Festl, Ruth

Quandt, Thorsten

Diskurs Kindheits- und Jugendforschung / Discourse Journal of Childhood and Adolescence Research

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Cyberbullying at schools : a longitudinal research project

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www.ssoar.infoCyberbullying at schools: a longitudinal researchprojectFestl, Ruth; Quandt, ThorstenVeröffentlichungsversion / Published VersionZeitschriftenartikel / journal articleZur Verfügung gestellt in Kooperation mit / provided in cooperation with:Verlag Barbara BudrichEmpfohlene Zitierung / Suggested Citation:Festl, R., & Quandt, T. (2014). Cyberbullying at schools: a longitudinal research project. Diskurs Kindheits- undJugendforschung / Discourse. Journal of Childhood and Adolescence Research, 9(1), 109-114. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-404324Nutzungsbedingungen:Dieser Text wird unter einer CC BY-SA Lizenz (Namensnennung-Weitergabe unter gleichen Bedingungen) zur Verfügung gestellt.Nähere Auskünfte zu den CC-Lizenzen finden Sie hier:https://creativecommons.org/licenses/by-sa/4.0/deed.deTerms of use:This document is made available under a CC BY-SA Licence(Attribution-ShareAlike). For more Information see:https://creativecommons.org/licenses/by-sa/4.0Kurzbeitrag Diskurs Kindheits- und Jugendforschung Heft 1-2014, S. 109-114 Cyberbullying at schools: A longitudinal research project Ruth Festl, Thorsten Quandt    1 Introduction  Today, most of everyday life is permeated by information and communication technolo-gies (ICTs). This trend is true especially for children, adolescents, and young adults who grew up with digital media (see Dehm/Storll 2010) and form the group of so-called Digi-tal Natives (Prensky 2001). However, new (digital) opportunities also confront young us-ers with new forms of content-related and social challenges that can lead to risky or, in some cases, even deviant (online) behavior. Sexual harassment, unwanted exposure to pornography, and experiences of cyberbullying are some of the risks associated with ado-lescents’ Internet use (Livingstone et al. 2011). Public and scientific awareness of cyber-bullying has increased noticeably in recent years. Cyberbulling is defined as “an aggres-sive, intentional act carried out by a group or individual, using electronic forms of con-tact, repeatedly and over time against a victim who cannot easily defend him or herself” (Smith et al. 2008, p. 376). Researchers often fear more severe consequences for victims than inflicted by conventional bullying, because the specific technical attributes of cyber-bullying enable anonymous forms of perpetration that can reach a nearly unlimited (me-dia) audience (e.g. Heirman/Walrave 2008). Given these worries, there appears an urgent need to clarify which conditions and (social) constellations in particular support cyberbul-lying behavior. Although cyberbulling is not restricted to children and adolescents, much of the re-search focuses on this young group, especially high school students. For a number of rea-sons, this target group is particularly relevant when analyzing perpetration and victimiza-tion via the Internet. By definition (see Smith et al. 2008), all cyberbullying actions re-quire the use of ICTs – in most cases, the use of the Internet. Compared to older adults, Digital Natives are not only more media literate but also more intensively integrate ICTs into their everyday behavior. Additionally, aggressive or deviant behavior often appears or arises during adolescence due to developmental problems. Inappropriate coping with these developmental problems can result in the disruption of personality development, as well as in problematic internal and external behavior (Hurrelmann 2005, p. 160). Moreo-ver, cyberbullying always happens between at least two individuals in what constitutes a socio-structural component inherent in every bullying act. Although the technical features of the Internet principally enable a diffusion of the bullying contents to a nearly unlimited 110   R. Festl, T. Quandt: Cyberbullying at schools audience (Heirman/Walrave 2008; Slonje/Smith/Frisen 2012), cyberbullying often arises in stable contexts in which the participants know each other (Slonje/Smith/Frisen 2013). In this case, electronic means are used only as a channel and not as an amplifier directed at the broader digital world. Except for family, school and classes are the most stable so-cial contexts in adolescents’ life. They not only spend most of their time in school but al-so find a large portion of their social contacts in this setting. In previous studies, a psychological, individual-based perspective was the predomi-nant approach to analyzing cyberbullying behavior. The literature on conventional bully-ing in school, however, suggests that the socio-structural character of the phenomenon should also be considered. Group-based aspects, such as the social hierarchy and different forms of peer pressure, could be relevant factors of influence.  