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Prevention Science

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01.09.2006 | Original Paper

The Multilevel Structure of Four Adolescent Problems

verfasst von: Keith Smolkowski, Anthony Biglan, Clyde Dent, John Seeley

Erschienen in: Prevention Science | Ausgabe 3/2006

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Abstract

This paper examines variability in adolescent self-reported behavior at the individual, cohort, and school levels for 8th and 11th graders. We examine four adolescent behaviors: substance use, antisocial behavior, depression, and academic performance. Research staff collected the data as part of the Oregon Healthy Teens survey of a population-based sample of 60,837 adolescents over three years in 92 communities. The results indicate that schools vary over time, but not necessarily systematically, and grade-level cohorts account for important variance within schools. The school and cohort combined, however, accounted for at most 4% of the overall variance. The results have implications for research and practice in schools and communities. For example, selection of communities for interventions based on high levels of adolescent problems may be unproductive if individuals account for at least 96% of the variance. Furthermore, in non-experimental designs, cohort variability, not an intervention, may account for apparent improvement across years.

The pattern of results for drugs used and antisocial behavior were nearly identical to those of log transformations of the variables. We presented the results from the untransformed variables to aid interpretability.

We compared variance estimates between the FIML and REML estimates. The differences in school-level variance estimates were negligible and would not have changed our interpretation.

Arthur, M. W., Hawkins, J. D., Pollard, J. A., Catalano, R. F., & Baglioni, A. J., Jr. (2002). Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors: The Communities that Care youth survey. Evaluation Review, 26(6), 355–381.CrossRef

Aveyard, P., Markham, W. A., & Cheng, K. K. (2004). A methodological and substantive review of the evidence that schools cause pupils to smoke. Social Science & Medicine, 58(11), 2253–2265.CrossRef

Benson, P. L. (1998). Mobilizing communities to promote developmental assets: A promising strategy for the prevention of high-risk behaviors. Family Science Review, 11(3), 220–238.

Biglan, A., Ary, D. V., Smolkowski, K., Duncan, T. E., & Black, C. (2000). A randomized control trial of a community intervention to prevent adolescent tobacco use. Tobacco Control, 9, 24–32.PubMedCrossRef

Biglan, A., Ary, D. V., & Wagenaar, A. C. (2000). The value of interrupted time-series experiments for community intervention research. Prevention Research, 1(1), 31–49.

Biglan, A., Brennan, P. A., Foster, S. L., Holder, H. D., Miller, T. L., Cunningham, P. B. et al. (2004). Helping adolescents at risk: Prevention of multiple problem behaviors. New York: Guilford.

Birnbaum, A. S., Lytle, L. A., Hannan, P. J., Murray, D. M., Perry, C. L., & Forster, J. L. (2003). School functioning and violent behavior among young adolescents: a contextual analysis. Health Education Research, 18(3), 389–403.PubMedCrossRef

Boles, S. M., Biglan, A., & Smolkowski, K. (2005). Relationships among negative and positive behaviors in adolescence. Journal of Adolescence, 29(1), 33–52.PubMedCrossRef

Bryk, A. S., & Raudenbush, S. W. (1988). Toward a more appropriate conceptualization of research on school effects. American Journal of Education, 97(1), 65–108.CrossRef

Burnham, K. P., & Anderson, D. R. (2002). Model selection and multi-model inference: A practical information-theoretic approach. Berlin Heidelberg New York: Springer-Verlag.

Crockett, L. J., Schulenberg, J. E., & Petersen, A. C. (1987). Congruence between objective and self-report data in a sample of young adolescents. Journal of Adolescent Research, 2, 383–392.CrossRef

Cross-National Collaborative Group (1992). The changing rate of major depression. Journal of the American Medical Association, 268, 3098–3105.CrossRef

Dishion, T. J., & Owen, L. D. (2002). A longitudinal analysis of friendships and substance use: Bidirectional influence from adolescence to adulthood. Developmental Psychology, 38, 480–491.PubMedCrossRef

Donner, A., Birkett, N. J., & Buck, C. (1981). Randomization by cluster: Sample size requirements and analysis. American Journal of Epidemiology, 114(6), 906–914.PubMed

Donner, A., & Klar, N. (1996). Statistical considerations in the design and analysis of community intervention trials. Journal of Clinical Epidemiology, 49(4), 435–439.PubMedCrossRef

Elliott, S. N., Busse, R. T., & Gresham, F. M. (1993). Behavior rating scales: Issues of use and development. School Psychology Review, 22(2), 313–321.

