This article was downloaded by: [University of Alabama at Tuscaloosa], [Kelly Burton] On: 14 March 2013, At: 11:03 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Journal of Aggression, Maltreatment & Trauma Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/wamt20
Differences in Proactive and Reactive Aggression in Traditional Bullies and Cyberbullies a
b
K. Alex Burton , Dan Florell & Jonathan S. Gore
b
a
Department of Psychology, University of Alabama, Tuscaloosa, Alabama, USA b
Department of Psychology, Eastern Kentucky University, Richmond, Kentucky, USA
To cite this article: K. Alex Burton , Dan Florell & Jonathan S. Gore (2013): Differences in Proactive and Reactive Aggression in Traditional Bullies and Cyberbullies, Journal of Aggression, Maltreatment & Trauma, 22:3, 316-328 To link to this article: http://dx.doi.org/10.1080/10926771.2013.743938
PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Journal of Aggression, Maltreatment & Trauma, 22:316–328, 2013 Copyright © Taylor & Francis Group, LLC ISSN: 1092-6771 print/1545-083X online DOI: 10.1080/10926771.2013.743938
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Differences in Proactive and Reactive Aggression in Traditional Bullies and Cyberbullies K. ALEX BURTON Department of Psychology, University of Alabama, Tuscaloosa, Alabama, USA
DAN FLORELL and JONATHAN S. GORE Department of Psychology, Eastern Kentucky University, Richmond, Kentucky, USA
This study examined how proactive and reactive aggression related to traditional bullying and cyberbullying. Participants were 851 students in Grades 6 through 8 who completed a survey that assessed bullying behaviors, proactive aggression, and reactive aggression. Most of these students were Caucasian and from a rural background. For both traditional bullying and cyberbullying groups, uninvolved students were found to have significantly lower proactive and reactive aggression than bullies and bully-victims. Further, it was found that for traditional and cyberbullying groups, bully-victims had significantly higher proactive and reactive aggression than bullies. Implications and future directions are discussed. KEYWORDS aggression, bullying, cyberbully
Bullying in schools has been prevalent for many years and a considerable amount of the research on bullying has examined the impact on victims (Beaty & Alexeyev, 2008; Cook, Williams, Guerra, Kim & Sadek, 2010). However, it is also important to consider the bullies themselves. Therefore, this study focuses on bullies and bully-victims (i.e., those who are both a bully and a victim), and how they compare to students who are uninvolved in bullying.
Received 29 August 2011; revised 20 April 2012; accepted 23 April 2012. Address correspondence to K. Alex Burton, Department of Psychology, The University of Alabama, Box 870348, Tuscaloosa, AL 35487–0348. E-mail:
[email protected] 316
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Aggression and Cyberbullying
317
Traditional bullying has been conceptualized as a type of aggression repeatedly directed toward a person and is characterized by an imbalance of power (Olweus, 1993). Recent technological advances have led to the development of cyberbullying, a type of aggressive behavior in which the perpetrator repeatedly utilizes the Internet, computers, or other forms of technology to harass a victim (Patchin & Hinduja, 2006). Henceforth, we refer to traditional bullying as offline bullying and cyberbullying as online bullying. Evidence suggests that offline bullying is related to aggression (Camodeca, Goossens, Terwogt, & Schuengel, 2002; Ireland & Power, 2004; Machek, 2006), but because cyberbullying is a new phenomenon, the connection to aggression is relatively unexplored. Furthermore, research rarely distinguishes among the types of aggression bullies might exhibit. This study focuses on two main types of aggression in bullying: proactive and reactive (Dearing, Hubbard, McAuliffe, Morrow, & Rubin, 2007). Proactive aggression is usually planned with the goal of gaining something else; it is a means to an end (Fite, Colder, Lochman, & Wells, 2008). Unprovoked bullying behaviors are an example of proactive aggression where the gained reward might be attention, admiration, or status from peers. In contrast, reactive aggression is defense-driven and is the result of others’ behaviors, particularly if those behaviors are threatening or perceived as intentional; it is an end in itself (Fite et al., 2008). For example, reactive aggression might take the form of physical or verbal retaliation such as pushing or shouting toward an aggressor or bully. Thus, bullies can exhibit both proactive and reactive aggression.
