The Transition of Online Aggressive Behavior among College Students: A Latent Transition Analysis

Jin Tonglin; Wu Yuntena; Zhang Lu; Lei Zeyu; Jia Yanru

doi:10.16719/j.cnki.1671-6981.20240313

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Journal of Psychological Science ›› 2024, Vol. 47 ›› Issue (3) : 614-621. DOI: 10.16719/j.cnki.1671-6981.20240313

Developmental & Educational Psychology

The Transition of Online Aggressive Behavior among College Students: A Latent Transition Analysis

Jin Tonglin^1,2, Wu Yuntena^1,2, Zhang Lu¹, Lei Zeyu¹, Jia Yanru¹

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Abstract

With the rapid development of information technology, the online aggressive behavior associated with the use of the Internet has attracted the attention of society and academia. Existing studies have shown that the incidence of online aggressive behavior among Chinese college students is 59.5%, and that of foreign college students is 49.7%. Recently, this phenomenon among college students has become more rampant. Therefore, it is critical to focus on how online aggressive behavior among college students develop over time and the factors that influence this development pattern. However, most of the existing research on online aggressive behavior is cross-sectional study from variable-centered perspective, which limits our understanding of development pattern of this deviant behavior. Thus, we aim to explore the development pattern of the college students' online aggressive behavior by using longitudinal data and latent transition analysis methods from a person-centered perspective. Furthermore, previous literature has shown that there are some factors that can predict online aggressive behavior, such as age, gender, moral disengagement, and the experience of being left at home in childhood, but the related mechanism is still unclear. Thus, we further explore the factors which influence the development pattern of college students' online aggressive behavior.
We conducted a 2-wave longitudinal study with a 4-month interval. A total of 2000 (Mage = 19.39 years; 45.73% male) college students from 7 universities took a survey including the Online Aggressive Behavior Scale (OABS) and Moral Disengagement Questionnaire (MDQ) at two time points. SPSS25 and Mplus8.3 were used for data processing. Statistical methods include independent sample t test, latent class analysis and latent transition analysis, etc.
The results showed that: (1) There was a significant difference in the rate of college students' online aggressive behavior at the two points in time; (2) According to the conditional probabilities of the two groups in each item, the students were categorized as "low-aggressive group" and "high-aggressive group", and the proportion of the two groups changed with time were 86.6%, 91.0% and 13.4%, 9.0% respectively; (3) The latent transition analysis showed that the rate of the low- aggressive group to the high-aggressive group was 6.7%, and the rate of the high-aggressive group to the low-aggressive group was 35.4%; (4) Taking the low-aggressive group as the reference group, multivariate logistic regression analysis showed that compared with male students, the odds ratio of high-aggressive group for female students was .66. Age had no significant effect on high- aggressive groups. The odds ratio of the high-aggressive group was 5.43 for every 1 unit increase in the level of moral disengagement; (5) Taking college students who maintained their original latent status as the reference group, the results showed that the rate of female students transformed from low-aggressive group to high-aggressive group decreased (OR=.62). Under the influence of moral disengagement, the number of people who transformed from low-aggressive group to high- aggressive group increased (OR=5.53).
In short, it has been found that all the college students who exhibit online aggressive behavior can be divided into high-aggressive group and low-aggressive group, and the students in high-aggressive group demonstrates a trend to low-aggressive group easily with time. In addition, gender and moral disengagement are key influencing factors of the classification and transition of college students’ online aggressive behavior. The present study provided an insight to decrease the college students’ online aggressive behavior in practice, including reducing moral disengagement level, promoting mental health level, etc.

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college students / online aggressive behavior / latent class analysis / latent transition analysis

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Jin Tonglin, Wu Yuntena, Zhang Lu, Lei Zeyu, Jia Yanru. The Transition of Online Aggressive Behavior among College Students: A Latent Transition Analysis[J]. Journal of Psychological Science. 2024, 47(3): 614-621 https://doi.org/10.16719/j.cnki.1671-6981.20240313

