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A Multivariate Associative Finite Growth Mixture Modeling Approach Examining Adolescent Alcohol and Marijuana Use

  • Hollie Hix-Small
  • Terry E. Duncan
  • Susan C. Duncan
  • Hayrettin Okut
Article

Abstract

Theoretical and empirical substance use development research suggests that adolescent populations are not homogenous and can often be separated into subpopulations characterized by qualitatively different patterns of substance use development. This paper demonstrates the application of a multivariate associative finite latent growth mixture modelling approach to examine heterogeneity in patterns of adolescent alcohol and marijuana use and the influence of age, gender, parent, and peer substance use. Substance use problem outcomes were also examined. Participants were male and female adolescents (N = 1,044) ranging in age from 11 to 17 years at the first assessment (Mean age = 14.47; SD = 1.95). Individuals were 45% female and 82% Caucasian. Using growth mixture methodology, a 7-class model captured distinct simultaneous alcohol and marijuana use patterns over a 3-year period. Findings highlight the importance of examining subgroups of adolescent substance use, rather than focusing only on single samples.

Growth Generalized Mixture Modeling substance use development associative Latent Growth Model Parallel-Process Growth Model 

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REFERENCES

  1. Anthony, J. C., & Petronis, K. R. (1995). Early-onset drug use and risk of later drug problems. Drug and Alcohol Dependence, 40,9–15.Google Scholar
  2. Bates, M. E. & Labouvie, E. W. (1997). Adolescent risk factors and the prediction of persistent alcohol and drug use into adulthood. Alcohol Clinical Experimental Research, 21,944–950.Google Scholar
  3. Bukstein, O. G. (1995). Adolescent substance abuse: Assessment, prevention and treatment. New York: Wiley.Google Scholar
  4. Collins, L., & Sayer, A. (2001). Niew methods for the analysis of change. Washington, DC: American Psychological Association.Google Scholar
  5. Coombs, R. H., Paulson, M. J., & Richardson, M. A. (1991). Peer vs. parental influence in substance use among Hispanic and Anglo children and adolescents. Journal of Youth and Adolescence, 20 ,73–88.Google Scholar
  6. Costello, E. J., Erkanli, A., Federman, E., & Angold, A. (1999). Development of psychiatric comorbidity with substance abuse in adolescents: Effects of timing and sex. Journal of Clinical Child Psychology, 28,298–311.Google Scholar
  7. Donovan, J. E. (1996). Problem-behavior theory and the explanation of adolescent marijuana use. Journal of Drug Issues, 26,379–404.Google Scholar
  8. Donovan, J. E., & Jessor, R. (1985). Structure of problem behavior in adolescence and young adulthood. Journal of Consulting and Clinical Psychology, 53,890–904.Google Scholar
  9. Donovan, J. E., Jessor, R., & Costa, F. M. (1988). Syndrome of problem behavior in adolescence: A replication. Journal of Consulting and Clinical Psychology, 56,762–765.Google Scholar
  10. Duncan, S. C., & Duncan, T. E. (1994). Modeling incomplete lon-gitudinal substance use data using latent variable growth curve methodology. Multivariate Behavioral Research, 29,313–338.Google Scholar
  11. Duncan, S. C., & Duncan, T. E. (1996). A multivariate latent growth curve analysis of adolescent substance use. Structural Equation Modeling, 3,323–347.Google Scholar
  12. Duncan, T. E., Duncan, S. C., & Hops, H. (1994). The effects of family cohesiveness and peer encouragement on the development of ado-lescent alcohol use: A cohort-sequential approach to the analysis of longitudinal data. Journal of Studies on Alcohol, 55,588–599.Google Scholar
  13. Duncan, T. E., Duncan, S. C., & Hops, H. (1996). The role of parents and older siblings in predicting adolescent substance use: Mod-eling development via structural equation latent growth curve methodology. Journal of Family Psychology, 10(2), 158-172.Google Scholar
