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A model-based cluster analysis approach to adolescent problem behaviors and young adult outcomes

Published online by Cambridge University Press:  23 January 2008

Eun Young Mun ,
Michael Windle  and
Lisa M. Schainker
Eun Young Mun*
Affiliation:
Rutgers University
Michael Windle
Affiliation:
Emory University
Lisa M. Schainker
Affiliation:
Iowa State University
*
Address correspondence and reprint requests to: Eun Young Mun, Rutgers Center of Alcohol Studies, Rutgers University, 607 Allison Road, Piscataway, NJ 08854; E-mail: [email protected].
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Abstract

Data from a community-based sample of 1,126 10th- and 11th-grade adolescents were analyzed using a model-based cluster analysis approach to empirically identify heterogeneous adolescent subpopulations from the person-oriented and pattern-oriented perspectives. The model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities and accordingly to classify subpopulations using more rigorous statistical procedures for the comparison of alternative models. Four cluster groups were identified and labeled multiproblem high-risk, smoking high-risk, normative, and low-risk groups. The multiproblem high risk exhibited a constellation of high levels of problem behaviors, including delinquent and sexual behaviors, multiple illicit substance use, and depressive symptoms at age 16. They had risky temperamental attributes and lower academic functioning and educational expectations at age 15.5 and, subsequently, at age 24 completed fewer years of education, and reported lower levels of physical health and higher levels of continued involvement in substance use and abuse. The smoking high-risk group was also found to be at risk for poorer functioning in young adulthood, compared to the low-risk group. The normative and the low risk groups were, by and large, similar in their adolescent and young adult functioning. The continuity and comorbidity path from middle adolescence to young adulthood may be aided and abetted by chronic as well as episodic substance use by adolescents.

Type
Research Article
Information
Development and Psychopathology , Volume 20 , Issue 1 , Winter 2008 , pp. 291 - 318
Copyright
Copyright © Cambridge University Press 2008

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Footnotes

This research was supported by the National Institute on Alcohol Abuse and Alcoholism Grant R37-AA07861 awarded to Michael Windle. We are grateful to Alexander von Eye for his helpful comments on an earlier version of this manuscript.