OFFLINE BULLYING There are various participants in offline bullying such as the bullies, victims, bully-victims, and those who are uninvolved. Offline bullies, who often have high levels of aggression and are less prosocial and cooperative than others (Perren & Alsaker, 2006), are the active aggressors within the bullying process, and the victims are the recipients of the bully’s aggression. Offline bullying is highly comorbid with other problematic behaviors in youth. For example, offline bullies often use tobacco and intoxicants (Nansel et al., 2001). In addition, offline bullies are at a higher risk than other youth for behavioral problems and later problems in adulthood, potentially including criminal activity (Eliot & Cornell, 2009). Another type of bully is a bully-victim, who is both the recipient and perpetrator of bullying (Menesini, Modena, & Tani, 2009; Solberg, Olweus, & Endresen, 2007). Bully-victims often have the poorest outcomes in terms of social and emotional adjustment (Gradinger, Strohmeier, & Spiel, 2009; Nansel et al., 2001), the highest levels of aggression (Veenstra et al., 2005), and are bullied more than victims who do not bully (Dulmus, Sowers, &
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
318
K. A. Burton et al.
Theriot, 2006). In contrast, uninvolved students are those who do not participate, or are minimally involved, in bullying. These individuals often have better academic performance (Veenstra et al., 2005) and lower levels of aggression than offline bullies (Perren & Alsaker, 2006). Research suggests that aggression and bullying are interrelated. For example, offline bullies and offline bully-victims have been found to have high proactive and reactive aggression (Camodeca et al., 2002). In fact, offline bullying can be predicted by proactive and reactive aggressive traits (Machek, 2006). Typically, offline bullies have a higher degree of proactive aggression than other students (Pellegrini, Bartini, & Brooks, 1999), whereas both offline bullies and offline victims are more reactively aggressive than typical students (Camodeca & Goossens, 2005). Reactive aggression tends to predict future proactive aggression (Salmivalli & Helteenvuori, 2007), which suggests that engaging in one type of aggression could lead to the other, more harmful, displays of aggression. Given the link between offline bullying and aggression, it is important to determine if such a connection exists between aggression and online bullying as well.
ONLINE BULLYING Online bullying has several distinct features. First, the harm inflicted on the victim is psychological in nature rather than physical. Generally, the home, rather than the school or social setting, is the environment in which online bullying occurs (Dehue, Bolman, & Völlink, 2008). This environment allows online bullies the opportunity to engage in harassing behaviors without adult supervision at any time (Patchin & Hinduja, 2006). Another distinct feature of online bullying is anonymity, which results in an absence of accountability and identifiability (Nogami & Takai, 2008), and promotes antisocial behavior (Mathes & Guest, 1976). Although the identity of the bully is not always hidden from the victim, anonymity does play a role in some instances. For example, one study reported that over one fourth of online victims were unsure of the identity of their cyberbully (Juvonen & Gross, 2008). Essentially, the Internet and computer systems become environments in which masked aggression can thrive (Dehue et al., 2008). Online bullying is thus a phenomenon distinct and separate from offline bullying, although offline bullies are at risk to also engage in cyberbullying (Burton, Florell, & Wygant, 2013; Erdur-Baker, 2010). Online bullying seems widespread because of the overall ease of access adolescents have to new communication technology, which is easily used for verbal and other nonphysical forms of harassment (Abelman, 2007). These communication tools have options for copying and pasting material and offer quick access to personal information (Juvonen & Gross, 2008). Furthermore, online bullying does not require that a bully be at the same
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Aggression and Cyberbullying
319
location and time as the victim. This could result in a delay in the notification of the online bullying, so the victim is not immediately aware of the harassment. Put simply, the characteristics of online bullying, such as asynchrony and anonymity, provide an environment conducive for such behaviors. There is evidence of the relationship between aggression and online bullying that suggests aggression is closely linked to online bullying much like offline bullying. One study found that those who had higher normative beliefs about aggression were more likely to be online and offline bullies (Burton et al., 2013). The aim of this study is to further elucidate the relationship between online bullying and aggression by showing how group status (e.g., online bully-victim) relates to proactive and reactive aggression. Another study found that online bullying was significantly associated with proactive aggression (Calvete, Orue, Estevez, Villardon, & Padilla, 2010). However, in addition to examining how online bullying relates to proactive and reactive aggression, this study used a categorical approach to bullying and included an online and offline bully-victim group in the analyses, which expands on the findings of Calvete et al. (2010). Therefore, this study offers a novel contribution to the online bullying literature.
