Prevalence, Predictors, and Risk of Cyber Victimization in Canada

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  • 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

    1. 1. Prevalence, Predictors, and Risk of Cyber Victimization in Canada Nikolina Ljepava, M.S. , M.A
    2. 2. Cyber Victimization online fraud, identity theft, phishing, computer viruses, cyber bullying, cyber stalking….The prevalence of technology-related crimes is continuously increasing**Jones, Mitchell, & Finkelhor, 2011.
    3. 3. 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)
    4. 4. Computer Crimes computer crimes - criminal offences facilitated by using technologies, unrelated to technological knowledge Referred more often as “cyber bullying victimization”
    5. 5. 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 .
    6. 6. 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
    7. 7. Predictors of Cyber Victimization Age Gender Income Education Loneliness Mental health
    8. 8. 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
    9. 9. 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
    10. 10. Structural Equation ModelingModel 1Loneliness Loneliness Online Online behaviour Cyber victimization Cyber victimization behaviour Mental Mental health health Age Age Sex Sex
    11. 11. Structural Equation ModelingModel 2 Sex SexLoneliness Loneliness Online Online behaviour Cyber victimization Cyber victimization behaviour Mental Mental health health Age Age
    12. 12. 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
    13. 13. 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).
    14. 14. 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.
    15. 15. InstrumentsTo test a structural equation model, a summary score of eight questions exploring different types of cyber bullying and cyber crime victimization was created
    16. 16. 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
    17. 17. 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
    18. 18. Participants: full sample 49.7% female, 50.3% male
    19. 19. Participants: full sample Income
    20. 20. Participants: full sample Education
    21. 21. Participants: parents 51.4 % female, 48.6% male
    22. 22. Participants: parents Income
    23. 23. Participants: parents Education
    24. 24. 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%).
    25. 25. 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
    26. 26. Results
    27. 27. Results
    28. 28. Results
    29. 29. Results Logistic Regression results: cyber crime Predicto Odds Ratio SE z p value Agegr5 1.035834 .0076397 4.77 0.000 sex .549076 .0241462 -13.63 0.000 Morethan100 .7224016 .0472819 -4.97 0.000 missingincm .6008996 .0430427 -7.11 0.000 inc20_39 .6173358 .0405762 -7.34 0.000 inc40_59 .7650704 .0519032 -3.95 0.000 undegree 2.67254 .1969819 13.34 0.000 comcol 1.635635 .1147576 7.01 0.000 someuni 1.762247 .1413277 7.06 0.000 edmiss 10.7847 11.0755 2.32 0.021 Fb .7044694 .0344521 -7.16 0.000 chat .6069713 .035262 -8.59 0.000 meetinRL .55473 .0436345 -7.49 0.000 secpriv .7643397 .012909 -15.91 0.000 truncsrh120 .8953929 .0131294 -7.54 0.000 psycon 1.315463 .1797177 2.01 0.045
    30. 30. Results Logistic regression cyber bullying Predictor Odds Ratio SE z p value Agegr5 1.168699 .0188339 9.67 0.000 comcol .5979134 .0794098 -3.87 0.000 edmiss 8.64662 28.18306 8.47 0.000 Fb .6915541 .0664929 -3.84 0.000 chat .5572777 .0481839 -6.76 0.000 meetinRL .3968217 .0346108 -10.60 0.000 secpriv .8253592 .0265101 -5.98 0.000 lifesat .8976057 .0221205 -4.38 0.000 stress 1.244524 .0536452 5.07 0.000 psycon 1.597002 .2780475 2.69 0.007 Logistic regression results: cyber bullying children Predictor Odds Ratio SE z p value sex 1.502771 .2021978 3.03 0.002 chat .7307806 .1078592 -2.13 0.034 secpriv .8913731 .0456775 -2.24 0.025 stress 1.270991 .0919194 3.32 0.001
    31. 31. Results Percentage of Population Risk Attributable to Predictors of Cyber Victimization Under LogisticRegression Predictors Cyber crime Cyber Child cyber victimization bullying bullying victimization victimization* Age 4% 73% -- Sex 15% 9% -- Social networks use 9% 43% 28% Chat programs 10% 51% 29% Meeting in real life 94% 92% 87% Security and privacy 92% 89% 85% Depression medication <1%. 1% -- Life satisfaction 5% 25% 19% Stress 10% 40% -- Loneliness 5% 13% -- Income 46% 23% 35% Education 8% 56% 16% Parent cyber bullied -- -- 6% Note. Predictors are based on parents’ behaviour.
    32. 32. ResultsAbsolute Reductions in Population Risk Attributable to Predictors of Cyber Victimization Predictors Cyber crime Cyber Child cyber victimization bullying bullying victimization victimization Age .004 .056 -- Sex .013 .009 -- Social networks use .007 .033 .003 Chat programs .009 .040 .003 Meeting in real life .077 .072 .008 Security and privacy .077 .069 .079 Loneliness .004 .011 -- Stress .009 .031 -- Depression medication .000 .001 -- Life satisfaction .004 .020 .002 Income .039 .018 .032 Education .007 .043 .014 Unreduced population risk .084 .077 .092
    33. 33. Results:Structural model
    34. 34. ResultsComparative model fit for the models testedModels tested χ2 df p CFI TLI RMSEAModel 1 5147.2 54 .000 .802 .666 .082Model 2 1600.5 51 .000 .942 .911 .046
    35. 35. Results Parameter estimates for Model in Figure 2. Stand. Parameter Estimate S.E p value Estimate onlinebehaviour <--- mentalhealth .148 .534 .081 *** onlinebehaviour <--- loneliness .028 .001 .000 *** onlinebehaviour <--- SEX .085 .042 .006 *** onlinebehaviour <--- AGEGR5 .838 -.064 .001 *** IRP_160R <--- onlinebehaviour .494 1.000 IRP_170R <--- onlinebehaviour .404 .732 .017 *** IRP_180R <--- onlinebehaviour .261 .371 .013 *** secprivr1 <--- onlinebehaviour .211 1.099 .048 *** ISL_020 <--- loneliness .777 1.000 psycon <--- mentalhealth .373 1.000 MEDDEPR <--- mentalhealth .283 1.080 .103 *** SRH_130 <--- mentalhealth .438 6.429 .615 *** SRH_120 <--- mentalhealth -.691 -15.906 1.422 *** cyvictim <--- onlinebehaviour 1.385 6.230 .706 *** ISL_010 <--- loneliness .941 1.955 .142 *** cyvictim <--- AGEGR5 .995 .339 .045 *** cyvictim <--- SEX -.226 -.503 .049 ***
    36. 36. Conclusion• Demographic variables , mental health and online behaviour predicted cyber victimization.• Loneliness influenced online behavioural patterns indirectly influencing cyber victimization
    37. 37. 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)
    38. 38. 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
    39. 39. 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.
    40. 40. 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
    41. 41. 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
    42. 42. QUESTIONS?ljepavan@uwindsor.ca

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