Online Activity, Alcohol Use, and Internet Delinquency Among Korean Youth: A Multilevel Approach

REFERENCES

• Axenroth, J.B. (1983). Social class and delinquency in cross-cultural perspective. Journal of Research in Crime and Delinquency, 20, 164–182doi: 10.1177/002242788302000202
   Web of Science ®Google Scholar
• Brunelle, N., Leclerc, D., Cousineau, M-M., Dufour, M., & Gendron, A. (2012). Internet gambling, substance use, and delinquent behavior: An adolescent deviant behavior involvement pattern. Psychology of Addictive Behaviors, 26, 364–370.doi: 10.1037/a0027079
   PubMed Web of Science ®Google Scholar
• Bryk, A.S., & Raudenbush, S.W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147–158.doi: 10.1037/0033-2909.101.1.147
   Web of Science ®Google Scholar
• Cohen, L., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608.
   Web of Science ®Google Scholar
• Farrington, D.P. (1994). Childhood, adolescent, and adult features of violent males. In L. Huesemann (Ed.), Aggressive behavior: Current perspectives (215–240). New York, NY: Plenum.
   Google Scholar
• Finkelhor, D., Mitchell, K.J., & Wolak, J. (2000). Online victimization: A report on the nation's youth. Alexandria, VA: National Center for Missing and Exploited Children.
   Google Scholar
• Goldstein, H. (1995). Multilevel statistical models(2nd ed.). London, England: Edward Arnold.
   Google Scholar
• Goldstein, H., & Rasbash, J. (1996). Improved approximations for multilevel models with binary responses. Journal of the Royal Statistical Society, 159, 505–513.
   Google Scholar
• Grabosky, P.N. (2001). Virtual criminality: Old wine in new bottles? Social & Legal Studies, 10, 243–249.
   Web of Science ®Google Scholar
• Han, S., Choe, M.K., Lee, M.S., & Lee, S.H. (2001). Risk-taking behavior among high school students in South Korea. Journal of Adolescence, 24, 571–574.doi: 10.1006/jado.2001.0411
   PubMed Web of Science ®Google Scholar
• Hawkins, J.D., Catalano, R.F., & Miller, J.Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64–105.doi: 10.1037/0033-2909.112.1.64
   PubMed Web of Science ®Google Scholar
• Hinduja, S., & Patchin, J.W. (2008). Cyberbullying: An exploratory analysis of factors related to offending and victimization. Deviant Behavior, 29, 129–156.doi: 10.1080/01639620701457816
   Web of Science ®Google Scholar
• Institute for Homeland Security Solutions. (2009). The impact of the Internet on deviant behavior and deviant communities. Retrieved from https://www.ihssnc.org/portals/0/IRW%20Literature%20Reviews%20Deviance%20and%20the%20Internet.pdf
   Google Scholar
• Kamata, A. (2001). Item analysis by the hierarchical generalized linear model. Journal of Educational Measurement, 38, 79–93.doi: 10.1111/j.1745-3984.2001.tb01117.x
   Web of Science ®Google Scholar
• LaGrange, T. (1999). The impact of neighborhoods, schools, and malls on the spatial distribution of property damage. Journal of Research in Crime and Delinquency, 36, 393–422.doi: 10.1177/0022427899036004003
   Web of Science ®Google Scholar
• Lee, J., Onifade, E., Teasley, M., & Noel, L. (2012). The effects of risk and protective factors on juvenile delinquency in South Korea. Journal of Ethnicity in Criminal Justice, 10, 316–329.doi: 10.1080/15377938.2012.732880
   Google Scholar
• Lenhart, A., Madden, M., & Hitlin, P. (2005). Teens and technology: Youth are leading the transition to a fully wired and mobile nation. Washington, DC: Pew Internet and American Life Project.
   Google Scholar
• Marcum, C.D., Higgins, G.E., & Ricketts, M.L. (2010). Potential factors of online victimization of youth: An examination of adolescent online behaviors utilizing routine activity theory. Deviant Behavior, 31, 381–410.doi: 10.1080/01639620903004903
   Web of Science ®Google Scholar
• McCullagh, R., & Nelder, J.A. (1989). Generalized linear models(2nd ed.). London, England: Chapman & Hall.