THIS STUDY The purpose of this study is to examine the relationship of proactive and reactive aggression to offline and online bullying. Because of the similarities between offline and online bullying, the predictions about proactive and reactive aggression for offline bullying will be the same as those for online bullying. It is predicted that offline uninvolved students will have significantly lower reactive and proactive aggression than offline bullies (Hypotheses 1 and 2) and offline bully-victims (Hypotheses 3 and 4). It is also predicted that online uninvolved students will have significantly lower reactive and proactive aggression than online bullies (Hypotheses 5 and 6) and online bully-victims (Hypotheses 7 and 8).
METHOD Participants Participants were 851 middle school students (337 males, 501 females, 13 unspecified) in Grades 6 through 8, ages 10 to 16 years old (M = 12.93, SD = 0.92). Participants completed a questionnaire that contained demographic information such as age, socioeconomic status (51.2% were on a free or reduced-price lunch program), gender, county and state of residence, ethnicity (91% Caucasian, 2.5% African American, 1.4% Hispanic, 1.2% American
320
K. A. Burton et al.
Indian, 1% Asian, and 2.4% unspecified), grade level, and grades typically received in school (the minority population in the sample corresponds with the U.S. Census for that rural area). Additionally, the questionnaire contained the offline bullying, online bullying, reactive aggression, and proactive aggression measures.
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Materials OFFLINE
BULLYING
This scale was adapted from previous research (Jolliffe & Farrington, 2006; Olweus, 1991; Whitney & Smith, 1993). The offline bullying scale (α = .813) contained seven statements such as “I spread rumors about someone” or “I physically hurt someone” to measure the student as the perpetrator of bullying behaviors. Participants rated the statements on a 5-point scale (1 = never, 2 = once or twice this year, 3 = a few times this year, 4 = about one time every week, 5 = about a few times every week) to assess the prevalence and frequency of offline bullying. The greater the score on this measure, the higher the frequency of bullying behaviors.
ONLINE
BULLYING
The online bullying scale (α = .894), which has been used in recent research (Ang & Goh, 2010), contains nine statements such as “I sent or posted cruel and untrue things about someone” and “I kept on sending someone rude and insulting messages” to measure the student as the online bully. Participants rated the statements on a 5-point scale (1 = never, 2 = once or twice this year, 3 = a few times this year, 4 = about one time every week, 5 = about a few times every week) to assess the prevalence and frequency of online bullying. The greater the score on this measure, the higher the frequency of online bullying behaviors (see the Appendix).
PROACTIVE
AND REACTIVE AGGRESSION
Participants also completed the Reactive Proactive Aggression Questionnaire, Child Version (RPQ–C; Raine et al., 2006) to measure reactive and proactive aggression among children and adolescents. Participants rated each statement such as “I yell at others when they annoy me” and “I have temper tantrums” on a 3-point scale (0 = never, 1 = sometimes, 2 = often). This measure yields a total score, in addition to a score for 12 items assessing proactive aggression (α = .80) and 11 items for reactive aggression (α = .82). For the purposes of this study the total score is not used, only the scores on the two subscales.
Aggression and Cyberbullying
321
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Procedure University institutional review board approval, school district, parental consent, and student assent were obtained before data collection. After contacting schools in six rural public school districts about participating, administrators collected consent forms from students they allowed to participate.1 The recruitment method varied among the schools, at the discretion of the school’s administrators, but all participants were given a brief summary of the purpose of the study prior to participation. In each school, participants were administered the questionnaires, which took approximately 40 minutes to complete, in a large group setting. Prior to the testing session, participants met at a designated area to complete the questionnaires and were seated at tables with at least two empty seats separating each student to reduce information sharing and response acquiescence. The investigator remained present for the entire session to answer any questions and, on completion of the instruments, students returned to their classrooms.