Farmer, T. W., Estell, D. B., Leung, M.-C., Trott, H., Bishop, J. L., & Cairns, B. D. (2003). Individual characteristics, early adolescent peer affiliations, and school dropout: An examination of aggressive and popular school types. Journal of School Psychology, 41, 217–232.CrossRef

Forster, M. R. (2000). Key concepts in model selection: Performance and generalizability. Journal of Mathematical Psychology, 44, 205–231.PubMedCrossRef

Gibbons, R. D., Hedeker, D., Elkin, I., Waternaux, C., Kraemer, H. C., Greenhouse, J. B. et al. (1993). Some conceptual and statistical issues in analysis of longitudinal psychiatric data: Application to the NIMH treatment of depression collaborative research program dataset. Archives of General Psychiatry, 50, 739–750.PubMed

Hannan, P. J., & Murray, D. M. (1996). Gauss or Bernoulli? A Monte Carlo comparison of the performance of the linear mixed model and the logistic mixed model analyses in simulated community trials with a dichotomous outcome variable at the individual level. Evaluation Review, 20(3), 338–352.PubMedCrossRef

Hawkins, J. D., Van Horn, M. L., & Arthur, M. W. (2004). Community variation in risk and protective factors and substance use outcomes. Prevention Science, 5(4), 213–220.PubMedCrossRef

Hox, J. (2002). Multilevel analysis techniques and applications. Mahwah, NJ: Erlbaum.

Jencks, C., Crouse, J., & Mueser, P. (1983). The Wisconsin model of Status Attainment: A national replication with improved measures of ability and aspiration. Sociology of Education, 56(1), 3– 19.CrossRef

Kann, L., Kinchen, S. A., Williams, B. I., Ross, J. G., Lowry, R., Grunbaum, J. A. et al. (2000). Youth Risk Behavior Surveillance–United States, 1999. Morbidity and Mortality Weekly Report, 49(SS-5), 1–94.

Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R. et al. (2003). The epidemiology of major depressive disorder: Results from the National Comorbidity Survey Replication (NCS-R). JAMA: Journal of the American Medical Association, 289(23), 3095–3105.PubMedCrossRef

Kiesner, J. Dishion, T. J., & Poulin, F. (2001). A reinforcement model of conduct problems in children and adolescents: Advances in theory and intervention. In J. Hill & B. Maughan (Eds.), Conduct disorders in childhood and adolescence. New York: Cambridge University Press.

Kiesner, J., Poulin, F., & Nicotra, E. (2003). Peer relations across contexts: Individual-network homophily and network inclusion in and after school. Child Development, 74(5), 1328–1343.PubMedCrossRef

Klerman, G. L. & Weissman, M. M. (1989). Increasing rates of depression. Journal of the American Medical Association, 261, 2229–2235.PubMedCrossRef

Kreft, I., & de Leeuw, J. (1998). Introducing multilevel modeling. London: Sage.

Lansford, J. E., Criss, M. M., Pettit, G. S., Dodge, K. A., & Bates, J. E. (2003). Friendship quality, peer group affiliation, and peer antisocial behavior as moderators of the link between negative parenting and adolescent externalizing behavior. Journal of Research on Adolescence, 13, 161–184.

Lewinsohn, P. M., Hops, H., Roberts, R. E., Seeley, J. R., & Andrews, J. A. (1993). Adolescent psychopathology: I. Prevalence and incidence of depression and other DSM-III-R disorders in high school students. Journal of Abnormal Psychology, 102, 133–144.PubMedCrossRef

Lewinsohn, P. M., Rohde, P., & Seeley, J. R. (1994). Psychosocial risk factors for future adolescent suicide attempts. Journal of Consulting and Clinical Psychology, 62, 297–305.PubMedCrossRef

Lewinsohn, P. M., Rohde, P., Seeley, J. R., & Fischer, S. A. (1993). Age-cohort changes in the lifetime occurrence of depression and other mental disorders. Journal of Abnormal Psychology, 102, 110–120.PubMedCrossRef

Lewinsohn, P. M., Rohde, P., Seeley, J. R., Klein, D. N., & Gotlib, I. H. (2003). Psychosocial functioning of young adults who have experienced and recovered from major depressive disorder during adolescence. Journal of Abnormal Psychology, 112, 353–363.PubMedCrossRef

Little, R. J. A. (1995). Modeling the dropout mechanism in repeated-measures studies. Journal of the American Statistical Association, 90, 1112–1121.CrossRef

Maas, C. J. M., & Hox, J. J. (2004). The influence of violations of assumptions on multilevel parameter estimates and their standard errors. Computational Statistics and Data Analysis, 46, 427–440.CrossRef

Metzler, C. W., Biglan, A., Ary, D. V., & Li, F. (1998). The stability and validity of early adolescents’ reports of parenting constructs. Journal of Family Psychology, 12(4), 600–619.CrossRef

Mouttapa, M., Valente, T., Gallaher, P., Rohrbach, L. A., Unger, J. B. (2004). Social network predictors of bullying and victimization. Adolescence, 39(154), 315–35.PubMed

Mrazek, P. J., Biglan, A., & Hawkins, J. D. (2004). Community-monitoring systems: Washington, DC: Society for Prevention Research.

Murray, D. M. (1998). Design and analysis of group-randomized trials. New York: Oxford University Press.