  14. Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., & Alpert, A. (1999). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Erlbaum.Google Scholar
  15. Duncan, T. E., Susan, S. C., Strycker, L. A., Okut, H., & Li, F. (2002). Growth mixture modeling of adolescent alcohol use data. Retrieved from Oregon Research Institute Web site: http://www.ori.org/methodologyGoogle Scholar
  16. Elder, G. H. (1980). Family structure and socialization. New York: Arno Press.Google Scholar
  17. Elliott, D. S., Huizinga, D., & Menard, S. (1989). Multiple problem youth: Delinquency, substance use, and mental health problems. New York: Springer-Verlag.Google Scholar
  18. Farrell, A. D., Danish, S. J., & Howard, C. W. (1992). Relationship be-tween drug use and other problem behaviors in urban adolescents. Journal of Consulting and Clinical Psychology, 60,705–712.Google Scholar
  19. Hanna, E. Z., & Grant, B. F. (1999). Parallels to early onset alcohol use in the relationship of early onset smoking with drug use and DSM-IVdrug and depressive disorders: Findings from the National Longitudinal Epidemiologic Survey. Alcoholism: Clinical and Experimental Research, 23,513–522.Google Scholar
  20. Hansen, W. B., Graham, J. W., Sobel, J. L., Shelton, D. R., Flay, B. R., & Johnson, C. A. (1987). The consistency of peer and parent influences on tobacco, alcohol, and marijuana use among young adolescents. Journal of Behavioral Medicine, 10,559–579.Google Scholar
  21. Hawkins, J., Catalano, R., & Miller, J. (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
  22. Hops, H., Duncan, T. E., & Duncan, S. C. (1996). Parent substance use as a predictor of adolescent use: A six-year lagged analysis. Annals of Behavioral Medicine, 18(3), 157–164.Google Scholar
  23. Jessor, R. (1987). Problem-behavior theory, psychosocial development, and adolescent problem drinking. British Journal of Addiction, 82,331-342.Google Scholar
  24. Jessor, R., & Jessor, S. L. (1977). Problem behavior and psychosocial development. New York: Academic Press.Google Scholar
  25. Johnston, L. D., O'Malley, P. M., & Bachman, J. G. (1996). National survey results on drug use from the Monitoring the Future Study, 1975-1995. Washington, DC: National Institute on Drug Abuse.Google Scholar
  26. Johnston, L. D., O'Malley, P. M., & Bachman, J. G. (2002). Monitoring the Future national results on adolescent drug use. Bethesda, MD: National Institute on Drug Abuse.Google Scholar
  27. Kandel, D. B. (1985). On processes of peer influences in adolescent drug use: A developmental perspective. Advances in Alcohol and Substance Abuse, 4 ,139–163.Google Scholar
  28. Li, F., Duncan, T. E., & Hops, H. (2001). Examining developmental tra-jectories in adolescent alcohol use using piecewise growth mixture modeling analysis. Journal of Studies on Alcohol, 62,199–210.Google Scholar
  29. Lo, Y., Mendell, N., & Rubin, D. (2001). Testing the number of components in a normal mixture. Biometrika, 88,767–778.Google Scholar
  30. Loeber, R. (1988). Natural histories of conduct problems, delinquency, and associated substance use: Evidence for developmental pro-gressions. In B. B. Lahey, & A. E. Kazdin, (Eds.), Advances in clinical child psychology (pp. 73–124). New York: Plenum Press.Google Scholar
  31. McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In R. B. Cattel & J. Nesselroade (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 561–614). New York: Plenum Press.Google Scholar
  32. McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58,110-133.Google Scholar
  33. McArdle, J. J., & Hamagami, F. (1991). Modeling incomplete lon-gitudinal and cross-sectional data using latent growth structural models. In L. M. Collins & J. C. Horn (Eds.), Best methods for the analysis of change (pp. 276–304). Washington, DC: American Psychological Association.Google Scholar
  34. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55,107–122.Google Scholar
