Session Information
05 ONLINE 00 PS, General Poster Session (online) - NW 05
General Poster Session
Contribution
Cyberbullying has received substantial attention in the last ten years. With the advances of modern technology devices (e.g. mobile phones) and social networks that have become a staple in the daily lives of adolescents, it has been made possible for bullying perpetration and victimization to extend beyond the school environment. Moreover, cyberbullying can undertake various forms (e.g. sending text or picture messages, making embarrassing short videos of a student, using another students’ social network account). Thus, the bully and victim do not have to be in the same physical location, bullying can occur at any time of day, spread very quickly, and has the possibility of inviting a larger audience compared to traditional forms (e.g. physical, verbal, relational). Furthermore, as students can have different roles in the occurrences of cyberbullying, it is important to consider the orthogonal perspective (Menesini & Salmivalli, 2017) which establishes two interrelated dimensions: a) cyberbullying and b) cybervictimization, and students can score high and/or low on both dimensions. On this basis, four participant roles can be established when cyberbullying perpetration occurs: the cyberbully (the perpetrator of a cyberbullying occurrence), cybervictim (a student that is a victim of the cyberbullying occurrence), cyberbully-victim (a student that scores high in both dimensions and transitions from either role depending on the situation), and uninvolved students (students that score low on both dimensions and are not participating in cyberbullying occurrences). In the present study, we are taking a look at how students in the four cyberbullying roles (cyberbully, cybervictim, cyberbully-victim, uninvolved) differ according to their psychosocial characteristics. For this purpose, we have made a distinction between two types of psychosocial characteristics: a) the individual characteristics of students (i.e. empathy, anger expression) and b) characteristics related to student peer networks (self-perceived popularity, perceived peer support). Previous research has already established some associations between cyberbullying perpetration, cybervictimization, and the mentioned psychosocial characteristics of students. For example, cybervictims are higher in empathy (Arato et al., 2020; van Cleemput et al., 2014), while lower empathy is associated with cyberbullying perpetration (Brewer & Kerslake, 2015; Casas et al., 2013). Students’ feelings of anger and anger expression are present in cyberbullies (Kowalski et al., 2014; Wang et al., 2017), as well as cybervictims (Aricak & Ozbay, 2016; Kowalski et al., 2014). Studies (Calvete et al., 2010; Williams & Guerra, 2007) report lower perceived social support from peers is related to cyberbullying perpetration, while the lack of peer support has also been reported as a risk factor for cybervictimization (Baldry et al., 2015; Kowalski et al., 2014). Concerning perceived popularity, studies suggest the least popular students are involved in cyberbullying perpetration (Ciucci & Baroncelli, 2014), while cybervictims can have varying popularity levels (Malamut et al., 2021). However, more research is needed to understand the association between popularity and cyberbullying.
Although the mentioned studies have established associations between psychosocial characteristics, cyberbullying perpetration, and cybervictimization, the present study addresses a gap in the literature by focusing on students in Slovenia, as well as performing analyses on a relatively large sample of students. Thus, the present study aims to compare psychosocial characteristics of groups of participants in cyberbullying (cyberbully, cybervictim, cyberbully-victim, uninvolved) according to the degree of cybervictimization and cyberbullying perpetration, as well as to determine differences between them.
Method
Participants: The study is part of a project on bullying among adolescents in Slovenia. The sample consists of 1926 adolescents (59.8 % females) aged between 13 and 20 years (M = 15,52; SD = 1,49) from 20 primary and secondary schools. Participants were divided into four roles with the final sample consisting of 755 participants (56.4 % females): cyberbullies (13.1 %; N = 99), cybervictims (14 %; N = 106); cyberbully-victims (31 %; N = 234), and uninvolved (41.9 %; N = 316). Instruments: Revised Adolescent Peer Relations Instrument (RAPRI-BT, Griezel et al., 2012) measured cyberbullying perpetration and cybervictimization in the last school year with 26 items on a 6-point Likert scale (1-never to 6-everyday). Reliability for the subscales were 0.88 and 0.89 in our sample. Basic Empathy Scale (BES, Jolliffe & Farrington, 2006) measured affective and cognitive empathy with 20 items on a 5-point Likert scale (1-absolutely disagree to 5-absolutely agree). Reliability for affective and cognitive empathy was 0.80 and 0.79. Anger Expression Index (AEI-A, Parada, 2000) was used to measure anger internalization, anger externalization, and anger control with 12 items on a 6-point Likert scale (1-never to 6-always). Reliability for the three subscales was 0.76, 0.67, and 0.85. Classroom Life Instrument (CLI, Johnson et al., 1983) measured perceived social support from peers with 5 items on a 5-point Likert scale (1- never true to 5-always true). Reliability was 0.87. Self-perceived popularity was measured by a single item “Evaluate based on your opinion, how popular you are among your classmates in the classroom?” on a 5-point Likert scale (1- a lot less than most to 5- a lot more than most). Procedure: The study was awarded ethical approval by the Faculty of Arts at the University of Maribor. Data collection was undertaken by using the paper and pencil method. Students were given an oral definition of cyberbullying before filling out the questionnaire battery. Analysis: IBM SPSS 27 was used for statistical analyses. Students were divided into one of four groups based on a percentile criterion: cyberbullies scored >80th percentile on the cyberbullying scale and <60th percentile on cybervictimization scale; cybervictims scored >80th percentile on cybervictimization and <60th percentile on cyberbullying, cyberbully-victims scored >80th percentile on both cyberbullying and cybervictimization, and uninvolved students scored <20th percentile on both scales. Descriptive statistics were computed and the results are based on multivariate analysis of variance (MANOVA).
