all stages of data processing. Under a collaborative agreement, researchers received a decoded database from the National Coordination office of the HPV program.

Results

Description of Cybervictimization Items and Association Between Cybervictimization and Study Variables

Table 1 shows the frequencies of cybervictimization at the individual level, grouped according to the different forms of cybervictimization and by both total sample and variable. The most reported type of cybervictimization was receiving insulting or mocking messages over the internet or by cell phone, reported by 10.5% of students to have occurred at least once in the last month. Boys reported more cybervictimization than girls. In addition, cybervictimization was reported more frequently in fifth grade, decreasing progressively as the grade increased. As for SES, greater cybervictimization was reported at lower levels. Students belonging to any ethnic minority and those discriminated against due to sexual orientation reported higher levels of cyber victimizations than those who did not. People who were over age also reported being cyberbullied more than those who were not.

TABLE 1

Table 1. Percentage of students who report cybervictimization grouped by variable.

Because the objective of this study was to analyze cybervictimization at the individual and school levels, Table 2 shows the descriptive results and correlations of the individual factors and the correlations of the school factors. At the individual level, cybervictimization presented highly significant correlations (p < 0.01) with all variables analyzed; at the school level, only the school climate variable was not significant (p > 0.05). At both levels, discrimination by sexual orientation presented the highest correlation with the dependent variable (0.35 at the individual level, 0.59 at the school level).

TABLE 2

Table 2. Descriptive statistics at the individual level and correlations at the individual and school level.

Multilevel Linear Regression Analysis Predicting Cybervictimization

Table 3 shows the results of the two-level hierarchical analysis, considering the null model, individual factors (Model 1), school factors (Model 2), and interactions between the individual variables (Model 3). In Model 1, all analyzed variables were statistically significant. The results showed that female gender (b = −0.03, p < 0.01), higher SES (b = −0.02, p < 0.001), better school climate (b = −0.17, p < 0.001), and higher grade (b = −0.05, p < 0.001) predicted lower levels of cybervictimization. On the other hand, being over age (b = 0.13, p < 0.001), belonging to an ethnic group (b = 0.10, p < 0.001), and experiencing discrimination because of sexual orientation (b = 1.39, p < 0.001) predicted a higher frequency of cyberbullying.

TABLE 3

Table 3. Multilevel model results predicting peer cybervictimization from individual and school level variables using full maximum likelihood estimation.

In Model 2, school-level variables were included. The variables at the individual level maintained their significance; at the school level, only the proportion of students discriminated against due to their sexual orientation (b = 1.38, p < 0.01) was significant. A higher percentage of students reporting feeling discriminated against predicted higher levels of cybervictimization.

In Model 3, by including interactions of variables at the individual level, some individual variables lost their significance, with only over age (b = 0.23, p < 0.05) and sexual orientation discrimination (b = 3.12, p < 0.001) remaining significant. At the school level, significance was not affected. The interactions that were significant (p < 0.05) were gender and overage (b = 0.10, p < 0.01), gender and sexual orientation discrimination (b = −0.34, p < 0.01), gender and ethnic origin (b = −0.06, p < 0.05), over age and sexual orientation discrimination (b = 0.43, p < 0.001), SES and sexual orientation discrimination (b = −0.06, p < 0.001), ethnic group and SES (b = −0.02, p < 0.05), and ethnic group and grade level (b = 0.38, p < 0.01). These results indicate that being female and over age could predict greater cybervictimization, as could being female and: belonging to an ethnic group, having low SES, belonging to an ethnic group, and being in a higher grade. Variables intersected with discrimination based on sexual orientation that predicted increased cybervictimization were male gender, over age, and low SES.

Table 3 also shows the variance components partition of the dependent variable at the student and school levels and the explained variance compared to the null model. Even though the variables explained a higher proportion of the school-level variance, the differences found in cybervictimization were concentrated at the student level, with a relatively low variance at the school level; this could be explained by the fact that schools that participated in the HPV program shared similar characteristics.

