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

doi:10.16719/j.cnki.1671-6981.20240313

Abstract

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.

Key words: college students, online aggressive behavior, latent class analysis, latent transition analysis

摘要： 采用潜在转变分析探讨大学生网络攻击行为的类别转变及其影响因素。2000名大学生参加了一项跨度为4个月的追踪研究,研究者对其网络攻击行为进行了2次测量。结果发现：（1）大学生网络攻击行为分为低攻击型与高攻击型2种网络攻击模式,且随时间的发展,高攻击型更容易向低攻击型转变;（2）性别和道德推脱是大学生网络攻击行为类别及其转变的重要影响因素。

关键词: 大学生, 网络攻击行为, 潜在类别分析, 潜在转变分析

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.

金童林, 乌云特娜, 张璐, 雷泽宇, 贾彦茹. 大学生网络攻击行为的类别转变:一项潜在转变分析^*[J]. 心理科学, 2024, 47(3): 614-621.

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