Therefore, through a large research project, we intend to expand the previous individ-ual-based findings with a socio-structural perspective and a longitudinal design that ena-bles identification of the dynamics and developmental traces of adolescents’ cyberbully-ing behavior. In this paper, we explain the basics of this research project and present the first descriptive results.  2 The current research project The current project is aimed at advancing basic research in the context of cyberbullying. Therefore, we analyze the phenomenon, specifically its influencing factors, patterns, and (longitudinal) effects on adolescents. We expand previous research on cyberbullying be-havior with three important innovations: First, a multi-level design has been implemented. To gain a holistic view, we have considered not only the personal attributes influencing cyberbullying behavior but also the structural and systematic factors. For the present project, the school context was cho-sen as the relevant setting for the investigation. Within the German school system, all stu-dents are assigned to particular school classes. These fixed social groups are characterized by numerous attributes, such as the inherent class climate, the actual behavior of students, and perceptions of the class’ teachers. Moreover, different aspects of the schools them-selves (e.g., level of education) could also be relevant to individual behavior. We, there-fore, expand the predominant, individualistic view of cyberbullying research to address these contextual levels of influence. Moreover, the socio-structural character of cyberbulling should be treated not only on its different (formal) levels of influence but also in a more content-oriented manner: In addition to ego-centered variables, this study applied social network analysis to explore the reconstruction of social structures. This method enabled analyzing whether individu-als’ cyberbullying behavior is also influenced by social position and vice versa. In addi-tion, we modeled different aspects of peer influence on individual behavior. Along with classmates’ attitudes and behaviors, we measured the influence of (close) friends. Finally, previous research on cyberbullying behavior is generally limited to cross-sectional data. Consequently, there are no empirical findings about the behavioral stabil-ity, long-term risk factors, or consequences of cyberbullying on adolescents’ personal and social life. To fill this gap, we are conducting a three-wave panel survey of students and Diskurs Kindheits- und Jugendforschung Heft 1-2014, S. 109-114   111 teachers integrating the innovations described earlier. This longitudinal perspective is employed in order to identify traces and dynamics of the cyberbullying process in school.  The project has been funded by the German Research Foundation (DFG) for 36 months and runs until September 2015. Research is conducted in cooperation of the Uni-versities of Münster and Hohenheim. In the following section, details of the sample and the cyberbullying instrument used are described. 3 Method 3.1 Participants We recruited participants from 33 schools representing the three tracks of education in Germany: lower (Hauptschule, 10 schools), middle (Realschule, 10 schools), and higher education (Gymnasium, 13 schools). Ministerial and parental consent was obtained for all participants before administering the survey. For the first wave of the study, 5,656 stu-dents filled out a questionnaire during lessons in school. The participants were 50.3 per-cent female with an average age of 13.9 (SD = 1.3). They came from 303 classes: 46 from the lower track (15.2%), 105 from the middle (34.7%), and 152 from the higher (50.1%). The classes had an average size of 15 participating students (SD = 5.6), with a maximum of 26. 