Abstract
A hierarchical linear model was used to estimate the individual and school level effects for marijuana use among a national sample of 12th-grade students. School effects were small in comparison to individual level effects, accounting for 2.9% of the variance in marijuana use. At the individual level, perceived harm, perceived availability, and their interaction were significant predictors, each of which varied randomly across schools. Among two school-level predictors, the normative environment for perceived harm was not significant, but normative perceived availability predicted level of marijuana use. The effect of perceived availability on marijuana use was stronger in larger, compared to smaller communities. Results are discussed in light of the use of random regression methods for identifying school-specific patterns of risk and protection for prevention planning.
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REFERENCES
Aitkin, M., & Longford, N. (1986). Statistical modelling issues in school effectiveness studies. Journal of the Royal Statistical Society, 149, 1-43.
Ashby, J. S. (1995). Impact of contextual variables on adolescent situational expectation of substance use. Journal of Drug Education, 25, 11-22.
Bachman, J. G., Johnston, L. D., & O'Malley, P. M. (1990). Explaining the recent decline in cocaine use among young adults: Further evidence that oerceived risks and disapproval lead to reduced drug use. Journal of Health and Social Behavior, 31, 173-184.
Bachman, J. G., Johnston, L. D., O'Malley, P. M., & Humphrey, R. H. (1988). Explaining the recent decline in marijuana use: Differentiating the effects of perceived risks, disapproval, and general lifestyle factors. Journal of Health and Social Behavior, 29, 92-112.
Battistich, V., & Hom, A. (1997). The relationship between students' sense of their school as a community and their involvement in problem behaviors. American Journal of Public Health, 87, 1997-2001.
Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and data analysis methods, (pp. 17-18, 61-64). Newbury Park, CA: Sage
Bryk, A. S., Raudenbush, S. W., & Congdon, R. T., Jr., (1996). HLM for Windows (Version 4.01.01). Chicago: Scientific Software International
Bryk, A. S., Raudenbush, S. W., Seltzer, M., & Congdon, R. (1988). An introduction to HLM: Computer program and user's guide (2nd ed.). Chicago: University of Chicago, Department of Education
Dembo, R., Farrow, D., Schmeidler, J., & Burgos, W. (1979). Testing a causal model of environmental influences on early drug involvement of inner city junior high school youths. American Journal of Drug and Alcohol Abuse, 6, 313-336.
Donner, A., Birkett, N., & Buck, C. (1981). Randomization by cluster. American Journal of Epidemiology, 114, 906-914.
Edwards, R. W. (1997). Drug and alcohol use among youth in rural communities. In E. Robertson, Z. Sloboda, G. Boyd, L. Beatty, & N. Kozel (Eds.), Rural substance abuse: State of knowledge and issues (NIDA Research Monograph 168, pp. 53-75). Rockville, MD: National Institute on Drug Abuse
Ennett, S. T., Flewelling, R. L., Lindrooth, R. C., & Norton, E. C. (1997). School and neighborhood characteristics associated with school rates of alcohol, cigarette, and marijuana use. Journal of Health and Social Behavior, 38, 55-71.
Gorsuch, R. L., & Butler, M. C. (1976). Initial drug abuse: A review of predisposing social psychological ractors. Psychological Bulletin, 83, 120-137.
Gottfredson, D. C. (1988). An evaluation of an organization development approach to reducing school disorder. Evaluation Review, 11, 739-763.
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.
Institute of Medicine. (1994). Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC: National Academy Press.
Johnson, R. A., & Hoffmann, J. P. (2000). Adolescent cigarette smoking in U.S. racial/ethnic subgroups: Findings from the National Education Longitudinal Study. Journal of Health and Social Behavior, 41, 392-407.
Johnston, L. D., O'Malley, P. M., & Bachman, J. G. (2001). National survey results on drug use from The Monitoring the Future Study, 1975-1993 NIH Pub. No. 94-3809, (pp. 67-68, 118-122). Rockville, MD: National Institute on Drug Abuse
Kish, L. (1965). Survey sampling (pp. 257-259). New York: Wiley.
Lee, V., & Bryk, A. (1989). A multilevel model of the social distribution of high school achievement. Sociology of Education, 62, 172-192.
