CYBER-CRIME FEAR AND VICTIMIZATION: AN ANALYSIS OF NATIONAL SURVEY

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APPENDIX B 194

Table B.1 Cross-Tabulation of Cyber-Crime Victimization by Selec Variables Computer Computer- Identity Identity virus related fraud or theft Age N% fraud or scam N% <25 years- crime old N% N% 25-50 years-old 49 54.4 6 6.7 3 50.0 1 16.7 > 50 years- 368 65.8 49 8.8 13 26.5 25 51.0 old Chi-square 322 57.7 37 6.6 14 46.7 19 51.4 Low Income 10.506* 2.021 6.408 5.412 6 Mid 37 49.3 1 16.7 3 5.0 Income 8.0 High 259 62.0 29 6.9 10 34.5 14 48.3 Income 275 63.7 38 8.8 14 36.8 20 52.6 Chi-square 7.174 3.561 2.502 1.588 *Significance at p<.05

cted Variables Securities Cyber- Extortion Computer fraud stalking or hacking N% N% blackmail N% 1 16.7 N% 70 0 2 4.1 2 33.3 2 5.4 00 4 8.2 00 0 4 44.0 2 5.4 5.560 00 41 2.7 00 9.296 7.261 00 5.179 2 6.9 1 16.7 4 13.8 00 1 2.6 2 6.9 2.638 2 5.3 31 3.4 00 3.838 6.202 60 0 3.120 195

Table B.1 (Continued) Variables Computer Computer- Identity Identity virus related fraud or theft N% fraud or scam N% crime N% 00 N% 00 00 00 Frequency 1 2.2 00 Never 2 28.6 0 0 6 50.0 0 0 5 41.7 A few times 5 31.3 1 per year 23 46.0 2.0 Once or 101 53.4 12 6.3 twice a month 99 59.3 6 3.6 0 0 2 33.3 302 65.8 Once or 41 8.9 12 29.3 25 61.0 twice a 29 10.6 12 41.4 11 37.9 week Several days a week Once a day Several 188 69.6 times each 35.431*** 26.291* 6.885 6.706 day Chi-square *Significance at p<.05 ***Significance at p< .001

Securities Cyber- Extortion Computer fraud stalking or hacking N% N% blackmail N% 00 N% 00 00 00 00 00 00 00 00 00 00 00 00 00 1 8.3 2 33.3 00 2 4.9 00 2 33.3 1 2.4 1 3.4 1 2.4 00 1 16.7 4 13.8 10.863 1 2.4 2.427 1 3.4 8.657 4.997 196

Table B.1 (Continued) Variables Computer Computer- Identity Identity virus related fraud or theft N% fraud or scam N% crime N% N% Duration 30 Minutes 258 55.0 22 4.7 8 36.4 9 40.9 or less 246 66.0 28 7.5 6 21.4 18 64.3 One hour 22 12.0 10 45.5 10 45.5 11.9 60.0 1-2 hours 128 69.6 5 17.9 0 0 3 41.7 12 5 41.7 5 2-3 hours 30 71.4 3 or more 48 71.6 hours 21.869** 25.180** 16.334* 12.403 Chi-square *Significance at p<.05 **Significance at p<.01

Securities Cyber- Extortion Computer fraud stalking or hacking N% N% blackmail N% N% 00 00 2 9.1 00 2 7.1 00 1 3.6 1 4.5 3 13.6 3 13.6 00 00 00 1 20.0 00 00 00 1 8.3 1 20.0 12.470 14.227 2 16.7 11.836 22.535** 197

APPENDIX C 198

199 Table C.1 Logistic Regression of Computer Virus Victimization (Interaction Terms) (Dependent Variable: 1 =Yes) Variables Model 1 Wald Coeffi Age 8.017 Gender1 (male) -0.15** 11243 Race2 (white) (0.985) 5.177 Type of Residence3 (Rural) 0.492** 0.039 (1.636) 7.250 Education 693* 2.601 Low Income4 (1.999) 0.101 -0.029 0.825 Mid Income (0.971) 5.078 0.083** 8.97 Income (missing) (1.086) 3.816 Children w/access to Internet5 -5.15 5.321 (0.597) 0.582 Children w/ access to Internet -0.053 0.895 (missing) (0.948) Frequency 0.176 (1.193) Duration 0.513* (1.671) Gender*Frequency 0.249 (1.283) Gender*Duration 0.137 Model X2 (1.146) df 0.203* n (1.224) 0.093 (1.097) 0.137 (1.147) 74.809*** 14 987 *P<.05; ** P<.01; ***P<.001 Note: Numbers in parentheses are Exp(B) 1)female is the reference; 2) black is the reference; 3)urban is the reference; 4) high income is the reference; 5) children with no access to the Internet

200 Table C.2 Logistic Regression of Computer Virus Victimization (Interaction Terms) (Dependent Variable: 1 =Yes) Variables Model 1 Wald Age Coeffi 7.556 Gender1 (male) 10.614 Race2 (white) -0.014** 0.228 Type of Residence3 (Rural) (0.986) 0.108 0.474** 7.153 Education (1.606) 1.349 Low Income4 -0.512 0.117 (0.599) 0.860 Mid Income -0.049 3.837 (0.952) 0.503 Income (missing) 0.082** 1.338 Children w/access to Internet5 (1.086) 2.422 -0.381 0.233 Children w/ access to Internet (0.683) 6.657 (missing) -0.057 Frequency (0.945 0.180 Duration (1.197) 0.450* Race*Frequency (1.568) 0.188 Race*Duration (1.207) Model X2 0.292 df (1.339) n -0.443 (0.642) -0.125 (0.882) 0.758* (2.134) 81.195*** 14 987 *P<.05; ** P<.01; ***P<.001 Note: Numbers in parentheses are Exp(B) 1)female is the reference; 2) black is the reference; 3)urban is the reference; 4) high income is the reference; 5) children with no access to the Internet