Offline and Online Bullying Groups Offline bullying and online bullying groups were designated using a method similar to Burton et al. (2013), Demaray and Malecki (2003), and Veenstra et al. (2005), which is based on a frequency of scores. The students who scored at or above the 75th percentile on the offline bullying or online bullying scales and at or below the 50th percentile on offline victimization or online victimization scales were designated as offline bullies or online bullies, respectively. Offline bully-victims were students who scored at or above the 75th percentile on both the offline bullying and victimization scales. Students who scored at or above the 75th percentile on both the online bullying and online-victimization scales were designated as online bully-victims. The uninvolved students were those who scored at or below the 50th percentile on victimization and bullying measures. It is important to note that those who are designated as offline bullies and offline bully-victims are not necessarily the same students as the online bullies and online bully-victims.
RESULTS A multivariate analysis of variance (MANOVA) was conducted to determine the effect of offline group status (offline bully, offline bully-victim, offline uninvolved) and online group status (online bully, online bully-victim, online uninvolved) on proactive and reactive aggression. The omnibus test revealed 1
Each school made the decision of which grade levels and classrooms were allowed to participate.
322
K. A. Burton et al.
TABLE 1 Mean Scores on Dependent Variables by Offline and Online Group Status Bully-victim
Uninvolved
Dependent variables
Offlinea
Onlineb
Offlinec
Onlined
Offlinee
Onlinef
Proactive aggression Reactive aggression
7.15 14.12
6.68 13.61
3.26 9.96
4.57 10.93
1.22 6.77
1.24 7.07
a
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Bully
n = 104. b n = 142. c n = 53. d n = 75. e n = 140. f n = 279.
significant main effects of offline group status, F(4, 570) = 7.48, p < .01, Wilks’s λ = .90, and online bullying group status, F(4, 570) = 4.86, p < .01, Wilks’s λ = .93. There was not a significant Offline Status × Online Status interaction effect. Follow-up univariate analyses revealed significant differences among offline groups on proactive aggression, F(2, 285) = 9.80, p < .01, and reactive aggression, F(2, 285) = 13.31, p < .01 (see Table 1 for descriptive statistics). A Tukey test of pairwise comparisons revealed significant differences among all three groups on proactive and reactive aggression. The offline bullyvictim group was significantly higher than the other two offline groups in both forms of aggression, and the offline bully group was also significantly higher than the offline uninvolved group in both forms of aggression. The results confirmed all of the hypotheses regarding offline bullying. Similar to the offline groups, follow-up univariate analyses revealed significant differences among online bullying groups on proactive aggression, F(2, 285) = 6.92, p < .01, and reactive aggression, F(2, 285) = 8.48, p < .01 (see Table 1 for descriptive statistics). A Tukey test of pairwise comparisons revealed significant differences among all three groups on proactive and reactive aggression. Replicating the earlier results, the online bully-victim group was significantly higher than the other two online groups in both forms of aggression, and the online bully group was also significantly higher than the online uninvolved group in both forms of aggression. The results confirmed all of the hypotheses regarding online bullying.
DISCUSSION The purpose of this study was to examine the differences in proactive and reactive aggression among types of bullies. In support of the predictions, the findings suggest that those uninvolved in online and offline bullying have significantly lower proactive and reactive aggression than bullies and bully-victims. It was also found that both online and offline bully-victims had higher proactive and reactive aggression than bullies. These results support past research that offline bully-victims have high levels of aggression
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Aggression and Cyberbullying
323
(Camodeca et al., 2002; Nansel et al., 2001) and contribute to the literature by demonstrating that online bully-victims also have the highest proactive and reactive aggression. This might provide some insight as to why bullyvictims have poor adjustment (Gradinger et al., 2009; Nansel et al., 2001; Veenstra et al., 2005) For offline bullies and offline bully-victims, this might suggest that extended involvement in subcultures of aggression (where bullying is a normative behavior) could result in higher levels of proactive and reactive aggression compared to uninvolved individuals, who have little experience with offline bullying. On the other hand, bully-victims, who are involved in bullying more often than others, had even higher aggression than bullies. Taken together, this provides support for the notion that involvement in bullying relates to proactive and reactive aggression, and that this aggression is more pronounced among bullies who are also victims of aggression. A more important aspect of this study regarded online bullying. As mentioned before, only one study has examined how online bullying relates to proactive and reactive aggression (Calvete et al., 2010). This study did not address differences in aggression in bully-victims though, nor did it demonstrate that online and offline bullying category membership relates to proactive and reactive aggression. Additionally, this study expanded on research that found a relationship between normative beliefs about aggression and online bullying (Burton et al., 2013) by showing that online bullying relates to aggression, rather than beliefs about aggression.