Expected Outcomes
For assessing the differences in psychosocial characteristics of groups of participants in cyberbullying (i.e. cyberbullies, cybervictims, cyberbully-victims and uninvolved students) two MANOVA’s were conducted as all assumption for the analyses were met. Firstly, findings show that there was a significant effect of cyberbullying participant roles on the combined dependent variables measuring individual students’ characteristics (affective empathy, cognitive empathy, anger internalization, anger externalization, anger control) Wilks’ Λ = 0.821; F(15, 2063) = 10.200; p = 0.00; η2 = 0.064. Moreover, differences between participant groups were found on the following dependent variables: affective empathy F(3, 751) = 5.580; p = 0.00; η2 = 0.022, anger internalization F(3, 751) = 16.318; p = 0.00; η2 = 0.061, anger externalization F(3, 751) = 35.084. p = 0.00; η2 = 0.123, anger control F(3, 751) = 14.729; p = 0.00; η2 = 0.056, but not cognitive empathy F(3, 751) = 2.488; p = 0.06; η2 = 0.010. Secondly, there was a significant effect found of the cyberbullying roles on the combined dependent variables measuring students’ characteristics related to peer networks (perceived peer support, self-perceived popularity) Wilks’ Λ = 0.844; F(6, 1500) = 22.080; p = 0.00; η2 = 0.081. Moreover, differences between participant groups were found on both dependent variables: self-perceived popularity F(3, 751) = 20.360; p = 0.00; η2 = 0.075 and perceived peer support F(3, 751) = 17.763; p = 0.00; η2 = 0.066. Results show cyberbullies, cybervictims, cyberbully-victims and uninvolved students to differ amongst each other based on psychosocial characteristics. Cyberbullying prevention programs should take the mentioned students’ characteristics into account when planning interventions. Implication for theory and practice in the EU area will be examined.
References
Arató, N., Zsidó, A. N., Lénárd, K., & Lábadi, B. (2020). Cybervictimization and cyberbullying: The role of socio-emotional skills. Frontiers in Psychiatry, 11, 248. doi: 10.3389/fpsyt.2020.00248 Aricak, O. T., & Ozbay, A. (2016). Investigation of the relationship between cyberbullying, cybervictimization, alexithymia and anger expression styles among adolescents. Computers in Human Behavior, 55, 278–285. doi: 10.1016/j.chb.2015.09.015 Baldry, A. C., Farrington, D. P., & Sorrentino, A. (2017). School bullying and cyberbullying among boys and girls: Roles and overlap. Journal of Aggression, Maltreatment & Trauma, 26(9), 937–951. doi: 10.1080/10926771.2017.1330793 Brewer, G., & Kerslake, J. (2015). Cyberbullying, self-esteem, empathy and loneliness. Computers in Human Behavior, 48, 255–260. doi: 10.1016/j.chb.2015.01.073 Calvete, E., Orue, I., Estevez, A., Villardon, L., & Padilla, P. (2010). Cyberbullying in adolescents: Modalities and aggressors’ profile. Computers in Human Behavior, 26, 1128–1135. doi: 10.1016/j.chb.2010.03.017 Casas, J. A., Del Rey, R., & Ortega-Ruiz, R. (2013). Bullying and cyberbullying: Convergent and divergent predictor variables. Computers in Human Behavior, 29, 580–587. doi: 10.1016/j.chb.2012.11.015 Ciucci, E., & Baroncelli, A. (2014). Emotion-Related personality traits and peer social standing: Unique and interactive effects in cyberbullying behaviors. Cyberpsychology, Behavior, and Social Networking, 17(9), 584–590. doi: 10.1089/cyber.2014.0020 Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073–1137. doi: 10.1037/a0035618 Malamut, S. T., Dawes, M., van den Berg, Y., Lansu, T. A., Schwartz, D., & Cillessen, A. H. (2021). Adolescent victim types across the popularity status hierarchy: Differences in internalizing symptoms. Journal of Youth and Adolescence, 50(12), 2444-2455. doi: 10.1007/s10964-021-01498-w Menesini, E., & Salmivalli, C. (2017). Bullying in schools: The state of knowledge and effective interventions. Psychology, Health & Medicine, 22(sup1), 240–253. https://doi.org/10.1080/13548506.2017.1279740 Van Cleemput, K., Vandebosch, H., & Pabian, S. (2014). Personal characteristics and contextual factors that determine “helping”, “joining in”, and “doing nothing” when witnessing cyberbullying. Aggressive Behavior, 40(5), 383–396. doi: 10.1002/ab.21534 Wang, X., Yang, L., Yang, J., Wang, P., & Lei, L. (2017). Trait anger and cyberbullying among young adults: A moderated mediation model of moral disengagement and moral identity. Computers in Human Behavior, 73, 519–526. doi: 10.1016/j.chb.2017.03.073 Williams, K. R., & Guerra, N. G. (2007). Prevalence and predictors of internet bullying. Journal of Adolescent Health, 41, S14–S21. doi: 10.1016/j.jadohealth.2007.08.018
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