3.2 Measuring cyberbullying behavior Previous studies either used a definition- or behavior-based measure of cyberbulling (e.g. Sawyer/Bradshaw/O’Brennan 2008). The former was often found to be problematic in terms of social desirability, while questions about concrete behavior tend to generate higher prevalence estimations (Sawyer/Bradshaw/O’Brennan 2008). Therefore, for the present project, we used both methods: First, we introduced brief definitions of bullying and cyberbullying, including behavioral examples. Next, the actual cyberbullying behav-ior was measured based on its variants. We asked students about 11 behaviors or experi-ences over the past 12 months. Students rated their answers on a frequency scale—0 (“never”), 1 (“once”), 2 (“occasionally”), and 3 (“often”). Through this approach, we could fulfill the definitional criteria of repetition put forth by Smith et al. (2008, see the introduction). Six items referred to the perpetration of cyberbullying, and five to forms of victimization. A student who answered at least one of the six perpetrator items with the response of “occasionally” or “often” was classified as a perpetrator. The same procedure was employed for the victim category. A student who indeed scored as both a perpetrator and a victim was categorized as a perpetrator/victim. 112   R. Festl, T. Quandt: Cyberbullying at schools 4 First results 4.1 Cyberbullying prevalence To analyze the diffusion of cyberbullying within our sample, we first looked at the behav-ioral items. Sending insulting messages was the method most commonly used to harm an-other person, followed by forwarding personal information to others (see Figure 1). Alto-gether, 21.8 percent of students agreed with at least one of the perpetrator items.  Fig. 1: Agreement with perpetrator items (at least agreed “occasionally”)   Receiving insulting messages (12.6%) was also the form of cyberbullying most common-ly experienced by victims, followed by having personal information forwarded (8.0%), rumors being spread on the Internet (7.3%), receiving messages from a person with a fake identity (6.5%), and having embarrassing pictures or videos of oneself uploaded (3.3%). Of the participants, 22.5 percent said they had been victimized in one of these ways. Constructing behavioral roles based on behaviors of perpetration and victimization, we identified three categories: bullies who have not been cyberbullied themselves in the past 12 months (N = 627; 11.1%), victims who have not have bullied someone else during the past year (N = 602; 11.7%), and perpetrator/victims who experienced both behaviors within this time frame (N = 660; 10.6%). One third of students (N = 1889; 33.4%) had been involved with cyberbullying within the last year. 0,02,04,06,08,010,012,014,016,0Send insulting messagesWrite insulting things on apublic websiteUpload embarissingpictures of someoneWrite messages using afake identitySpread rumors in theInternetForward personalinformation to othersDiskurs Kindheits- und Jugendforschung Heft 1-2014, S. 109-114   113 4.2 Cyberbullying and sociodemographic aspects We found interesting differences in cyberbullying behavior among socio-demographic groups (see Table 1). Male students were most often categorized as perpetrator/victims (11.8% versus female: 9.4%; Χ2 = 4.0; p < .05) or pure victims (12.6% versus female: 10.9%; Χ2 = 8.8; p < .01), while there were no gender differences among perpetrators. In addition, older students were generally more often involved in cyberbullying. Adolescents who exclusively or also cyberbullied others, in particular, were most often found in the oldest age group of age 16 and above (P: 14.2%; P/V: 18.3%). Among all three cyberbul-lying groups, the percentage of affected students was slightly positively correlated with their age (P: r = .09, p <.01; P/V: r = .12, p < .01; V: r = .05, p < .01). Finally, regarding students’ education, we identified significant differences only among adolescents who had already experienced both perpetration and victimization. Significantly more perpetra-tor/victims were found in lower-track schools (20.7%) than in middle- (11.6%) and high-er-track schools (8.5%). The same was true for the victims of cyberbullying.  Tab. 1: Cyberbullying behavior by socio-demographic groups   All Sex Age School Type   ♂ ♀ 11–13 14–15 16+ LE ME HE Perpetrator (P) 11.1 11.2 11.0   8.1 12.9 14.2 11.9 11.1 10.9 Perpetrator/ Victim (P/V) 10.6 11.8   9.4   7.2 12.3 18.3 20.7 11.6   8.5 Victim (V) 11.7 12.6 10.9 10.6 11.5 16.6 14.2 11.2 11.5 LE = Lower education, ME = Middle education, HE = Higher education. Significant differences were identified for the following variables: P: age 11–13 and 14–15, 11–13 and 16+; P/V: all differences; V: gender; age 11–13 and 16+, 14–15 and 16+, school type LE and ME, LE and HE. 5 Conclusion and Outlook In this paper, we presented the first results of the research project “Cyberbullying at Schools”. Using a comprehensive survey study, we showed that cyberbullying is a wide-spread phenomenon among German students. One-third of participants had already been involved in cyberbullying during the last year. Although the data were not representative of all German students, the selected 33 schools in the Southwest