Cyberbullying

Abstract

Although the research on cyberbullying has increased dramatically in recent years, still little is known about how cyberbullying participant groups (i.e., cyberbullies, cybervictims, and cyberbully-victims) differ from one another. This study aims to discriminate between these groups at an individual and relational level by controlling for age and gender. Self-control, offline aggression, and self-esteem are analyzed as individual-level variables. Parental attachment and peer rejection are involved as relational-level variables. A total of 2,092 Czech adolescents aged 12 to 18 were enrolled from a random sample of 34 primary and secondary schools located in the South Moravian region of the Czech Republic. Discriminant function analyses indicated that the participant groups are discriminated by two functions. The first function increases the separation between cyberbullies and cyberbully-victims from cybervictims, indicating that cyberbullies and cyberbully-victims are similar to each other in terms of low self-control, offline aggression, and gender, and have higher scores on measures of low self-esteem and offline aggression. However, cyberbully-victims had the highest scores on these measures. The second function discriminates between all three groups, which indicates that those variables included in the second function (i.e., parental attachment, peer rejection, self-esteem, and age) distinguish all three involved groups.

Keywords

bullying media and violence Internet and abuse

Introduction

With the increasing use of electronic communication technologies, bullying, a long-standing problem in children’s lives (see, for example, Olweus, 1993), has begun to take place through electronic communication technologies as well as in person. This form of bullying is called cyberbullying, a term designating an online form of aggression. The exact definitions of cyberbullying vary across studies. There are, however, common features used in most conceptualizations, defining cyberbullying as an “aggressive, hostile, or harmful act that is perpetrated by a bully through an unspecified type of electronic device” (Tokunaga, 2010, p. 270). The electronic devices and means may include e-mails, blogs, social networking sites, mobile phones, and/or others. Some other features are also cited in definitions of cyberbullying, such as power imbalance and repetition; however, these are used inconsistently (Smith, del Barrio, & Tokunaga, 2013), contributing to differences in findings across studies (Ybarra, 2013).

Among children, cyberbullying is often connected with traditional bullying: victims of cyberbullying are often also victims of traditional bullying, and bullies have been found to be cyberbullies as well (Gradinger, Strohmeier, & Spiel, 2009; Hinduja & Patchin, 2008; Raskauskas & Stoltz, 2007; Ybarra & Mitchell, 2004a). Both phenomena also share common features, such as the intentional and aggressive/hostile character of the behaviors and serious harm to the victim (Dooley, Pyzalski, & Cross, 2009; Li, 2007; Slonje & Smith, 2008). Thus, many findings from research on traditional bullying are highly relevant to cyberbullying as well. However, it is important to acknowledge the features that make cyberbullying distinct: (a) a degree of technological expertise is required, (b) cyberbullies may have a greater degree of anonymity compared with traditional bullies, and (c) the act of bullying in cyberspace typically happens when the bully and victim are physically distant (Cowie, 2009; Li, Smith, & Cross, 2012). Also, (d) cyberbullying tends to be generally more indirect than traditional bullying, (e) the role of physical strength is minimal, (f) the audience can be substantially wider, and (g) compared with traditional bullying, it is difficult to escape or hide from cyberbullying attacks (e.g., in one’s own home; Heirman & Walrave, 2008; Ševčíková, Šmahel, & Otavová, 2012).

Characteristics of Bullies, Victims, and Bully-Victims in Real Life and Cyberspace

In research on traditional bullying, three groups of active participants have been identified: bullies, victims, and bully-victims (see, for example, Salmivalli, Lagerspetz, Björkqvist, Österman, & Kaukiainen, 1996; Sutton & Smith, 1999). It should be mentioned that there is also a fourth group of participants called bystanders, consisting of children who are not directly involved but who also (albeit indirectly) participate in the process (Salmivalli, 2010).

Bullies are characterized as aggressive children with low self-control (Olweus, 1991; Sourander et al., 2010), domineering toward peers, and with poorer relationships (Boulton & Smith, 1994; Cairns, Cairns, Neckerman, Gest, & Gariépy, 1988; Dodge, 1991). Victims are generally characterized by low self-esteem (O’Moore & Kirkham, 2001), low self-control (Björkqvist, Ekman, & Lagerspetz, 1982; Perry, Kusel, & Perry, 1988), and peer rejection (Björkqvist et al., 2001; Graham & Juvonen, 1998; Pellegrini, Bartini, & Brooks, 1999). Bully-victims were found to be more aggressive (Salmivalli & Nieminen, 2002), have more insecure attachments to parents, and higher peer rejection compared with bullies and victims (Boulton & Smith, 1994; Bowers, Smith, & Binney, 1994).