Murray, D. M. (2001). Statistical models appropriate for designs often used in group-randomized trials. Statistics in Medicine, 20, 1373–1385.PubMedCrossRef

Murray, D. M., Clark, M. H., & Wagenaar, A. C. (2000). Intraclass correlations from a community-based alcohol prevention study: The effect of repeat observations on the same communities. Journal of Studies in Alcohol, 61(6), 881–890.

Murray, D. M., Hannan, P. J., & Baker, W. L. (1996). A Monte Carlo study of alternative responses to intraclass correlation in community trials: Is it ever possible to avoid Cornfield's penalties? Evaluation Review, 20(3), 313–337.PubMedCrossRef

Murray, D. M., Hannan, P. J., Varnell, S. P., McCowen, R. G., Baker, W. L., & Blitstein, J. L. (in press). A comparison of permutation and mixed-model regression methods for the analysis of simulated data in the context of a group-randomized trial. Statistics in Medicine.

Murray, D. M., Hannan, P. J., Wolfinger, R. D., Baker, W. L., & Dwyer, J. H. (1998). Analysis of data from group-randomized trials with repeat observations on the same groups. Statistics in Medicine, 17, 1581–1600.PubMedCrossRef

Murray, D. M., Rooney, B. L., Hannan, P. J., Ary, D. V., Biglan, A., Botvin, G. J. et al. (1994). Intraclass correlation among common measures of adolescent smoking: Estimates, correlates and applications in smoking prevention studies. American Journal of Epidemiology, 140(11), 1038–1050.PubMed

Murray, D. M., & Short, B. (1995). Intraclass correlation among measures related to alcohol use by young adults: Estimates, correlates and applications in intervention studies. Journal of Studies on Alcohol, 56(6), 681–694.PubMed

Murray, D. M. & Short, B. J. (1997). Intraclass correlation among measures related to tobacco use by adolescents: Estimates, correlates and applications in intervention studies. Addictive Behaviors, 22(1), 1–12.PubMedCrossRef

Murray, D. M., Varnell, S. P., & Blitstein, J. L. (2004). Design and analysis of group-randomized trials: A review of recent methodological developments. American Journal of Public Health, 94(3), 423–432.PubMed

Myung, I. J. (2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190–204.PubMedCrossRef

Myung, I. J., Forster, M. R., & Browne, M. W. (2000). Guest editors’ introduction: Special issue on model selection. Journal of Mathematical Psychology, 44, 1–2.PubMedCrossRef

Newman, D. L., Moffitt, T. E., Caspi, A., Magdol, L., Silva, P. A., & Stanton, W. R. (1996). Psychiatric disorder in a birth cohort of young adults: Prevalence, comorbidity, clinical significance, and new case incidence from ages 11 to 21. Journal of Consulting and Clinical Psychology, 64, 552–562.PubMedCrossRef

Opdenakker, M. C., & Van Damme, J. (2000). The importance of identifying levels in multilevel analysis: An illustration of the effects of ignoring the top or intermediate levels in school effectiveness research. School Effectiveness and School Improvement, 11(1), 103–130.CrossRef

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage.

Reinherz, H. Z., Giaconia, R. M., Carmola-Hauf, A. M., Wasserman, M. S., & Silverman, A. B. (1999). Major depression in the transition to adulthood: Risks and impairments. Journal of Abnormal Psychology, 108(3), 500–510.PubMedCrossRef

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.CrossRef

Seeley, J. R., Rohde, P., Lewinsohn, P. M., & Clarke, G. N. (2002). Depression in youth: Epidemiology, identification, and intervention. In M. Shinn & H. Walker (Eds.) Interventions for academic and behavior problems II: Preventive and remedial approaches Silver Spring, MD: National Association of School Psychologists.

Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 24(4), 323–355.

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.

Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage.

Swaim, R. C. (2003). Individual and school level effects of perceived harm, perceived availability, and community size on marijuana use among 12th-grade students: A random effects model. Prevention Science, 4(2), 89–98.PubMedCrossRef

Urberg, K. A., Degirmencioglu, S. M., Tolson, J. M., & Halliday-Scher, K. (1995). The structure of adolescent peer networks. Developmental Psychology, 4, 540–547.CrossRef

Varnell, S. P., Murray, D. M., Janega, J. B., & Blitstein, J. L. (2004). Design and analysis of group-randomized trials: A review of recent practices. American Journal of Public Health, 94(3), 393–399.PubMedCrossRef

Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. Berlin Heidelberg New York: Springer-Verlag.

Wagenmakers, E. J., & Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic Bulletin & Review, 11(1), 192–196.

Zucchini, W. (2000). An introduction to model selection. Journal of Mathematical Psychology, 44, 41–61.PubMedCrossRef

Titel: The Multilevel Structure of Four Adolescent Problems
verfasst von: Keith Smolkowski
Anthony Biglan
Clyde Dent
John Seeley
Publikationsdatum: 01.09.2006
Verlag: Springer US
Erschienen in: Prevention Science / Ausgabe 3/2006
Print ISSN: 1389-4986
Elektronische ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-006-0034-5

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