1. Prevalence, Predictors, and Risk of Cyber
Victimization in Canada
Nikolina Ljepava, M.S. , M.A

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. Cyber Crimes
Two 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. Computer Crimes
computer crimes - criminal offences
facilitated by using technologies, unrelated
to technological knowledge
Referred more often as “cyber bullying
victimization”

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. 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. Predictors of Cyber Victimization
Age
Gender
Income
Education
Loneliness
Mental health

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. 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. Structural Equation Modeling
Model 1
Loneliness
Loneliness
Online
Online
behaviour Cyber victimization
Cyber victimization
behaviour
Mental
Mental
health
health
Age
Age Sex
Sex

11. Structural Equation Modeling
Model 2
Sex
Sex
Loneliness
Loneliness
Online
Online
behaviour Cyber victimization
Cyber victimization
behaviour
Mental
Mental
health
health
Age
Age

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. 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. 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. Instruments
To test a structural equation model, a
summary score of eight questions
exploring different types of cyber bullying
and cyber crime victimization was created

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. 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. Participants: full sample
 49.7% female, 50.3% male

19. Participants: full sample
 Income

20. Participants: full sample
 Education

21. Participants: parents
 51.4 % female, 48.6% male

22. Participants: parents
 Income

23. Participants: parents
 Education

24. Results
97.3% used Internet within last month
74% reported being cyber victimized
73.9% cyber crime victimization
Male reported significantly higher cyber crime
victimization
7.8% 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. 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. Results

27. Results

28. Results

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. 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. 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. Results
Absolute 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. Results:
Structural
model

34. Results
Comparative model fit for the models tested
Models tested χ2 df p CFI TLI RMSEA
Model 1 5147.2 54 .000 .802 .666 .082
Model 2 1600.5 51 .000 .942 .911 .046

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. Conclusion
• Demographic variables , mental health
and online behaviour predicted cyber
victimization.
• Loneliness influenced online behavioural
patterns indirectly influencing cyber
victimization

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. 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. 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. Conclusion
For 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. 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. QUESTIONS?
ljepavan@uwindsor.ca