Knowledge of these characteristics is important for identifying children at risk of involvement in bullying. But groups of children involved in bullying are distinct not only in comparison with those not involved at all, but also when the groups are compared with one another. For example, despite the fact that some aggressors have worse status within their social groups, they are still better accepted than victims (Lagerspetz, Björkqvist, Berts, & King, 1982; Salmivalli et al., 1996). The findings of Haynie et al. (2001) supported the hypothesis that all three groups can differ substantially in several respects, such as problem behaviors, parenting, and depressive symptoms. Specifically, the researchers indicated that bully-victims were a distinct group showing more problem behaviors, depressive symptoms, and conflicts with parents compared with bullies and victims. Similarly, Unnever (2005) indicated that bully-victims were significantly different from bullies and victims and had higher parental/family conflict, higher reactive/proactive aggression, lower self-control, and weaker social bonds. Therefore, the issue of distinguishing between children involved in bullying in different roles is important, as is examining the connection between the aforementioned characteristics.

Research on cyberbullying also looks into differences among groups of involved and non-involved children, and relevant predictors have been already identified, as we review further. But, to our knowledge, only a handful of studies have compared the groups of children directly involved in cyberbullying (Gradinger et al., 2009; Vandebosch & van Cleemput, 2008, 2009). Gradinger et al. (2009) focused on the co-occurrence of traditional bullying, cyberbullying, traditional victimization, and cybervictimization and compared the groups in terms of internalizing and externalizing behaviors. They found that bully-victims (both traditional and cyber) had higher reactive/proactive aggression and depressive and somatic symptoms. Vandebosch and van Cleemput (2008) compared cyberbullies and cybervictims in terms of age, Internet dependency, perceived popularity, and co-occurrence of offline and online bullying/victimization. They found that cyberbullies were younger and more often the perpetrators of traditional bullying. In contrast, cybervictims were less popular, more Internetdependent, and more often victims of traditional bullying.

In sum, none of the studies compared all three groups involved from a more holistic viewpoint, including both individual-and relational-level variables. Therefore, to construct a more complex portrait of children involved in cyberbullying, we follow the Ecological Systems Theory (EST; Bronfenbrenner, 1979, 1994). According to Bronfenbrenner (1994), human development occurs within a nested environmental system (i.e., micro-, meso-, exo-, and macrosystem). The microsystem includes the individual and his/her most direct relations (e.g., parents, peers). The mesosystem includes the interactions between two or more microsystems. The exosystem contains the contexts in which the individual is not involved directly. Finally, the macrosystem represents the social and cultural structures within which the individual develops. Following EST, we examined two demographic variables (age, gender) as the core developmental characteristics of an individual in the center of ecosystem of human development. Three individual-level (i.e., self-esteem, self-control, and offline aggression) and two relational-level (i.e., peer rejection and parental attachment) variables were analyzed as factors of the microsystem. Discriminant Function Analysis (see details below) provided the combined effect between peer and parental relations under functions. Therefore, the results also show some indications for the mesosystem. A brief literature review is presented below to show the associations between demographic, individual, and relational-level variables and traditional bullying/cyberbullying.

Demographic Variables

Age

The relationship between cyberbullying and children’s age is not clear: some studies found a positive correlation (e.g., Hinduja & Patchin, 2008; Ybarra & Mitchell, 2004a), some found an inverse relation (e.g., Ševčíková & Šmahel, 2009; Ybarra, Mitchell, Wolak, & Finkelhor, 2006), while others found no significant association (e.g., Slonje & Smith, 2008; Smith, Mahdavi, Carvalho, & Tippett, 2006). To the best of our knowledge, there is no study that has compared the three groups involved in cyberbullying in terms of age.

Gender

As in the case of age, research on the relation of gender to cyberbullying shows inconsistent results, and none of the studies compared all three groups in terms of gender. Some studies found that males tend to be more involved in cyberbullying, whereas females tend to be cyberbullied (Aricak et al., 2008; Li, 2007; Slonje & Smith, 2008), some studies showed the reverse (Bauman, 2012; Kowalski & Limber, 2007), and some others indicated no difference (Ortega, Calmaestra, & Mora-Merchán, 2007; Smith et al., 2008; Wolak, Mitchell, & Finkelhor, 2007). Lastly, in a meta-synthesis by Tokunaga (2010), both males and females were found to be equally vulnerable to cyberbullying.