References

[1] 胡阳, 范翠英. (2013). 青少年网络欺负行为研究述评与展望. 中国特殊教育, 5, 82-87.
[2] 金童林. (2018). 暴力暴露对大学生网络攻击行为的影响: 反刍思维与网络道德的作用 (硕士学位论文). 哈尔滨师范大学.
[3] 金童林, 陆桂芝, 张璐, 金祥忠, 王晓雨. (2017). 特质愤怒对大学生网络攻击行为的影响: 道德推脱的作用. 心理发展与教育, 33(5), 605-613.
[4] 金童林, 陆桂芝, 张璐, 闫萌智, 刘艳丽. (2016). 人际需求对大学生网络偏差行为的影响: 社交焦虑的中介作用. 中国特殊教育, 9, 84-89.
[5] 金童林, 乌云特娜, 张璐, 李鑫, 刘振会. (2020). 儿童期心理虐待对青少年网络欺负的影响: 领悟社会支持及性别的调节作用. 心理科学, 43(2), 323-332.
[6] 刘慧瀛, 何季霖, 胡悦, 王婉, 李恒涛. (2017). 大学生网络欺负与心理症状、网络社会支持和心理弹性的关系. 中国心理卫生杂志, 31(12), 988-993.
[7] 刘文, 刘红云, 李宏利. (2015). 儿童青少年心理学前沿. 浙江教育出版社..
[8] 庞锐, 彭娟. (2018). 我国有留守经历大学生心理健康状况meta分析. 实用预防医学, 25(4), 467-469.
[9] 王碧瑶, 张敏强, 张洁婷, 胡俊. (2015). 基于转变矩阵描述的个体阶段性发展: 潜在转变模型. 心理研究, 8(4), 36-43.
[10] 王兴超, 杨继平. (2010). 中文版道德推脱问卷的信效度研究. 中国临床心理学杂志, 18(2), 177-179.
[11] 吴鹏, 刘华山, 陈京军, 谢继红. (2014). 攻击性初中生的类别转变: 潜在转变分析. 心理科学, 37(5), 1167-1173.
[12] 吴鹏, 王杨春子, 刘华山. (2019). 初中生网络欺负的发展趋势: 道德推脱、观点采择与共情关注的作用. 心理科学, 42(5), 1098-1105.
[13] 杨继平, 王兴超, 高玲. (2010). 道德推脱的概念、测量及相关变量. 心理科学进展, 18(4), 671-678.
[14] 杨雪岭, 冯现刚, 崔梓天. (2014). 大学生的留守经历与心理韧性、心理病理症状. 中国心理卫生杂志, 28(3), 227-233.
[15] 翟友华. (2019). 大学生特质愤怒、反刍思维对网络攻击行为的影响及其干预 (硕士学位论文). 哈尔滨工程大学.
[16] 詹启生, 武艺. (2016). 留守经历大学生家庭教养方式对情绪调节策略的影响: 亲子沟通的中介作用. 中国特殊教育, 10, 40-46.
[17] 张春阳, 徐慰. (2020). 大学生留守经历与攻击性: 安全感与自卑感的链式中介作用. 中国临床心理学杂志, 28(1), 173-177.
[18] 张璐, 刘丽红, 金童林, 贾彦茹. (2017). 大学生特质愤怒在儿童期心理虐待和网络攻击行为关系中的中介作用. 中国心理卫生杂志, 31(8), 659-664.
[19] 赵锋, 高文斌. (2012). 少年网络攻击行为评定量表的编制及信效度检验. 中国心理卫生杂志, 26(6), 439-444.
[20] 赵卫国, 王奕丁, 姜雯宁, 李鑫辉. (2020). 越轨同伴交往与男性犯罪青少年攻击行为的关系: 一个有调节的中介模型. 中国特殊教育, 11, 62-69.
[21] 周浩, 龙立荣. (2004). 共同方法偏差的统计检验与控制方法. 心理科学进展, 12(6), 942-950.
[22] 朱晓伟, 周宗奎, 褚晓伟, 雷玉菊, 范翠英. (2019). 从受欺负到网上欺负他人: 有调节的中介模型. 中国临床心理学杂志, 27(3), 492-496.
[23] Archer, J. (2004). Sex differences in aggression in real-world settings: A meta-analytic review. Review of General Psychology, 8(4), 291-322.
[24] Bakioğlu, F., & Eraslan Çapan, B. (2019). Moral disengagement and cyber bullying, a mediator role of emphatic tendency. International Journal of Technoethics, 10(2), 22-34.
[25] Barboza, G. E. (2015). The association between school exclusion, delinquency and subtypes of cyber- and F2F-victimizations: Identifying and predicting risk profiles and subtypes using latent class analysis. Child Abuse and Neglect, 39, 109-122.
[26] Cénat J. M., Blais M., Lavoie F., Caron P. O., & Hébert M. (2018). Cyberbullying victimization and substance use among Quebec high schools students: The mediating role of psychological distress. Computers in Human Behavior, 89, 207-212.
[27] Chang Q. S., Xing J. L., Ho R. T. H., & Yip, P. S. F. (2019). Cyberbullying and suicide ideation among Hong Kong adolescents: The mitigating effects of life satisfaction with family, classmates and academic results. Psychiatry Research, 274, 269-273.
[28] Collins, L. M., & Wugalter, S. E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27(1), 131-157.
[29] Fanti K. A., Demetriou A. G., & Hawa V. V. (2012). A longitudinal study of cyberbullying: Examining risk and protective factors. European Journal of Developmental Psychology, 9(2), 168-181.
[30] Festl R., Vogelgesang J., Scharkow M., & Quandt T. (2017). Longitudinal patterns of involvement in cyberbullying: Results from a latent transition analysis. Computers in Human Behavior, 66, 7-15.
