Prevention Science

, Volume 4, Issue 2, pp 89–98 | Cite as

Individual and School Level Effects of Perceived Harm, Perceived Availability, and Community Size on Marijuana Use Among 12th-Grade Students: A Random Effects Model

  • Randall C. Swaim


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.

school effects marijuana adolescent perceived harm perceived availability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aitkin, M., & Longford, N. (1986). Statistical modelling issues in school effectiveness studies. Journal of the Royal Statistical Society, 149, 1-43.Google Scholar
  2. Ashby, J. S. (1995). Impact of contextual variables on adolescent situational expectation of substance use. Journal of Drug Education, 25, 11-22.Google Scholar
  3. 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.Google Scholar
  4. 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.Google Scholar
  5. 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.Google Scholar
  6. Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and data analysis methods, (pp. 17-18, 61-64). Newbury Park, CA: SageGoogle Scholar
  7. Bryk, A. S., Raudenbush, S. W., & Congdon, R. T., Jr., (1996). HLM for Windows (Version 4.01.01). Chicago: Scientific Software InternationalGoogle Scholar
  8. 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 EducationGoogle Scholar
  9. 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.Google Scholar
  10. Donner, A., Birkett, N., & Buck, C. (1981). Randomization by cluster. American Journal of Epidemiology, 114, 906-914.Google Scholar
  11. 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 AbuseGoogle Scholar
  12. 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.Google Scholar
  13. Gorsuch, R. L., & Butler, M. C. (1976). Initial drug abuse: A review of predisposing social psychological ractors. Psychological Bulletin, 83, 120-137.Google Scholar
  14. Gottfredson, D. C. (1988). An evaluation of an organization development approach to reducing school disorder. Evaluation Review, 11, 739-763.Google Scholar
  15. 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.Google Scholar
  16. Institute of Medicine. (1994). Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC: National Academy Press.Google Scholar
  17. 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.Google Scholar
  18. 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 AbuseGoogle Scholar
  19. Kish, L. (1965). Survey sampling (pp. 257-259). New York: Wiley.Google Scholar
  20. Lee, V., & Bryk, A. (1989). A multilevel model of the social distribution of high school achievement. Sociology of Education, 62, 172-192.Google Scholar
  21. 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.Google Scholar
  22. 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.Google Scholar
  23. Mason, W. M., Anderson, A. F., & Hayat, N. (1988). Manual for GENMOD. Ann Arbor, MI: University of Michigan, Population Studies Center.Google Scholar
  24. 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.Google Scholar
  25. 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.Google Scholar
  26. Oetting, E. R., & Beauvais, F. (1990). Adolescent drug use: Findings of national and local surveys. Journal of Consulting and Clinical Psychology, 58, 385-394.Google Scholar
  27. 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, IncGoogle Scholar
  28. 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.Google Scholar
  29. 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.Google Scholar
  30. Rabash, J., Prosser, R., & Goldstein, H. (1989). ML2: Software for two-level Analysis. User's guide. London: University of London, Institute of Education.Google Scholar
  31. 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.Google Scholar
  32. 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.Google Scholar
  33. 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.Google Scholar
  34. 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.Google Scholar
  35. 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.Google Scholar
  36. 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.Google Scholar

Copyright information

© Society for Prevention Research 2003

Authors and Affiliations

  • Randall C. Swaim
    • 1
  1. 1.Tri-Ethnic Center for Prevention Research, Department of PsychologyColorado State UniversityFort Collins

Personalised recommendations