included different types of education level and a variety of other criteria, such as urban and rural settings. The de-scriptive findings further revealed some interesting hints about the diffusion of cyberbul-lying behavior within different socio-demographic groups. Male students were most often found in the groups that had been victimized via the Internet. This finding seems to con-flict with previous results that found male adolescents more likely to be perpetrators (De-hue/Bolman/Völlink 2008; Li 2006; Smith et al. 2008) and female adolescents to be cybervictims (Dehue/Bolman/Völlink 2008; Smith et al. 2008). In general, involvement in cyberbullying was higher among older students, perhaps due to the higher and often more self-contained Internet use usually practiced by older age groups (see Medienpädagog-ischer Forschungsverbund Südwest 2012). Finally, we found a clearly higher prevalence 114   R. Festl, T. Quandt: Cyberbullying at schools of perpetrator/victims in lower-track schools, while pure perpetrators and victims did not differ by school type. Lower-track schools, therefore, seem to be characterized by a cli-mate in which cyberbullying is accepted as a typical behavioral response. A student who bullies other students can expect the victims to seek revenge. It is assumed that these be-havioral patterns are barely interrupted and foster a general climate of aggression in these schools. It must be kept in mind that these first findings are only descriptive and need to be confirmed in more comprehensive studies which, for example, also control for students’ Internet use and competence. As mentioned, these aspects of media use might be an alter-native explanation for the relationship between age and cyberbullying. Therefore, we will next apply a multilevel analysis to the cross-sectional data to handle the hierarchical structure of the (school) data. In addition, socio-structural predictors, such as social posi-tion and aspects of peer influence, will be covered in more complex investigations. In the next two years, we will collect longitudinal data to identify long-term risk factors and the resulting consequences for adolescents’ life to better understand the phenomenon. In the long term, these results will help to develop appropriate prevention and intervention strat-egies that meet the criteria needed to fight cyberbullying at schools.   References Dehm, U./Storll, D. (2010): Medien und Tabus. Media Perspektiven, 9, pp. 410-431. Dehue, F./Bolman, C./Völlink, T. (2008): Cyberbullying: Youngsters’ Experiences and Parental Percep-tion. CyberPsychology & Behavior, 11, 2, pp.17-223. Heirman, W./Walrave, M. (2008): Assessing Concerns and Issues about the Mediation of Technology in Cyberbullying. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 2, 2. Online verfügbar unter: http://cyberpsychology.eu/view.php?cisloclanku=2008111401&article=1, Stand: 31.12.2013.  Hurrelmann, K. (2005): Lebensphase Jugend. Eine Einführung in die sozialwissenschaftliche Jugendfor-schung. – Weinheim, München. Li, Q. (2006): Cyberbullying in Schools: A Research of Gender Differences. School Psychology Interna-tional, 27, 2, pp. 157-170. Livingstone, S./Haddon, L./Görzig, A./Olafsson, K. (2011): Risks and safety on the internet: The perspec-tive of European children. Full Findings. – London. Medienpädagogischer Forschungsverbund Südwest (Hrsg.) (2012): JIM-Studie 2012: Jugend, Informati-on, (Multi-)Media. Basisuntersuchung zum Medienumgang 12- bis 19-Jähriger. Online verfügbar unter http://www.mpfs.de/fileadmin/JIM-pdf12/JIM2012_Endversion.pdf, Stand: 05.11.2012. Prensky, M. (2001): Digital Natives, Digital Immigrants Part 1. On the Horizon, 9, 5, pp. 1-6. Sawyer, A. L./Bradshaw, C. P./O’Brennan, L. M. (2008): Examining Ethnic, Gender, and Developmental Differences in the Way Children Report Being a Victim of “Bullying” on Self-Report Measures. Journal of Adolescent Health, 43, pp. 106-114. Slonje, R./Smith, P. K./Frisen, A. (2012): Processes of cyberbullying, and feeling of remorse by bullies: A pilot study. European Journal of Developmental Psychology, 9, 2, pp. 244-259. Slonje, R./Smith, P. K./Frisen, A. (2013): The nature of cyberbullying, and strategies for prevention. Computer in Human Behavior, 29, pp. 26-32. Smith, P. K./Mahdavi, J./Carvalho, M./Fisher, S./Russell, S. (2008): Cyberbullying – its nature and im-pact in secondary school pupils. Journal of Child Psychology and Psychiatry, 49, 4, pp. 376-385.   

Cyberbullying at schools: a longitudinal research project

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Cyberbullying at schools: a longitudinal research project

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