   Google Scholar
• Mitchell, K.J., Finkelhor, D., & Wolak, J. (2004). Victimization of youths on the Internet. Journal of Aggression, Maltreatment & Trauma, 8, 1–39.doi: 10.1300/J146v08n01_01
   Google Scholar
• Mustaine, E.E., & Tewksbury, R. (1998). Predicting risks of larceny theft victimization: A routine activity analysis using refined lifestyle measures. Criminology, 36, 829–857.doi: 10.1111/j.1745-9125.1998.tb01267.x
   Web of Science ®Google Scholar
• Muthén, B.O. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557–585.
   Web of Science ®Google Scholar
• Nagin, D., & Tremblay, R.E. (1999). Trajectories of boys’ physical aggression, opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency. Child Development, 70, 1181–1196.doi: 10.1111/1467-8624.00086
   PubMed Web of Science ®Google Scholar
• Postmes, T., & Spears, R. (1998). Deindividuation and antinormative behavior: A meta-analysis. Psychological Bulletin, 123, 238–259.doi: 10.1037/0033-2909.123.3.238
   Web of Science ®Google Scholar
• Pratt, T., & Cullen, F. (2005). Assessing macro-level predictors and theories of crime: A meta-analysis. In M. Tonry (Ed.), Crime and justice: A review of research (pp. 373–450). Chicago, IL: University of Chicago Press.
   Google Scholar
• Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Application and data analysis methods(2nd ed.). Thousand Oaks, CA: Sage.
   Google Scholar
• Raudenbush, S.W., Bryk, A.S., Cheong, Y.F., Congdon, R., & du Toit, M. (2004). HLM 6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International.
   Google Scholar
• Rho, K. (2002). Uses of the Internet in Korea. Educational Technology Research and Development, 50, 84–88.
   Web of Science ®Google Scholar
• Roncek, D., & Bell, R. (1981). Bars, blocks, and crime. Journal of Environmental Systems, 11, 35–47.
   Web of Science ®Google Scholar
• Roncek, D.W., & Maier, P.A. (1991). Bars, blocks, and crimes revisited: Linking the theory of routine activities to the empiricism of hot spots. Criminology, 29, 725–753.doi: 10.1111/j.1745-9125.1991.tb01086.x
   Web of Science ®Google Scholar
• Seltzer, M.H. (1994). Studying variation in program success: A multilevel modeling approach. Evaluation Review, 18, 342–361.doi: 10.1177/0193841×9401800304
   Web of Science ®Google Scholar
• Tseloni, A., Wittebrood, K., Farrell, G., & Pease, K. (2004). Burglary victimization in England and Wales, the United States and The Netherlands: A cross-national comparative test of routine activities and lifestyle theories. British Journal of Criminology, 44, 66–91.doi: 10.1093/bjc/44.1.66
   Web of Science ®Google Scholar
• Wells, M., & Michell, K.J. (2008). How do high-risk youth use the Internet? Characteristics and implications for prevention. Child Maltreatment, 13, 227–234.doi: 10.1177/1077559507312962
   PubMed Web of Science ®Google Scholar
• Willoughby, T. (2008). A short-term longitudinal study of Internet and computer game use by adolescent boys and girls: Prevalence, frequency of use, and psychosocial predictors. Developmental Psychology, 44, 195–204.doi: 10.1037/0012-1649.44.1.195
   PubMed Web of Science ®Google Scholar
• Yar, M. (2005). The novelty of “cybercrime”: An assessment in light of routine activity theory. European Journal of Criminology, 2, 407–427.doi: 10.1177/147737080556056
   Google Scholar
• Ybarra, M.L., & Mitchell, K.J. (2004a). Online aggressor/targets, aggressors, and targets: A comparison of associated youth characteristics. Journal of Child Psychology and Psychiatry, 45, 1308–1316.doi: 10.1111/j.1469-7610.2004.00328.x
   PubMed Web of Science ®Google Scholar
• Ybarra, M.L., & Mitchell, K.J. (2004b). Youth engaging in online harassment: Associations with caregiver–child relationships, Internet use, and personal characteristics. Journal of Adolescence, 27, 319–336.doi: 10.1016/j.adolescence.2004.03.007
   PubMed Web of Science ®Google Scholar
• Young, M.L., Preisser, J.S., Qaqish, B.F., & Wolfson, M. (2007). Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials. Statistical Methods in Medical Research, 16, 167–184.doi: 10.1177/0962280206071931
   PubMed Web of Science ®Google Scholar