Individual-Level Variables

Offline aggression

Aggression, as a concept that is closely linked to antisocial behavior, is becoming one of the salient topics in discussions of the online world. Perceived anonymity, physical distance, and online disinhibition may quite easily result in cyberbullying, as these conditions support the expression of aggressive tendencies (Brighi, Guarini, & Genta, 2009; Brighi et al., 2012). These features were already mentioned as some of the causes of aggression by Bandura (1996).

Cyberbullying and aggression are thus inextricably linked, yet still they represent two different constructs: that is, every instance of cyberbullying is an act of aggression, but not every act of aggression is cyberbullying. Many studies have pointed out the association between cyberbullying and aggression (Ang, Tan, & Mansor, 2010; Aricak et al., 2008; Calvete, Orue, Estevéz, Villardón, & Padilla, 2010; Dilmaç, 2009; Gradinger, Strohmeier, & Spiel, 2012), whereas some of the authors even use aggression (or cyber-aggression) as an umbrella term for all such behavior, including bullying and cyberbullying. Ybarra, boyd, Korchmaros, and Oppenheim (2012) emphasize the need for distinguishing between cyber-aggression and cyberbullying according to specific criteria (such as repetition or the fact that the behavior is intentional). Thus, offline aggression can serve as a key concept to understand cyberbullying, yet little is known about how specific groups of young people involved in cyberbullying differ in offline aggression, as it has not been studied to the extent that traditional bullying has. Also, there are wide variations in terminology and definitions within the literature of aggression up to this point, which makes it difficult to compare and generalize the results (Sugarman & Willoughby, 2013).

Self-esteem

As we already mentioned, studies dealing with traditional bullying indicate that victims have lower self-esteem compared with non-victims (Egan & Perry, 1998; Salmivalli, Kaukiainen, Kaistaniemi, & Lagerspetz, 1999; Wild, Flisher, Bhana, & Carl, 2004) and bullies (e.g., Frisén, Jonsson, & Persson, 2007; Jankauskiene, Kardelis, Sukys, & Kardeliene, 2008). However, some studies found higher self-esteem among bullies compared with non-involved children (e.g., Beaty & Alexeyev, 2008; Salmivalli et al., 1999). The studies showed that bully-victims had lower self-esteem compared with victims and bullies (e.g., Andreou, 2000). Some studies indicated similar associations between self-esteem and cyberbullying (e.g., Brighi, Genta, & Guarini, 2008; Brighi et al., 2012; Harman, Hansen, Cochran, & Lindsey, 2005; Kowalski, Limber, & Agatston, 2008; Patchin & Hinduja, 2010), confirming that cybervictims and cyberbully-victims tend to have lower self-esteem compared with others; however, still less is known about cyberbullies’ self-esteem.

Self-control

In a meta-analysis of the studies related with Gottfredson and Hirschi’s (1990) General Theory of Crime, Pratt and Cullen (2000) showed that low self-control is a strong predictor of criminal and analogous behaviors. However, there are few studies in the literature that show an association between low self-control and traditional bullying (e.g., Archer & Southall, 2009; Haynie et al., 2001; Unnever & Cornell, 2003). The number of studies that focused on the correlation between low self-control and cyberbullying was even smaller (e.g., Bossler & Holt, 2010; Vazsonyi, Machackova, Sevcikova, Smahel, & Cerna, 2012). We should consider that the Internet represents a unique context for disinhibited behaviors because of its features of anonymity and invisibility (Heirman & Walrave, 2008). Low self-control can further strengthen disinhibited behaviors and therefore increase the tendency for cyberbullying. Accordingly, Bossler and Holt (2010) found that low self-control predicted higher cyberbullying rates as well as other deviant behaviors online, including cybercrimes. Similarly, Vazsonyi et al. (2012) found that low self-control was directly and indirectly—through offline bullying and victimization—related to both cyberbullying and cybervictimization. In other words, low self-control was found as a common predictor of cyberbullying and cybervictimization. However, to the best of our knowledge, no study has yet compared cyberbullies, cybervictims, and cyberbully-victims in terms of low self-control.

Relational-Level Variables

Parents and peers are crucial parts of adolescents’ social context. The quality of peer and parental relationships can be related to both positive and negative outcomes for children’s well-being (Fraley & Davis, 1997; Kumru, Carlo, & Edwards, 2004).