Limitations There are some limitations to this study that should be addressed before further research is conducted on the topic. The use of self-report measures (e.g., offline and online bullying scales) can be vulnerable to students responding in a socially desirable manner. This is particularly the case with bullying, as the perpetrators can sometimes find themselves in trouble for participating in these behaviors. Thus, one would expect not every instance of bullying to be accurately reported, which would underestimate the frequency of such behaviors (Eliot & Cornell, 2009; Seals & Young, 2003). Additionally, the sample was gathered in a rural setting and contained only a small number of minorities, so the findings might not generalize to urban or minority populations. The groups by which the bullies, bully-victims, and uninvolved students were designated are arbitrary; some students can be offline bullies and online bullies simultaneously. To be classified as a bully, one would have to be at or above the 75th percentile on the bullying scale. Those who bully frequently, yet fail to meet this cutoff, are still bullies in the sense of the word, but they are not designated as such in this study. Therefore, this study did not classify those who engage in bullying behaviors infrequently as bullies.
324
K. A. Burton et al.
Additionally, this study design does not provide information on potential causal relationships (and how those relationships are temporally ordered) between online bullying and aggression.
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Future Directions and Conclusions The findings demonstrate the need for additional research into differences between offline bullying and the more recent phenomenon of online bullying. Online bully-victims have a high risk of depression (Patchin & Hinduja, 2010) and decreased self-esteem (Wang, Nansel, & Iannotti, 2011), so they might have the worst outcomes of all those involved. Increased proactive and reactive aggression might also increase the likelihood of future involvement in online and offline bullying. Therefore, this study demonstrates the need for future research on online bullying, especially in its relationship to aggression-related constructs. An important aspect of this research is that it highlights the similarities between offline and online bullying. Such findings could be particularly useful in schools where this, and related online bullying research, could be utilized to inform students and teachers on ways of dealing with online bullying situations. Research has provided evidence of the effectiveness of offline bully intervention programs in increasing social competence, peer acceptance, and self-esteem (Merrell, Gueldner, Ross, & Isava, 2008). Programs such as these might be equally effective for online bullies. The two main types of aggression, proactive and reactive, might explain the cycle of online and offline bullying. Once norms involving aggression have been established, it might be difficult to change them. It would be interesting to see how online bullies and bully-victims enter this cycle. Longitudinal studies might help determine when and where the use of aggression online begins and the outcomes of the students who use it. This would provide additional insight into the origin and consequences of engaging in online bullying behaviors, as offline bullies tend to have increased risk for relationship problems later in life than others (Pepler, Craig, Jiang, & Connolly, 2008). Although this study only focused on the aforementioned bullying categories, three other categories have been outlined in the literature. Reinforcers work to assist and reinforce the bullying behaviors, whereas defenders attempt to neutralize the bullying and defend victims against it (Salmivalli, 2010; Salmivalli, Kaukiainen, & Voeten, 2005). Similar but distinct from the uninvolved students, outsiders are those who withdraw from bullying involvement by pretending to be unaware of it. Future research should address how these bullying categories relate to bullies, bully-victims, and those who are uninvolved in online bullying. In sum, this study has shown that proactive and reactive aggression is important to consider for online bullies in particular. Online bullying
Aggression and Cyberbullying
325
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
appears to be similar to offline bullying in the degree of proactive and reactive aggression. Furthermore, this research demonstrated that for online and offline bullying, bully-victims are particularly high in proactive and reactive aggression. This suggests that more research is needed to fully examine the ways in which online bullying is similar to, and differs from, offline bullying.
