Prevalence and correlates of cyber-victimization in a nationally representative sample of South African youth

Abstract

Cyber-victimization is defined as “the experience of aggressive behaviours while using new electronic technologies, primarily mobile phones and the internet" (Álvarez-García et al., 2015a; Smith & Steffgen, 2013). Approximately 20 to 50% of adolescents have experienced online victimization globally (Zhu et al., 2021). This is a public health concern because cyber- victimization can harm the mental health of the victim thus leading to depressive symptoms such as anxiety, helplessness, distress, sadness, trauma symptoms, reduced self-esteem, feelings of isolation, fear of socialization, hopelessness, self-harm, or suicidal ideation (Hertz et al., 2015; Kim et al., 2022; Landoll et al., 2015; Mason et al., 2009). Research on the risk factors associated with cyber-victimization is relatively new and has some gaps and inconsistencies (Álvarez-García et al., 2015a; Zhu et al., 2021). This study will focus on analyzing the association of some demographic, psychological, educational, family factors and exposure to other forms of violence, with cyber-victimization, in a nationally representative sample of South African children. We aim to determine the lifetime prevalence and last-year prevalence (i.e., annual incidence) of cyber- victimization, as well as the association of cyber-victimization with its correlates, based on a nationally representative cross-sectional study of 15–17-year-old youth in South Africa. Method: This mini dissertation will use secondary data obtained, with permission, from the Optimus Study conducted in South Africa (Ward et al., 2018). The study drew on data from a population survey that was conducted with a sample of 15- to 17-year-old adolescents recruited nationally from schools (4 086 participants) as well as households (5 631 participants) (Ward et al., 2018). The aims of this study are as follows: To estimate the prevalence and incidence of cyber-victimization among South African youth as of 2013/2014, as well as in-person victimization. This will be achieved by reporting the relative frequencies with CI of both the lifetime and last-year prevalence, stratified by key demographic measures. We will also report the prevalence of each of the six types of cyberbullying. To measure the strength of association between cyber-victimization and potential risk/protective factors among South African youth. We will use logistic regression to estimate the association of each factor in table 1 with cyber-victimization, adjusting for the possible confounding factors listed. The associations will be expressed as odds ratios (ORs) with their 95% confidence intervals (Cis). The unadjusted odds ratios (OR) will be estimated using a univariable regression model for each correlate and adjusted OR (aOR) will be estimated using a multivariable regression model containing all correlates. To study the relationship between cyber-victimization and each of the potential consequences stratified by sex. For the factors, we will report differences in proportions, by cyber-victimization and CIs. The following correlates will be considered as consequences of cyber-victimization (table 2): Behavioral patterns (high-risky sexual behaviours, alcohol and substance misuse), educational (academic performance), and psychological (anxiety, depression, anger, and post-traumatic stress)