Parental attachment

Many studies (e.g., Demaray & Malecki, 2003; Haynie et al., 2001) have indicated that both traditional bullies and victims perceived less support from their parents compared with non-involved children/adolescents, which was an indicator of insecure parental attachment (see Cutrona, Cole, Colangelo, Assouline, & Russell, 1994, for parental support−parental attachment connection). This was comparable with the results of studies on cyberbullying as well. For example, Ybarra and Mitchell (2004a, 2004b) found that the perpetrators of cyberbullying had poorer emotional bonds with parents than those not involved. Wang, Iannotti and Nansel (2009) found that parental support was negatively correlated with all forms of bullying (i.e., verbal, physical, relational, and cyberbullying). Moreover, in a recent longitudinal study, Fanti, Demetriou, and Hawa (2012) found that, for adolescents, parental support was a protective factor against becoming a cyberbully or a cybervictim.

Peer rejection

The context of overall peer relationships plays a substantial role in bullying (Salmivalli, 2010). Many studies showed that victims had more conflicts and arguments with their friends and perceived more rejection by their peers (Champion, Vernberg, & Shipman, 2003; Gradinger et al., 2012; Kumpulainen, Räsänen, & Henttonen, 1999; Pellegrini et al., 1999). These consistent results can be explained by the Friendship Protection Hypothesis (Hodges, Boivin, Vitaro, & Bukowski, 1999), which presumes that rejection by peers can result in a smaller number of friends and less protection against bullying.

However, studies concerned with relationships between aggressors and peers point out that there is not a single trend. Some bullies perceive lower levels of trust and support and more rejection by their peers (Chang et al., 2005; King & Terrance, 2006), while other bullies with high social-cognitive abilities may have high-quality and securely attached friendships (Arnocky & Vaillancourt, 2012; De Bruyn, Cillesen, & Wissink, 2010; Hawley, 2003; Reijntjes et al., 2013; Xie, Swift, Cairns, & Cairns, 2002). Studies focused on cyberbullying confirm these trends of lower support or trust and higher rejection for both bullies and victims (Calvete et al., 2010; Vandebosch & van Cleemput, 2009), but no consistent research has been done in this area yet.

Research Questions and Hypothesis

As seen in this literature review, there are many inconsistent findings and remaining questions regarding the role of described factors in cyberbullying. The origin of these inconsistencies is not clear, and the results of previous research are hard to compare and synthesize due to differences between the methodologies used (Ybarra, 2013). Studying the differences among all three participant roles in one sample simultaneously allows us to make a direct comparison and explore the mutual relations between these groups without the constraints that result from diverse definitions of terminology and heterogeneous samples. Thus, we aimed to address the following research question:

Research Question 1: Do the three cyberbullying roles (i.e., cyberbullies, cybervictims, and cyberbully-victims) differ from each other in demographic, individual, and relational factors?

Specifically, we ask whether cyberbullies, cybervictims, and cyberbully-victims will be distinct from each other in terms of gender, age, low self-control, aggression, self-esteem, peer rejection, and parental attachment.

Based on the theory, we formulated three specific hypotheses. First, we expect that cyberbully-victims will be the most distinct group with the most psychosocial difficulties.

Hypothesis 1: Cyberbully-victims will have higher offline aggression, lower self-control, lower self-esteem, more insecure parental attachment, and higher peer rejection than cyberbullies and cybervictims.

Second, we expect that in some areas, cyberbullies will have better adjustment than cybervictims or cyberbully-victims.

Hypothesis 2: Cyberbullies will have higher self-esteem and lower peer rejection than cybervictims.

Third, we also expect that cyberbullies will have poorer psychosocial functioning in other areas.

Hypothesis 3: Cyberbullies will have higher offline aggression and lower self-control.

Because results of empirical studies are mixed with regard to demographics, we did not formulate a specific hypothesis for these variables.

Methods

Procedure

The present study was part of a research project that aimed to examine adolescents’ experiences with and responses to cyberbullying. Data were collected via an online survey of 2,092 Czech adolescents aged 12 to 18 (M = 15.1, SD = 1.86; 54.7% females) from primary and secondary schools located in the South Moravian region of the Czech Republic. The schools were randomly selected from a list of all schools in the region. Of the 63 schools that were contacted, 25 declined to participate. Depending on size of the school and their technical facilities, one or more classes participated in each (mode: 4 classes). Informed consent was obtained from the headmaster of every selected school. An anonymous online questionnaire was filled out by students in the school computer labs in the presence of a trained administrator who could answer children’s questions and offer technical advice, if needed. Pupils were motivated by the chance of winning one of three vouchers (about 80 Euros in value). The students were offered the choice not to participate in the study, but none declined.

Measures

Demographic variables

Respondents indicated their age (in years) and gender (0 = male, 1 = female).