REFERENCES Abelman, R. (2007). Fighting the war on indecency: Mediating TV, internet, and videogame usage among achieving and underachieving gifted children. Roeper Review, 29, 100–112. Ang, R., & Goh, D. (2010). Cyberbullying among adolescents: The role of affective and cognitive empathy, and gender. Child Psychiatry and Human Development, 41, 387–397. Beaty, L., & Alexeyev, E. (2008). The problem of school bullies: What the research tells us. Adolescence, 43, 1–11. Burton, K., Florell, D., & Wygant, D. (2013). The role of peer attachment and normative beliefs about aggression on traditional bullying and cyberbullying. Psychology in the Schools, 50, 103–115. Calvete, E., Orue, I., Estevez, A., Villardon, L., & Padilla, P. (2010). Cyberbullying in adolescents: Modalities and aggressors’ profiles. Computers in Human Behavior, 26, 1128–1135. Camodeca, M., & Goossens, F. (2005). Aggression, social cognitions, anger and sadness in bullies and victims. Journal of Child Psychology & Psychiatry, 46, 186–197. Camodeca, M., Goossens, F., Terwogt, M., & Schuengel, C. (2002). Bullying and victimization among school-age children: Stability and links to proactive and reactive aggression. Social Development, 11, 332–345. Cook, C., Williams, K., Guerra, N., Kim, T., & Sadek, S. (2010). Predictors of bullying and victimization in childhood and adolescence: A meta-analytic investigation. School Psychology Quarterly, 25, 65–83. Dearing, K., Hubbard, J., McAuliffe, M., Morrow, M., & Rubin, R. (2007). Reactive and proactive aggression: Stability of constructs and relations to correlates. The Journal of Genetic Psychology, 164, 365–382. Dehue, F., Bolman, C., & Völlink, T. (2008). Cyberbullying: Youngsters’ experiences and parental perception. CyberPsychology & Behavior, 11, 217–223. Demaray, M., & Malecki, C. (2003). Perceptions of the frequency and importance of social support by students classified as victims, bullies, and bully/victims in an urban middle school. School Psychology Review, 32, 471–489. Dulmus, C., Sowers, K., & Theriot, M. (2006). Prevalence and bullying experiences of victims and victims who become bullies (bully-victims) at rural schools. Victims & Offenders, 1, 15–31. Eliot, M., & Cornell, D. (2009). Bullying in middle school as a function of insecure attachment and aggressive attitudes. School Psychology International, 30, 201–214.
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
326
K. A. Burton et al.
Erdur-Baker, Ö. (2010). Cyberbullying and its correlation to traditional bullying, gender and frequent and risky usage of internet-mediated communication tools. New Media & Society, 12, 109–125. Fite, P., Colder, C., Lochman, J., & Wells, K. (2008). Developmental trajectories of proactive and reactive aggression from fifth to ninth grade. Journal of Clinical Child & Adolescent Psychology, 37, 412–421. Gradinger, P., Strohmeier, D., & Spiel, C. (2009). Traditional bullying and cyberbullying: Identification of risk groups for adjustment problems. Zeitschrift für Psychologie/Journal of Psychology, 217, 205–213. Ireland, J., & Power, C. (2004). Attachment, emotional loneliness, and bullying behavior: A study of adult and young offenders. Aggressive Behavior, 30, 298–312. Jolliffe, D., & Farrington, D. (2006). Examining the relationship between low empathy and bullying. Aggressive Behavior, 32, 540–550. Juvonen, J., & Gross, E. (2008). Extending the school grounds? Bullying experiences in cyberspace. Journal of School Health, 78, 496–505. Machek, G. (2006). Defensive egotism, reactive aggression, proactive aggression, and bullying behavior in school children. Dissertation Abstracts International Section A, 67. Mathes, E., & Guest, T. (1976). Anonymity and group antisocial behavior. Journal of Social Psychology, 100, 257–262. Menesini, E., Modena, M., & Tani, F. (2009). Bullying and victimization in adolescence: Concurrent and stable roles and psychological health symptoms. Journal of Genetic Psychology, 170, 115–134. Merrell, K., Gueldner, B., Ross, S., & Isava, D. (2008). How effective are school bullying intervention programs? A meta-analysis of intervention research. School Psychology Quarterly, 23, 26–42. Nansel, T., Overpeck, M., Pilla, R., Ruan, W., Simons-Morton, B., & Scheidt, P. (2001). Bullying behaviors among U.S. youth: Prevalence and association with psychosocial adjustment. The Journal of the American Medical Association, 285, 2094–2100. Nogami, T., & Takai, J. (2008). Effects of anonymity on antisocial behavior committed by individuals. Psychological Reports, 102, 119–130. Olweus D. (1991). Bully/victim problems among school children; Basic facts and effects of a school based intervention program. In D. J. Pepler & K. H. Rubin (Eds.), The development and treatment of childhood aggression (pp. 411–448). Hillsdale, NJ: Erlbaum. Olweus, D. (1993). Bullying at school: What we know and what we can do. Cambridge, MA: Blackwell. Patchin, J., & Hinduja, S. (2006). Bullies move beyond the schoolyard: A preliminary look at cyberbullying. Youth Violence and Juvenile Justice, 4, 148–169. Patchin, J., & Hinduja, S. (2010). Cyberbullying and self-esteem. Journal of School Health, 80, 614–621. Pellegrini, A., Bartini, M., & Brooks, F. (1999). School bullies, victims, and aggressive victims: Factors relating to group affiliation and victimization in early adolescence. Journal of Educational Psychology, 91, 216–224. Pepler, D., Craig, W., Jiang, D., & Connolly, J. (2008). Developmental trajectories of bullying and associated factors. Child Development, 79, 325–338.