[31] Heiman, T., & Olenik-Shemesh, D. (2016). Computer-based communication and cyberbullying involvement in the sample of Arab teenagers. Education and Information Technologies, 21(5), 1183-1196.
[32] Killer B., Bussey K., Hawes D., & Hunt C. (2019). A meta-analysis of the relationship between moral disengagement and bullying roles in youth. Aggressive Behavior, 45(4), 450-462.
[33] Kiriakidis, S. P., & Kavoura, A. (2010). Cyberbullying: A review of the literature on harassment through the internet and other electronic means. Family and Community Health, 33(2), 82-93.
[34] Kokkinos C. M., Antoniadou N., & Markos A. (2014). Cyber-bullying: An investigation of the psychological profile of university student participants. Journal of Applied Developmental Psychology, 35(3), 204-214.
[35] Kowalski R. M., Giumetti G. W., Schroeder A. N., & Lattanner M. R. (2012). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073-1173.
[36] Mishna F., Khoury-Kassabri M., Gadalla T., & Daciuk J. (2012). Risk factors for involvement in cyber bullying: Victims, bullies and bully-victims. Children and Youth Services Review, 34(1), 63-70.
[37] Muñoz-Fernández, N., & Sánchez-Jiménez, V. (2020). Cyber-aggression and psychological aggression in adolescent couples: A short-term longitudinal study on prevalence and common and differential predictors. Computers in Human Behavior, 104, Article 106191.
[38] Ouvrein G., De Backer, C. J. S., & Vandebosch H. (2018). Online celebrity aggression: A combination of low empathy and high moral disengagement? The relationship between empathy and moral disengagement and adolescents' online celebrity aggression. Computers in Human Behavior, 89, 61-69.
[39] Quintana-Orts, C., & Rey, L. (2018). Forgiveness and cyberbullying in adolescence: Does willingness to forgive help minimize the risk of becoming a cyberbully? Computers in Human Behavior, 81, 209-214.
[40] Schultze-Krumbholz A., Göbel K., Scheithauer H., Brighi A., Guarini A., Tsorbatzoudis H., & Smith P. K. (2015). A comparison of classification approaches for cyberbullying and traditional bullying using data from six European countries. Journal of School Violence, 14(1), 47-65.
[41] Seigfried-Spellar K. C., O'Quinn C. L., & Treadway K. N. (2015). Assessing the relationship between autistic traits and cyberdeviancy in a sample of college students. Behaviour and Information Technology, 34(5), 533-542.
[42] Tanrikulu, I., & Erdur-Baker, Ö. (2019). Motives behind cyberbullying perpetration: A test of uses and gratifications theory. Journal of Interpersonal Violence, 36(13-14), NP6699-NP6724.
[43] Tian L. L., Yan Y. R., & Huebner E. S. (2018). Effects of cyberbullying and cybervictimization on early adolescents' mental health: Differential mediating roles of perceived peer relationship stress. Cyberpsychology, Behavior, and Social Networking, 21(7), 429-436.
[44] Williford A. P., Brisson D., Bender K. A., Jenson J. M., & Forrest-Bank S. (2011). Patterns of aggressive behavior and peer victimization from childhood to early adolescence: A latent class analysis. Journal of Youth and Adolescence, 40(6), 644-655.