Cyberbullying participants’ roles

The respondents were provided with a description of cyberbullying as misusing the Internet or mobile phone to purposefully and repeatedly harm or harass another person who cannot easily defend himself or herself. The description was illustrated with examples of cyberbullying, such as sending offensive and vulgar e-mails, SMS, or IM messages, or impersonating someone. The respondents were asked dichotomous questions, whether or not they had ever experienced anything similar and whether they had done something similar to someone else. Overall, 528 adolescents (25.2% of the whole sample) had been involved in cyberbullying. Those who indicated cybervictimization experience were labeled as cybervictims (16.8%, n = 351), those who indicated bullying someone were labeled as cyberbullies (3.6%, n = 76), and those who indicated both experiences were labeled as cyberbully-victims (4.8%, n = 101); all categories are mutually exclusive. The rest of the sample (i.e., the non-involved group) was not included in the analyses because the main aim of the study was to compare participant roles in cyberbullying.

Self-esteem

Rosenberg’s (1965) 10-item Self-Esteem Scale was used (e.g., “I feel that I have a number of good qualities”; “At times I think I am no good at all”) with 4-point answers ranging from “strongly disagree” to “strongly agree.” Scale scores were computed as average of all items. The internal consistency of the scale was good (Cronbach’s α = .84, M = 2.73, SD = 0.50).

Offline aggression

The Buss–Perry Aggression Questionnaire scale (Bryant & Smith, 2001) was used, with 9 items measuring anger and physical/verbal aggression (e.g., “Given enough provocation, I may hit another person”; “I often find myself disagreeing with people”). Three items that decreased the reliability of the scale were omitted. Items were measured on a 4-point scale (1 = strongly disagree to 4 = strongly agree), and the final scale was calculated as the average of all answers (M = 2.43, SD = 0.53, Cronbach’s α = .78).

Low self-control

Low self-control was measured by 9 items created ad-hoc with 4-point scale answers (1 = strongly disagree to 4 = strongly agree; e.g., “I interrupt when people talk”; “It is a problem for me to say no to appealing things”). Principal component analysis confirmed the one-dimensional structure of the scale; final scores were calculated by averaging all answers (Cronbach’s α = .78, M = 2.57, SD = .63).

Parental attachment

The Parental Attachment subscale of Armsden and Greenberg’s (1987) Parental and Peer Attachment Scale was used, with 23 items and three subdimensions measuring trust toward parents (e.g., “My parents respect my feelings”), communication with parents (e.g., “My parents encourage me to talk about my problems”), and alienation from parents (e.g., “My parents have their own problems, so I don’t bother them with mine”). Following the authors’ method, we calculated parental attachment with the equation: [(Trust + Communication) – (Alienation)]; M = 4.57, SD = 2.49. The internal consistencies of the dimensions were .90, .90, and .80 for trust, communication, and alienation, respectively.

Peer rejection

A subscale Rejection by Peers from a scale measuring quality of peer attachment (Širůček & Širůčková, 2008) was used, consisting of 8 items (e.g., “They reject me”; “They laugh at me”) with 5-point answers ranging from “never” to “always” (Cronbach’s α = .87; M = 1.791; SD = 0.72).

Analysis

A direct discriminant function analysis was performed. This multivariate statistic is used to predict a categorical dependent variable using a series of predictor variables, and it identifies new latent variables known as discriminant functions by combining variables in one or more linear combinations (i.e., functions), which then discriminate between the groups (Cohen, Cohen, West, & Aiken, 2003). In our study, cyberbullying was divided into three types of participants (cyberbullies, cybervictims, and cyberbully-victims) as previously mentioned. All analyses were conducted using PASW.20.

Results

Descriptive Analyses

Using the chi-square test, we examined the gender differences within each role. There were significant differences between the groups, χ²(2) = 44.02, p < .001. Among cybervictims, females prevailed (73.5% females), while the opposite trend applied for cyberbullies (39.5% females); for cyberbully-victims, the gender distribution was almost symmetric (48.5% females).

We used one-way ANOVAs to compare the age differences between the groups. No age difference was found (M = 15.21, 15.27, and 15.21 for cyberbullies, cybervictims, and cyberbully-victims, respectively).

Correlations Between Variables

Pearson correlational coefficients between the variables are presented in Table 1. All correlations were significant.

Table 1.

Pearson Correlational Coefficients Between the Variables.

	Offline Aggression	Self-Esteem	Low Self-Control	Parental Attachment	Peer Rejection
Self-esteem	−.13**	—
Low self-control	.65**	−.10**	—
Parental attachment	−.21**	.27**	−.26**	—
Peer rejection	.35**	−.19**	.22**	−.19**	—

Note. **p < .001 (two-tailed).