201 Table C.3 Logistic Regression of Computer Virus Victimization (Interaction Terms) (Dependent Variable: 1 =Yes) Variables Model 1 Wald Age Coeffi 7.793 Gender1 (male) 10.406 Race2 (white) -0.014** 4.904 Type of Residence3 (Rural) (0.986) 0.820 0.468** 7.392 Education (1.596) 2.873 Low Income4 0.673* 0.077 (1.961) 0.930 Mid Income 0.497 4.931 (1.644) 0.815 Income (missing) 0.083** 8.550 Children w/access to Internet5 (1.087) 12.789 -0.542 1.036 Children w/ access to Internet (0.582) (missing) -0.046 Frequency (0.955) 0.187 Duration (1.206) 0.505* Type of residence*Frequency (1.657) Model X2 0.237 df (1.267) n 0.211** (1.235) 0.253*** (1.287) -0.120 (0.887) 74.131*** 13 987 *P<.05; ** P<.01; ***P<.001 Note: Numbers in parentheses are Exp(B) 1)female is the reference; 2) black is the reference; 3)urban is the reference; 4) high income is the reference; 5) children with no access to the Internet

202 Table C.4 Logistic Regression of Cyber-Crime Victimization (Interaction Terms)(Dependent Variable: 1 =Yes) Variables Model 1 Age Coeffi Wald Gender1 (male) Race2 (white) -0.009 2.890 Type of Residence3 (Rural) (0.991) Education Low Income4 0.493** 10.600 Mid Income (1.636) Income (missing) 0.700* 5.071 Children w/access to Internet5 (2.014) Children w/ access to Internet (missing) 0.042 0.075 Frequency (1.043) Duration 0.054 2.815 Id-target (1.055) Money-Target -0.389 1.395 Gender*Frequency (0.678) Gender*Duration Model X2 -0.024 0.019 df n (0.976) 0.221 1.194 (1.247) 0.428 3.339 (1.534) 0.203 0.563 (1.224) 0.099 1.878 (1.104) 0.193* 4.454 (1.213) 0.125* 0.038 (1.133) 0.177** 8.203 (1.194) 0.1.09 0.770 (1.115) 0.041 0.075 (1.041) 100.954*** 16 987 *P<.05; ** P<.01; ***P<.001 Note: Numbers in parentheses are Exp(B) 1)female is the reference; 2) black is the reference; 3)urban is the reference; 4) high income is the reference; 5) children with no access to the Internet

203 Table C.5 Logistic Regression of Cyber-Crime Victimization (Interaction Terms) (Dependent Variable: 1 =Yes) Variables Model 1 Age Coeffi Wald Gender1 (male) Race2 (white) -0.008 2.547 Type of Residence3 (Rural) (0.992) Education Low Income4 0.483** 1.621 Mid Income (10.407) Income (missing) -0.536 0.259 Children w/access to Internet5 (0.585) Children w/ access to Internet (missing) 0.031 0.039 Frequency (1.031) Duration 0.052 2.685 Id-target (1.054) Money-Target -0.282 0.698 Race*Frequency (0.754) Race*Duration Model X2 -0.032 0.034 df n (0.968) 0.219 1.168 (1.244) 0.372 2.492 (1.450) 0.149 0.302 (1.161) 0.143 0.331 (1.153) -0.290 1.202 (0.749) 0.130* 4.633 (1.138) 0.179** 8.313 (1.196) -0.004 0.0003 (0.996) 0.547* 3.991 (1.729) 104.834*** 16 987 *P<.05; ** P<.01; ***P<.001 Note: Numbers in parentheses are Exp(B) 1)female is the reference; 2) black is the reference; 3)urban is the reference; 4) high income is the reference; 5) children with no access to the Internet

204 Table C.6. Logistic Regression of Cyber-Crime Victimization (Interaction Terms) (Dependent Variable: 1 =Yes) Variables Model 1 Age Coeffi Wald Gender1 (male) Race2 (white) -0.009 0.100 Type of Residence3 (Rural) (0.991) Education Low Income4 0.478** 10.210 Mid Income (1.612 Income (missing) 0.696* 5.031 Children w/access to Internet5 (2.006 Children w/ access to Internet (missing) 0.262 0.558 Frequency (1.300) Duration 0.054 2.857 Id-target (1.055) Money-Target -0.401 1.479 Type of Residence*Frequency Model X2 (0.669) df n -0.024 0.019 (0.976) 0.223 1.22 (1.250) 0.415 3.155 (1.515) 0.190 0.500 (1.210) 0.152* 4.142 (1.164) 0.208** 8.017 (1.232) 0.126* 4.444 (1.135) 0.179** 8.417 (1.196) -0.051 0.176 (951) 100.207*** 15 987 *P<.05; ** P<.01; ***P<.001 Note: Numbers in parentheses are Exp(B) 1)female is the reference; 2) black is the reference; 3)urban is the reference; 4) high income is the reference; 5) children with no access to the Internet