Security and privacy scale – do you use antivirus software, read privacy policies, use well known websites, do not reveal your private information online… 8 questions
These findings support previous findings from) and Wang, Iannotti and Nansel (2009) who related higher education and income to more risky online behavioural patterns and higher incidence of cyber crime victimization. Age cohort effect. Most of the participants belonged to age group over 35 years – different results if only with participants under 35
Prevalence, Predictors, and Risk of Cyber Victimization in Canada
Prevalence, Predictors, and Risk of Cyber Victimization in Canada Nikolina Ljepava, M.S. , M.A
Cyber CrimesTwo types of technology-related crimes:*cyber crimes – Internet crimes that rely on specialized knowledge (e.g., bank frauds, identity thefts, computer viruses)*(Jaishankar, 2011)
Computer Crimes computer crimes - criminal offences facilitated by using technologies, unrelated to technological knowledge Referred more often as “cyber bullying victimization”
Routine Activities Theory(Cohen & Felson, 1979) Individual’s day-to-day activities have a direct impact on victimization, placing some individuals at increased risk of being victimized. Routine Activities Theory has been applied to explain cyber victimization .
Routine Activities Theory(Cohen & Felson, 1979) Examples of the online routine activities: Reading email Using social networks Using instant messaging programs Online shopping Visiting different websites (web browsing) Gaming
Predictors of Cyber Victimization Age Gender Income Education Loneliness Mental health
Purpose of the Study 1. To explore risk factors related to three different types of cyber victimization – cyber crime, cyber bullying and child cyber bullying. 2. To test the structural models of cyber victimization 3. To explore the application of Routine Activities Theory in online environment
Risk Analysis •Replication of the Arnold & Baron (2005) research of the victimization risk based on epidemiological concepts. •Based on logistic regression analysis •Calculation of population attributable risk and absolute reductions in population risk attributable to specific predictors
Structural Equation ModelingModel 1Loneliness Loneliness Online Online behaviour Cyber victimization Cyber victimization behaviour Mental Mental health health Age Age Sex Sex
Structural Equation ModelingModel 2 Sex SexLoneliness Loneliness Online Online behaviour Cyber victimization Cyber victimization behaviour Mental Mental health health Age Age
General Social Survey •Victimization cycle 23, conducted 2009 •Information related to cyber victimization collected for the first time in Canada •19, 500 participants 15 years and older across the 10 Canadian provinces
General Social Survey Three modules: • Internet use, risk, and prevention • Cyber bullying experienced by respondents • Cyber bullying experienced by respondents’ children (as reported by respondents).
Instruments 18 variables were used for the purpose of this study. In order to conduct logistic regression and risk analysis, three dichotomous dependent variables were used: cyber crime, cyber bullying, and child cyber bullying.
InstrumentsTo test a structural equation model, a summary score of eight questions exploring different types of cyber bullying and cyber crime victimization was created
Predictors• demographic variables (age, sex, income and education),• mental health variables (life satisfaction, stress and depression),• loneliness (measured by the number of close friends and the number of close friends living in the same city)• variables related to online behavior
Participants• Participants that used Internet within the last year• Full sample after data cleaning – 14,149• Sample of parents - 3,443• Age range 15 to over 70 years of age• 53% of the participants between 35 and 65 years old
Participants: full sample 49.7% female, 50.3% male
Results97.3% used Internet within last month74% reported being cyber victimized73.9% cyber crime victimization Male reported significantly higher cyber crime victimization7.8% cyber bullying victimization No gender differences in cyber bullying victimization for adult respondents Higher incidence in the age group from 15-35 years (13.4%).
Results 10.3% of children cyber bullied * 71.4% of cyber bullied children female 73.4% informed their parents about being cyber bullied*parent’s report
Conclusion• Age: significant predictor of both cyber crime and cyber bullying victimization, with risk of both types of cyber victimization decreasing with age.• Sex: significant predictor only of cyber crime victimization (for male participants)• Income and educational categories: significant predictors of cyber crime victimization (higher income and education higher risk)
Conclusion• Lower life satisfaction, higher levels of stress and experiencing psychological problems predicted cyber victimization.• Depression was not found to significantly predict any type of Internet victimization
Conclusion• Online behaviour accounted for most of the cyber victimization risk for all three types of cyber victimization.• These findings support the application of Routine Activities Theory in online environment: the way we behave online can increase (or decrease) risk of cyber victimization.
ConclusionFor children cyber bullying :• bullying was reported more often to mothers• parental security and privacy preferences accounted for the highest percentage of attributable risk of children cyber victimization
Conclusion• Structural model suggests the mediating effect of online behaviour needs to be taken into a consideration when researching the influence of different predictors on victimization on Internet.• Overall modifications in online behaviour can decrease the incidence of online victimization• Applicable in prevention programs, especially for children / adolescent population