One-Way ANOVAs

A series of one-way ANOVAs were conducted to investigate whether the values of individual-and relational-level variables differed according to cyberbullying roles (see Table 2). Bonferonni post-hoc tests showed that cyberbullies had significantly higher self-esteem scores than cybervictims. For low self-control, cyberbullies’ and cyberbully-victims’ self-control was lower than cybervictims’; an opposite trend applied for offline aggression, with cybervictims showing the lowest levels of offline aggression. Cybervictims had significantly higher parental attachment levels and lower peer rejection levels than cyberbully-victims.

Table 2.

Analyses of Variance for the Individual and Relational-Level Variables.

	M (SD)
Variable	Cyberbully (n = 76)	Cybervictim (n = 351)	Cyberbully-Victim (n = 101)	df	η²	F
Self-esteem	2.86^a (0.61)	2.69^a (0.59)	2.81 (0.61)	2	.01	3.37*
Low self-control	2.86^a (0.62)	2.41^a,b (0.58)	2.99^b (0.58)	2	.12	36.57**
Offline aggression	2.59^a (0.69)	2.29^a,b (0.57)	2.78^b (0.58)	2	.11	29.55**
Secure parental attachment	40.04 (16.99)	41.92^a (19.09)	33.11^a (19.49)	2	.03	7.59**
Peer rejection	15.85 (4.90)	14.69^a (5.69)	16.51^a (5.99)	2	.02	4.54*

Note. Values with same superscript are significantly different from each other.

p < .05. **p < .001.

Predictors of Membership for Three Cyberbullying Roles

We used the set of individual and relational variables, as well as age and gender, as predictors of membership in the three cyberbullying participant groups. Two discriminant functions were calculated. The first function (which included self-control, offline aggression, and gender) accounted for 80.6% of variance and had a canonical correlation of .449. The second function (which included peer rejection, parental attachment, self-esteem, and age) accounted for 19.4% of variance and had a canonical correlation of .16. As seen in Figure 1, the first function increased the separation between cyberbullies and cyberbully-victims from cybervictims; that is, the group centroids were negative for cybervictims and positive for cyberbullies and cyberbully-victims on the horizontal axis. This indicated that cyberbullies and cyberbully-victims were similar to each other in terms of low self-control, offline aggression, and gender, and had higher scores on these measures compared with cybervictims. The second function discriminated between all three groups; that is, the group centroids were similarly distant from each other on the vertical axis. This indicated that those variables loaded on the second function (i.e., parental attachment, peer rejection, self-esteem, and age) distinguished all three involved groups.

Figure 1.

Plot of group centroids on the functions resulting from the discriminant function analysis depicting the distance between the group means.

The loading matrix of correlations between predictors and discriminant functions is given in Table 3. The magnitude of the correlation can be interpreted as the degree to which each predictor contributes to the accuracy with which the function differentiates the groups. The two best predictors of group membership were low self-control and offline aggression. Parental attachment, gender, peer rejection, and self-esteem were all strong predictors. Age was the weakest predictor.

Table 3.

Discriminant Function Analyses: Correlations Between Predictor Variables and Functions.

Variable	Function 1	Function 2
Low self-control	.752*	.026
Offline aggression	.640*	.480
Gender	−.581*	.398
Peer rejection	.264	.275*
Parental attachment	−.279	−.606*
Self-esteem	.230	−.246*
Age	−.047	.069*
Percentage variance	80.6	19.4
Canonical correlation	.449	.160
Wilks’s lambda	.78	.97
Chi-square	125.01	12.89
Degrees of freedom	14	6

Note. *p < .05.

The accuracy of the classification coefficients from the discriminant function was assessed by comparing the classifications of participants using the coefficients with the original classifications based on self-reports (see Table 4). The classification function coefficients correctly classified 84.3% of the cases. The classification results most accurately distinguished the cybervictims from the other two groups.

Table 4.

Accuracy of Classification of Participants into Groups by the Discriminant Functions.

	Predicted Group
Original Group	Cyberbully	Cybervictim	Cyberbully-Victim	Total
Cyberbully	47 (65.3%)	6 (8.3%)	19 (26.4%)	72 (100%)
Cybervictim	7 (2.1%)	310 (92.8%)	17 (5.1%)	334 (100%)
Cyberbully-victim	29 (29.6%)	1 (1.0%)	68 (69.4%)	98 (100%)

Discussion

This study on cyberbullying among Czech adolescents focused on distinguishing the three groups of children involved in cyberbullying: cybervictims, cyberbullies, and cyberbully-victims. We considered the complexity of the cyberbullying problem and focused on several individual as well as relational characteristics that have been studied in previous research on both traditional bullying and cyberbullying, and examined how the three groups are distinctly characterized by the selected characteristics.