Discussion and Conclusion

Cybervictimization has become a relevant problem in recent years with the rise of ICT, especially among school-age children and adolescents. Knowing the individual and school conditions in which cybervictimization thrives is an important step toward addressing this phenomenon by taking measures to reduce its occurrence and impact on the most vulnerable groups.

The results suggest that all variables analyzed at the individual level (gender, sexual orientation discrimination, ethnic group, over age, SES, and school climate) could be considered predictors of cybervictimization in the studied sample. Thus, being male would be a risk factor for cybervictimization, as would having a low-SES background or belonging to an ethnic group. However, of all variables in the analysis, findings suggest that discrimination due to perceived sexual orientation was the most crucial factor in all three models. In other words, not only is students’ individual self-reports of being victimized due to sexual orientation a predictor of cybervictimization (moderl 1), but also a higher percentage of students at the school-level reporting feeling discriminated against due to their sexual discrimination predicted higher levels of cybervictimization (model 3). Even when controlling for the possible interactions between the variables, the specific contribution of sexual orientation-based discrimination remained significant (model 3). These results provide evidence of an important relationship between cybervictimization and homophobia, because feeling discriminated against based on sexual orientation can be considered a consequence of a homophobic environment (Furlong et al., 2005) with respect to those who do not conform to heteronormative expectations. These results are consistent with much of the international literature, which also shows high rates of cyberbullying among LGBT + students (Astor and Benbenishty, 2005; Hinduja and Patchin, 2011; Abreu and Kenny, 2018). The importance of undertaking a nested approach in researching cyberbullying through multilevel studies, lies in the possibility of understanding and providing evidence of how context-in this case, a larger number of students who report feelings of being discriminated because of their sexual orientation, which is indicative of a homophobic school context-shapes the forms and frequencies of cybervictimization.

In terms of age, the reported incidence of cybervictimization was high in the younger grade, which is consistent with the literature reviewed, which indicates middle school as the stage with the highest rate of cyberbullying (Kowalski et al., 2014). Unlike Hinduja and Patchin (2008), who described a peak of cybervictimization in eighth grade, our results show the highest peak in fifth grade, as did a meta-analysis by Barlett and Coyne (2014). As noted by Kowalski et al. (2019), this decline could be attributed to the effects of developmental stages of students, who adapt socially as they grow; however, the specific mechanisms that explain a decline in cybervictimization rates as age increases are still unknown and require further research.

The results also highlight the importance of considering the interactions of identity markers that could put specific population groups at greater risk of cybervictimization. As stated from the intersectional feminist perspective, the effects of multiple group memberships can also include multiplier effects, such that the effects of sexism or racism can exacerbate the effects of other markers (Bowleg, 2008; Bell et al., 2019). In this research, feeling discriminated against because of sexual orientation had a significant interaction effect with gender, SES, and over age.

In that sense, the experience of cybervictimization of students of some ethnic origin or who report feeling discriminated against because of their sexual orientation and also belong to more socioeconomically vulnerable groups could be different than the experience of students with only one of these characteristics. Considering Butler’s (Big Think, 2011) perspective, in which cyberbullying constitutes as a social practice of discrimination that reproduces a hegemonic order when discussing the intersectional approach, it can be understood that each category to which a person belongs, when interacting, produces a diversity of discrimination forms that are unique (Sánchez Melero and Gil Jaurena, 2015), so it is possible to hypothesize that cybervictimization is also a phenomenon in which individual characteristics and their social implications are brought into play and are decisive in their frequency and quality.

The results of this research support the idea that a quantitative methodology can be a theoretical partner of an intersectional feminist theoretical framework. Most research has explored intersectionality using a qualitative methodology. However, an empiricist–feminist approach, which incorporates a quantitative methodology, makes visible the problems of bias in the research process, because it recognizes the importance of attending to the interaction of identity elements such as race, ethnicity, SES, and sexual orientation of the research participants (Else-Quest and Hyde, 2016).