  35. Muthén, B. (2000). Methodological issues in random coefficient growth modeling using a latent variable framework: Applications to the development of heavy drinking. In J. Rose, L. Chassin, C. Presson, & J. Sherman (Eds.), Multivariate applications in substance use research (pp. 113–140). Hillsdale, NJ: Erlbaum.Google Scholar
  36. Muthén, B. O. (2001). Second-generation structural equation modeling with combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis for change (pp. 291–322). Washington, DC: American Psychological Association.Google Scholar
  37. Muthén, B., Brown, C. H., Khoo, S., Yang, C., & Jo, B. (1998). General growth mixture modeling of latent trajectory classes: Perspectives and prospects. Paper presented at the meeting of the Prevention Science and Methodology Group, Tempe, AZ.Google Scholar
  38. Muthén, B., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24,882–891.Google Scholar
  39. Muthén, L.K., & Muthén, B. (1998). Mplus: User's guide. Los Angeles: Muthén & Muthén.Google Scholar
  40. Muthén, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55,463–469.Google Scholar
  41. Nagin, D. S. (1999). Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods, 4,139–157.Google Scholar
  42. 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.Google Scholar
  43. Osgood, D. W., Johnston, L. D., O'Malley, P. M., & Bachman, J. G. (1988). The generality of deviance in late adolescence and early adulthood. American Sociology Review, 53,81–93.Google Scholar
  44. Patterson, G. R., & Dishion, T. J. (1985). Contributions of families and peers to delinquency. Criminology, 23,63–79.Google Scholar
  45. Ramaswamy, V., DeSarbo, W., Reibstein, D., & Robinson, W. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12,103–124.Google Scholar
  46. Schafer, J. L. (1994). Multivariate normal multiple imputation algo-rithms. Philadelphia: Pennsylvania State University, Department of Statistics.Google Scholar
  47. Schulenberg, J., O'Malley, P. M., Bachman, J. G., Wadsworth, K. N., & Johnston, L. D. (1996). Getting drunk and growing up: Trajectories of frequent binge drinking during the transition to young adulthood. Journal of Studies on Alcohol, 57,289–304.Google Scholar
  48. Substance Abuse and Mental Health Services Administration. (1998). Preliminary results from the 1997 National Household Survey on Drug Abuse (Publication No. (SMA) 98-3251). Rockville, MD: U.S. Department of Health and Human Services.Google Scholar
  49. U.S. Department of Health and Human Services. (1993). Eighth Special Report to the US Congress on Alcohol and Health. Rockville, MD: Author.Google Scholar
  50. Wang, P., Puterman, M., & Cockburn, I. (1998). Analysis of patent data: A mixed poisson regression model approach. Journal of Business Economic Statistics, 16(1), 27–41.Google Scholar
  51. Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116,363–381.Google Scholar
  52. Yang, C. C. (1998). Finite mixture model selection with psychometric applications. Unpublished doctoral dissertation, University of California, Los Angeles.Google Scholar
  53. Zucker, R. A. (1994). Pathways to alcohol problems and alcoholism: A developmental account of the evidence for multiple alcoholism and for contextual contributions to risk. In R. A. Zuker, J. Howard, and G. M. Boyd (Eds.), The development of alcohol problems: Exploring the biopsychosocial matrix of risk (pp. 255–289). Rockville, MD: U.S. Department of Health and Human Services.Google Scholar
  54. Zucker, R. A., Fitzgerald, H. E., & Moses, H. D. (1995). Emergence of alcohol problems and the several alcoholisms: A developmental perspective on etiologic theory and life course trajectory. In D. Cicchetti & D.J. Cohen (Eds.), Development psychopathology: Risk, disorder, and adaptation (Vol. 2, pp. 677–711). New York: Wiley.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2004

Authors and Affiliations

  • Hollie Hix-Small
    • 1
  • Terry E. Duncan
    • 1
  • Susan C. Duncan
    • 1
  • Hayrettin Okut
    • 1
  1. 1.Oregon Research InstituteEugene

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