The main distinction we found was between cybervictims and the other two participant groups. First, gender was among the most important predictors of group classification. Considering the basic gender differences in our sample, males were more often involved as cyberbullies, while females were more often cybervictims (which is consistent with some of the prior findings; e.g. Aricak et al., 2008; Li, 2007; Slonje & Smith, 2008). Interestingly, both genders were equally likely to be involved as cyberbully-victims. These differences became clearer in the complex analysis, which showed that in comparison with cyberbullies and cyberbully-victims, cybervictimization is distinctly predicted by gender (i.e., more girls are among the victims). Our finding opposes Tokunaga’s (2010) pre-conclusion about symmetrically distributed gender in cyberbullying, but this might be partly explained by distinction of the cyberbully-victims group, which included children often simply classified as “victims.” It should be also mentioned that higher prevalence of girls as cybervictims could increase the classification (prediction) of cybervictims (see Table 4). Previous studies showed that females reported more cybervictimization than males (Hoff & Mitchell, 2009, 2010). The authors interpreted this skewed response as a result of gender socialization (i.e., for females, being fragile and vulnerable is a normative feature, and this normalization could make females more willing to report cases of cybervictimization).

Next, cybervictimization is also distinguished by higher self-control and lower offline aggression relative to the other two groups. On the other hand, cyberbully-victims displayed higher levels of offline aggression and had lower self-control than cyberbullies. This result was in line with previous research on traditional bullying, which showed that bully-victims had more adjustment problems compared with bullies and victims (e.g., Andreou, 2000; Boulton & Smith, 1994; Bowers et al., 1994; Salmivalli & Nieminen, 2002). The differences in levels of self-control deserve more research attention, however, considering the importance of this concept for the explanation of antisocial behavior (Gottfredson & Hirschi, 1990; Pratt & Cullen, 2000); cyberbully-victims who showed higher levels of offline aggression and lower self-control might be a more vulnerable group for delinquent and antisocial behaviors compared with cyberbullies and cybervictims. Considering that in prior studies, cyberbully-victims were also found to have high normative beliefs about aggression (thinking that acting aggressively is something normal; Burton, Florell, & Wygant, 2013), this group should gain more attention from both researchers and prevention and intervention workers. Understanding the characteristics and beliefs that could underlie the aggravated aggression could help us take measures to prevent and intervene in cyberbullying.

Our results can contribute to an understanding of this issue mainly with regard to the group of cyberbully-victims, who are often classified as victims, but in many respects have more in common with cyberbullies and differ from victims substantially, as our study shows. The distinctiveness of this group is even more apparent if we take into account the second main differentiation according to the levels of parental attachment, peer rejection, self-esteem, and age. In this regard, all three groups proved to be different from one another. The largest distinction with regard to these characteristics was found between cyberbullies and cyberbully-victims. In the multiple-group prediction, the cyberbully-victims are children with poorer parental attachment, lower self-esteem, and high scores on peer rejection. They also seem to be older, but this relation was weak and should thus be interpreted with caution. These findings were in line with the traditional bullying literature, which consistently mentioned that bully-victims constitute a distinctive group that is more at risk than bullies and victims (e.g., Andreou, 2000; Bowers et al., 1994; Haynie et al., 2001; Salmivalli & Nieminen, 2002).

Our results also support findings that in both traditional and cyberbullying, victimization is connected to lower self-esteem (when compared with bullying; e.g., Brighi et al., 2008, 2012; Frisén et al., 2007; Harman et al., 2005; Jankauskiene et al., 2008; Kowalski et al., 2008; Patchin & Hinduja, 2010). On the other hand, when considering the effect of all variables, cybervictims had better parental attachment and were less rejected by peers than cyberbully-victims, yet still more rejected than cyberbullies. The literature on traditional bullying also consistently indicated that traditional victims perceived more rejection by their peers compared with bullies (e.g., Champion et al., 2003; Kumpulainen et al., 1999; Pellegrini et al., 1999). In this sense, our overall results can also be interpreted as supporting a view of online and offline bullying as interconnected phenomena.

Finally, all three groups were distinguished by significant age differences, although age was found to be the weakest predictor. It is important to note here that according to univariate statistics, age might be similarly varied across groups, and the results of multivariate statistics should be interpreted with caution. However, for future research, we would suggest the examination of a possible longitudinal explanation for this finding.