In the Chilean case, it is particularly important to review the experience of cybervictimization of students who are over age with respect to their grade, due to the grade repetition policies of the educational system. The results show that over age at an individual level is associated with greater cybervictimization, but not at the school level. Although in general terms, male students had higher levels of cybervictimization, when over age interacts with the student’s gender, there was cybervictimization among girls than boys. A possible explanation for this result could involve the different levels of sexual development among female students; however, further research is needed to support this statement.

With respect to school-level variables, it is important to mention that in contrast to individual SES, school SES was not associated with higher cybervictimization. These results might be explained by the similar characteristics of vulnerability shared among schools. In the context of an education system characterized as highly segregated due to neoliberal policies (López et al., 2012), further research would be needed to assess the effect of socioeconomic segregation of schools on students’ experience of cybervictimization.

A limitation of this study was the operationalization of discrimination due to sexual orientation, which was used as an approximation of sexual orientation. The variable measured having reported feeling discriminated against because of sexual orientation as perceived by other people, but not their actual sexual orientation per se. Future research in this field should employ a rigorous approach to measuring homophobic victimization, defining and measuring sexual orientation directly—considering the differences among homophobia, lesbophobia, biphobia, and transphobia—and establishing its statistical relationship to cybervictimization. In other words, future studies ought to aim at differentiating whether the victim is being harassed specifically because of their sexual orientation, or if the harassment involves homophobic comments and/or slurs to imply that the victim is not heterosexual despite their actual sexual orientation—a dynamic most common among teenage boys who say “that’s gay” as an insult—.

In addition, the methodology used was exclusively quantitative, therefore new studies should include a qualitative view at this phenomenon to deepen our understanding of the lived experiences of people who belong to minority groups of gender, sexual orientation, ethnic origin, SES, and over age—who reported greater cybervictimization—and how it relates to these variables and their interactions, emphasizing the voices of people who are positioned at different intersections of these categories. In this sense, the cross-sectional design is also a limitation, so future work should consider a longitudinal design that allows exploration of both the particularities of the interactions between different identity markers and the consequences of cybervictimization for the most vulnerable groups over time. For example, studies should inquire in the evolution of cybervictimization in students who are victims as they undergo a gender transition, analyzing the effects of the changing gender category as well as the newly acquired “trans” category.

Our findings suggest that public policies should recognize the diversity of students in the educational system, considering non-discrimination as the main axis to promote a healthy school coexistence. Understanding the intersectional nature of the violence experienced by people from different marginalized groups (Sánchez Melero and Gil Jaurena, 2015) is essential to guide promotional and preventive actions against violence. The incorporation of inclusive approaches, which promote heterogeneity as a value, respect for diversity as a fundamental pillar, and whole-school approaches can be fundamental in guaranteeing that all children and adolescents have an education free from violence, including that produced through digital media.

Based on these findings, it also is critical that public policies consider the importance of addressing digital citizenship (Sierra Caballero and Gravante, 2016) through school climate improvement efforts and promoting and teaching ethical behaviors in virtual media with a gender and social justice perspective that promotes active citizenship (Bolívar, 2016). Regarding the public-school mental health program in which this study is inscribed -HpV Program- as part of a monitoring system on school violence and school climate, the findings will allow the design of whole-school promotion and preventive strategies from an intersectional approach and with focus on information and communication technologies (ICTs) which have been crucial during the COVID-19 pandemic.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Bioethics Committee, Pontificia Universidad Católica de Valparaíso. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Written informed consent was obtained from the individual(s), and minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.

Author Contributions

MJ, CY, JR, VL, and CU conceived and designed the analysis. VL, AS, and PS led data production. MJ, CY, JR, VL, and LG performed the analysis. MJ, CY, JR, VL, LG, CU, AS, and PS wrote the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research was funded by the SCIA ANID CIE160009, FONDECYT 1191267, and FONDECYT 11180634.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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