Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model

Abstract

This cross-sectional descriptive study identified risk factors and predictors related to the perpetration of and potential for cyberbullying among adolescents, respectively. The analysis included a zero-inflated negative binomial regression model. Data were assessed from 2590 middle-school student panels obtained during the first wave of the Korean Child and Youth Panel Survey 2018. Of these respondents, 63.7% said they had not experienced the perpetration of cyberbullying. However, a subsequent count model analysis showed that several factors were significantly associated with cyberbullying, including offline delinquency, aggression, smartphone dependency, and smartphone usage on weekends (either 1–3 h or over 3 h). A logit model analysis also showed several predictive factors that increased the likelihood of cyberbullying, including gender (boys), offline delinquency, aggression, smartphone usage during weekdays (1–3 h), computer usage during weekends (1–3 h), and negative parenting. These identified risks and predictors should be useful for interventions designed to prevent the perpetration of cyberbullying among middle school students.

The current status of Cyberbullying research: a short review of the literature

“…Higher rate in LGBTQ+ community [36] Other online behaviours Associated with Internet addiction and using the Internet >2 hours/day [16,37] Higher in subjects with PUI [11,29] Association with online gaming disorder [28] Association with online gaming disorder [28] --Predicted by higher social media addiction scores and more hours spent online [27] Parent-child relationship Association with parental abuse, parental neglect, and family dysfunction [26] Predicted by childhood psychological maltreatment [38] Association with physical discipline by parents [28] Higher risk in adolescents with a high level of parental phubbing [39] Psychological factors Higher severity in subjects with social avoidance and social distress, in particular with peers [40] Associated with higher impulsivity or difficulties in emotions regulation [23,41,42] -Higher risk in adolescents with antisocial behaviours or conduct problems [23,43,44]…”

Role-Based Cyberbullying Situations: Cybervictims, Cyberaggressors and Cyberbystanders

“…Our findings are slightly higher, especially for cybervictims. This may be because more adolescents are using technology with increasing frequency [4], and without parental supervision, enhancing the disinhibition effect [34,35] used to describe the lowering of psychological restraints, which often serve to regulate behaviors in the online social environment.…”

“…In its broadest sense, cyberbullying is any behavior that is performed using technology, particularly the internet, by an individual or group that involves repeatedly sending aggressive messages or other similar actions with the intention of causing harm [2]. Like traditional bullying, cyberbullying can have long-term effects for its adolescent victims [3], with technology being one of the most important risk factors [4].…”

Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

“…The statistical results of the marginal distributions show that the fittest distribution of total precipitation and dry spells passed the Anderson-Darling test with a 5% significant level (shown by 𝑝𝑝-value > 0.05) so that the marginal distributions can be employed to estimate the copula parameter. Meanwhile, we still use the negative binomial distribution to deal with zero-inflated hotspot data (Zhang and Yi 2020; Kang et al 2021). Using these marginal distributions, we estimate the copula parameters of TP -H (total precipitation -hotspots) and DS -H (dry spells -hotspots) with different ENSO conditions (Eq.…”