In sum, our expectations about the differences were supported by our results. The three participant groups of cyberbullying were found to be distinct from each other, and cyberbully-victims were found to be the group with most psychosocial difficulties. Also, it was shown that Bronfenbrenner’s EST can be used as a useful framework to understand cyberbullying, as McGuckin and Minton (2014) mentioned in their recent review. Specifically, our results indicated that microsystem variables (and mesosystem as a form of their interactions) might be used to discriminate cyberbullies, cybervictims, and cyberbully-victims.

Although this study contributes to the current literature, it was not free from several limitations. First, due to the cross-sectional nature of our data, we are not able to make any causal links. Also, despite the fact that we collected the data in classes at school, we do not have information about the placement of students in classes. We would recommend collecting this information in future studies, which would allow examination of whether there are effects on the class (and even school) level. Also, the direct interactions between the variables were not tested due to the multivariate statistical analysis used, and our focus on discriminant factors among three participants groups. Future research might focus on possible interactions (i.e., Peer rejection × Insecure parental attachment) by using different analyses (e.g., Hierarchical Regression Analysis, Moderated Regression Analysis, Structural Equation Modeling). All analyses were dependent on self-reported data, which makes the findings, especially those concerned with parental attachment and peer rejection, difficult to compare with studies using multi-informant approaches (e.g., data from peers or teachers). Respondents might also have been hesitant to answer honestly because the survey was administered in a group setting at school, even though administrators in classes reminded respondents to focus on their own questionnaire only. Also, including relational/indirect forms of offline aggression might enrich the findings. Lastly, the determination of cyberbullying roles depended on dichotomous variables. Our results might change with more precise measurements.

Conclusion and Implications

To conclude, our findings contribute to the existing literature on cyberbullying by showing that cyberbullies, cybervictims, and cyberbully-victims are substantially different from one another. Specifically, they differ substantially in their levels of self-control and offline aggression. These groups also show significant differences in gender, quality of relationships with parents and peers, and self-esteem.

Although cyberbullying is not an entirely new academic field, more research is needed to understand the dynamics related to this problem. This study is the first to examine the factors discriminating among three cyberbullying participant groups. Our findings may help researchers to understand the differences between cyberbullies, cybervictims, and cyberbully-victims, and to give a way for further research aiming to find the other discriminant factors between these groups. Moreover, our results might have some implications, especially for practitioners (i.e., psychologists, social workers, school counselors) who are working with vulnerable populations such as bullies, victims, and bully-victims.

The results clearly showed that cyberbully-victims were more vulnerable than cyberbullies and cybervictims. This specific group showed the highest level of aggression, lowest self-control, poorest parental attachment, lowest self-esteem, and highest scores on peer rejection. It might be suggested that traditional bully-victims, who showed more developmental problems compared with bullies and victims, continue in this role online, bearing in mind that cyberbullies and cybervictims in our study also showed similar characteristics to traditional bullies and victims. Therefore, any intervention and prevention program related to cyberbullying should also take traditional bullying into account. Moreover, the programs must follow the holistic/contextual approach and aim for changes not only at the individual level, but also at the level of family and peer relations.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the support of the project VITOVIN (CZ.1.07/2.3.00/20.0184) and Employment of Newly Graduated Doctors of Science for Scientific Excellence (CZ.1.07/2.3.00/30.0009), which are co-financed by the European Social Fund and the state budget of Czech Republic.

Author Biographies

Fatih Bayraktar is an assistant professor in the Department of Psychology at Eastern Mediteranean University. His research interests include bullying-victimization, prosocial-antisocial behaviors among adolescents, cyberpsychology, resilience and parent-child relations.

Hana Machackova is a post-doc researcher in social psychology at Masaryk University, where she also works for the Institute for Research of Children, Youth and Family. Among her research interests are cyberbullying, online communities, SNS use, and self-disclosure and privacy online.

Lenka Dedkova is a PhD student of social psychology in the Institute for Research of Children, Youth and Family of Masaryk University. Her research interests include cyberbullying, meeting online strangers, online security behavior and SNS use.

Alena Cerna is a PhD student of social psychology at Masaryk University. Her research interests include cyberbullying, online communities, online subcultures, Internet & health, and psychotherapy.

Anna Ševčíková, PhD, is a postdoctoral researcher in the Faculty of Social Studies at Masaryk University in Brno, Czech Republic. Her research interest includes sexuality on the internet and psychosexual development. She also focuses on studying online risks among adolescents.

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