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Aggression and Cyberbullying
327
Perren, S., & Alsaker, F. (2006). Social behavior and peer relationships of victims, bully-victims, and bullies in kindergarten. Journal of Child Psychology & Psychiatry, 47, 45–57. Raine, A., Dodge, K., Loeber, R., Gatzke-Kopp, L., Lynam, D., Reynolds, C., et al. (2006). The reactive–proactive aggression questionnaire: Differential correlates of reactive and proactive aggression in adolescent boys. Aggressive Behavior, 32, 159–171. Salmivalli, C. (2010). Bullying and the peer group: A review. Aggression and violent behavior, 15, 112–120. Salmivalli, C., & Helteenvuori, T. (2007). Reactive, but not proactive aggression predicts victimization among boys. Aggressive Behavior, 33, 198–206. Salmivalli, C., Kaukiainen, A., & Voeten, M. (2005). Anti-bullying intervention: Implementation and outcome. British Journal of Educational Psychology, 75, 465–487. Seals, D., & Young, J. (2003). Bullying and victimization: Prevalence and relationship to gender, grade level, ethnicity, self-esteem, and depression. Adolescence, 38, 735–747. Solberg, M., Olweus, D., & Endresen, I. (2007). Bullies and victims at school: Are they the same pupils? British Journal of Educational Psychology, 77, 441–464. Veenstra, R., Lindenberg, S., Oldehinkel, A., De Winter, A., Verhulst, F., & Ormel, J. (2005). Bullying and victimization in elementary schools: A comparison of bullies, victims, bully/victims, and uninvolved preadolescents. Developmental Psychology, 41, 672–682. Wang, J., Nansel, T., & Iannotti, R. (2011). Cyber and traditional bullying: Differential association with depression. Journal of Adolescent Health, 48, 415–417. Whitney, I., & Smith, P. K. (1993). A survey of the nature and extent of bullying in junior/middle and secondary schools. Educational Research, 35, 3–25.
APPENDIX Online Bully Scale 1. I made fun of someone by sending/posting stories, jokes or pictures about him/her. 2. I purposely left someone out from an online group. 3. I sent or posted cruel and untrue things about someone 4. I entered or used someone’s e-mail, website, or computer without his/her permission. 5. I sent or posted messages to someone saying that I will hurt him/her. 6. I pretended to be someone else and sent/posted messages to get that person into trouble or make the person look bad. 7. I kept on sending someone rude and insulting messages. 8. I spread rumors or gossip about someone. 9. I tricked someone to provide me secret or embarrassing information and then I sent/posted it online to others.
328
K. A. Burton et al.
Downloaded by [University of Alabama at Tuscaloosa], [Kelly Burton] at 11:03 14 March 2013
Online Victimization Scale 1. Someone made fun of me by sending/posting stories, jokes, or pictures to describe me. 2. Someone purposely left me out of an online group. 3. Someone sent/posted cruel and untrue things about me 4. Someone entered or used my e-mail, website, or computer without my permission. 5. Someone sent or posted messages saying that he/she will hurt me. 6. A person pretended to be someone else and sent/posted messages to get me into trouble or made me look bad. 7. Someone kept sending me rude and insulting messages. 8. Someone spread rumors or gossip about me. 9. Someone tricked me to provide secret or embarrassing information and sent/posted it online to others. Have any of these things happened to you this year? Please circle the number that best describes you. 1 = Never 2 = Once or twice this year 3 = A few times this year 4 = About one time every week 5 = About a few times every week