Longford, N. T. (1988). Fisher scoring algorithm for variance component analysis of data with multilevel structure. In R. D. Bock (Ed.), Multilevel analysis of educational data (pp. 297-310). Orlando, FL: Academic Press.
Maddahian, E., Newcomb, M. D., & Bentler, P. M. (1988). Adolescent drug use and intention to use drugs: Concurrent and longitudinal analyses of four ethnic groups. Addictive Behaviors, 13, 191-195.
Mason, W. M., Anderson, A. F., & Hayat, N. (1988). Manual for GENMOD. Ann Arbor, MI: University of Michigan, Population Studies Center.
Murray, D. M., & Hannan, P. J. (1990). Planning for the appropriate analysis in school-based drug-use prevention studies. Journal of Consulting and Clinical Psychology, 58, 458-468.
Murray, D. M., Rooney, B. L., Hannan, P. J., Peterson, A. V., Ary, D. V., Biglan, A., Botvin, G. J., Evans, R. I., Flay, B. R., Futterman, R., Getz, J. G., Marek, P. M., Orlandi, M., Pentz, M. A., Perry, C. L., & Schinke, S. P. (1994). Intraclass correlation among common measures of adolescent smoking: Estimates, correlates, and applications in smoking prevention studies. American Journal of Epidemiology, 140, 1038-1050.
Oetting, E. R., & Beauvais, F. (1990). Adolescent drug use: Findings of national and local surveys. Journal of Consulting and Clinical Psychology, 58, 385-394.
Oetting, E. R., Beauvais, F., Edwards, R. W., & Waters, M. (1984). The drug and alcohol assessment system: Book II: Instrument development, reliability and validity. Fort Collins, CO: Rocky Mountain Behavioral Sciences Institute, Inc
Petraitis, J., Flay, B. R., & Miller, T. Q. (1995). Reviewing theories of adolescent substance use: Organizing pieces in the puzzle. Psychological Bulletin, 117, 67-86.
Pinilla, J., Gonzalez, B., Barber, P., & Santana, Y. (2002). Smoking in young adolescents: An approach with multilevel discrete choice models. Journal of Epidemiology and Community Health, 56, 227-232.
Rabash, J., Prosser, R., & Goldstein, H. (1989). ML2: Software for two-level Analysis. User's guide. London: University of London, Institute of Education.
Resnicow, K., Smith, M., Harrison, L., & Drucker, E. (1999). Correlates of occasional cigarette and marijuana use: Are teens harm reducing? Addictive Behaviors, 24, 251-266.
Rountree, P. W., & Clayton, R. R. (1999). A contextual model of adolescent alcohol use across the rural-urban continuum. Substance Use and Misuse, 34, 495-519.
Siddiqui, O., Hedeker, D., Flay, B. R., & Hu, F. B. (1996). Intraclass correlation estimates in a school-based smoking prevention study. American Journal of Epidemiology, 144, 425-433.
Thompson, E. A., Horn, M., Herting, J. R., & Eggert, L. L. (1997). Enhancing outcomes in an indicated drug prevention program for high risk youth. Journal of Drug Education 27, 19-41.
Unger, J. B., Cruz, T. B., Rohrbach, L. A., Ribisl, K. M., Baezconde-Garbanati, L., Chen, X., Trinidad, D. R., & Johnson, C. A. (2000). English language use as a risk factor for smoking initiation among Hispanic and Asian American adolescents: Evidence for mediation by tobacco-related beliefs and social norms. Health Psychology, 19, 403-410.
Unger, J. B., Rohrbach, L. A., Howard-Pitney, B., Ritt-Olson, A., & Mouttapa, M. (2001). Peer influences and susceptibility to smoking among California adolescents. Substance Use and Misuse, 36, 551-571.
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Swaim, R.C. Individual and School Level Effects of Perceived Harm, Perceived Availability, and Community Size on Marijuana Use Among 12th-Grade Students: A Random Effects Model. Prev Sci 4, 89–98 (2003). https://doi.org/10.1023/A:1022922231605
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DOI: https://doi.org/10.1023/A:1022922231605