References

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.Google Scholar
Arminger, G., & Stein, P. (1997). Finite mixtures of covariance structure models with regressors. Sociological Methods and Research, 26, 148–182.CrossRefGoogle Scholar
Arminger, G., Stein, P., & Wittenberg, J. (1999). Mixtures of conditional mean- and covariance-structure models. Psychometrika, 64, 475–494.CrossRefGoogle Scholar
Arnett, J. J. (1992). Reckless behavior in adolescence: A developmental perspective. Developmental Review, 12, 339–373.CrossRefGoogle Scholar
Arnett, J. J. (1999). Adolescent storm and stress, reconsidered. American Psychologist, 54, 317–326.CrossRefGoogle ScholarPubMed
Banfield, J. D., & Raftery, A. E. (1993). Model-based Gaussian and non-Gaussian clustering. Biometrics, 49, 803–821.CrossRefGoogle Scholar
Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338–363.CrossRefGoogle ScholarPubMed
Bauer, D. J., & Curran, P. J. (2004). The integration of continuous and discrete latent variable models: Potential problems and promising opportunities. Psychological Methods, 9, 3–29.CrossRefGoogle ScholarPubMed
Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.CrossRefGoogle ScholarPubMed
Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605–634.CrossRefGoogle ScholarPubMed
Bongers, I. L., Koot, H. M., van der Ende, J., & Verhulst, F. C. (2004). Developmental trajectories of externalizing behaviors in childhood and adolescence. Child Development, 75, 1523–1537.CrossRefGoogle ScholarPubMed
Brame, B., Nagin, D. S., & Tremblay, R. E. (2001). Developmental trajectories of physical aggression from school entry to late adolescence. Journal of Child Psychology and Psychiatry, 42, 503–512.CrossRefGoogle ScholarPubMed
Capaldi, D. M., & Stoolmiller, M., Clark, S., & Owen, L. D. (2002). Heterosexual risk behaviors in at-risk young men from early adolescence to young adulthood: Prevalence, prediction, and association with STD contraction. Developmental Psychology, 38, 394–406.CrossRefGoogle ScholarPubMed
Celeux, G., & Govaert, G. (1992). Comparison of the mixture and the classification maximum likelihood in cluster analysis. Journal of Statistical Computation and Simulation, 47, 127–146.CrossRefGoogle Scholar
Chassin, L., Flora, D. B., & King, K. M. (2004). Trajectories of alcohol and drug use and dependence from adolescence to adulthood: The effects of familial alcoholism and personality. Journal of Abnormal Psychology, 113, 483–498.CrossRefGoogle ScholarPubMed
Chassin, L., Pitts, S. C., & Prost, J. (2002). Heavy drinking trajectories from adolescence to emerging adulthood in a high-risk sample: Predictors and substance abuse outcomes. Journal of Consulting and Clinical Psychology, 70, 67–78.CrossRefGoogle Scholar
Chassin, L., Presson, C. C., Pitt, S. C., & Sherman, S. J. (2000). The natural history of cigarette smoking from adolescence to adulthood in a Midwestern community sample: Multiple trajectories and their psychosocial correlates. Health Psychology, 19, 223–231.CrossRefGoogle Scholar
Chassin, L., Presson, C. C., Sherman, S. J., & Edwards, D. A. (1991). Four pathways to young-adult smoking status: Adolescent social-psychological antecedents in a midwestern community sample. Health Psychology, 10, 409–418.CrossRefGoogle Scholar
Cicchetti, D., & Rogosch, F. A. (2002). A developmental psychopathology perspective on adolescence. Journal of Consulting and Clinical Psychology, 70, 6–20.CrossRefGoogle ScholarPubMed
Cicchetti, D., & Toth, S. L. (1998). The development of depression in children. American Psychologist, 53, 221–241.CrossRefGoogle ScholarPubMed
Colder, C. R., Campbell, R. T., Ruel, E., Richardson, J. L., & Flay, B. R. (2002). A finite mixture model of growth trajectories of adolescent alcohol use: Predictors and consequences. Journal of Consulting and Clinical Psychology, 70, 67–78.CrossRefGoogle ScholarPubMed
Colder, C. R., Mehta, P., Balanda, K., Campbell, R. T., Mayhew, K. P., & Stanton, W. R., et al. (2001). Identifying trajectories of adolescent smoking: An application of latent growth mixture modeling. Health Psychology, 20, 127–135.CrossRefGoogle ScholarPubMed
Crockett, L. J., Moilanen, K. L., Raffaelli, M., & Randall, B. A. (2006). Psychological profiles and adolescent adjustment: A person-centered approach. Development and Psychopathology, 18, 195–214.CrossRefGoogle ScholarPubMed
Dahl, R. E. (2004). Adolescent brain development: A period of vulnerabilities and opportunities. Annals New York Academy of Sciences, 1021, 1–22.CrossRefGoogle ScholarPubMed
Dolan, C. V., Schmittmann, V. D., Lubke, G. H., & Neale, M. C. (2005). Regime switching in the latent growth curve mixture model. Structural Equation Modeling, 12, 94–119.CrossRefGoogle Scholar
Dolan, C. V., & van der Mass, H. L. J. (1998). Fitting multivariate normal finite mixtures subject to structural equation modeling. Psychometrika, 63, 227–253.CrossRefGoogle Scholar
Dornbusch, S. M., Mont-Reynaud, R., Ritter, P. L., Chen, Z., Steinberg, L. (1991). Stressful events and their correlates among adolescents of diverse backgrounds. In Colten, M. E. & Gore, S. (Eds.), Adolescent stress: Causes and consequences (pp. 111–130). New York: Aldine de Gruyter.Google Scholar
Dumenci, L., & Windle, M. (2001). Cluster analysis as a method of recovering types of intraindividual growth trajectories: A Monte Carlo study. Multivariate Behavioral Research, 36, 501–522.CrossRefGoogle ScholarPubMed
Eaton, D. K., Kann, L., Kinchen, S., Ross, J., Hawkins, J., Harris, W. A., et al. (2006). Youth risk behavior surveillance—United States, 2005. Morbidity and Mortality Weekly Report, 55, 1–108.Google Scholar
Elliott, D. S., Huizinga, D., & Menard, S. (1989). Multiple problem youth. New York: Springer–Verlag.CrossRefGoogle Scholar
Everitt, B. S. (2005). Finite mixture distributions. In Everitt, B. S. & Howell, D. (Eds.), Encyclopedia of statistics in behavioral science. London: Wiley.CrossRefGoogle Scholar
Everitt, B. S., & Hand, D. J. (1981). Finite mixture distributions. London: Chapman & Hall CRC.CrossRefGoogle Scholar
Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster analysis (4th ed.). London: Arnold.Google Scholar
Flory, K., Lynam, D., Milich, R., Leukefeld, C., & Clayton, R. (2004). Early adolescent through young adulthood alcohol and marijuana use trajectories: Early predictors, young adult outcomes, and predictive utility. Development and Psychopathology, 16, 193–213.CrossRefGoogle ScholarPubMed
Fraley, C., & Raftery, A. E. (1998). How many clusters? Which clustering method? Answer via model-based cluster analysis. The Computer Journal, 41, 578–588.CrossRefGoogle Scholar
Fraley, C., & Raftery, A. E. (1999). MCLUST: Software for model-based cluster analysis. Journal of Classification, 16, 297–206.CrossRefGoogle Scholar
Fraley, C., & Raftery, A. E. (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97, 611–631.CrossRefGoogle Scholar
Fraley, C., & Raftery, A. E. (2002b). MCLUST: Software for model-based clustering, density estimation, and discriminant analysis (Technical Report No. 415). Seattle, WA: Department of Statistics, University of Washington.CrossRefGoogle Scholar
Fraley, C., & Raftery, A. E. (2003). Enhanced software for model-based clustering, discriminant analysis, and density estimation: MCLUST. Journal of Classification, 20, 263–286.CrossRefGoogle Scholar
Gorman-Smith, D., Tolan, P. H., Loeber, R., Henry, D. B. (1998). Relation of family problems to patterns of delinquent involvement among urban youth. Journal of Abnormal Child Psychology, 26, 319–333.CrossRefGoogle ScholarPubMed
Grunbaum, J. A., Kann, L., Kinchen, S., Ross, J., Hawkins, J., Lowry, R., et al. (2004). Youth risk behavior surveillance—United States, 2003. Morbidity and Mortality Weekly Report, 53, 1–95.Google ScholarPubMed
Guo, J., Chung, I. J., Hill, K. G., Hawkins, J. D., Catalano, R. F., & Abbott, R. D. (2002). Developmental relationships between adolescent substance use and risky sexual behavior in young adulthood. Journal of Adolescent Health, 31, 354–362.CrossRefGoogle ScholarPubMed
Hand, D. J., & Bolton, R. J. (2004). Pattern discovery and detection: A unified statistical methodology. Journal of Applied Statistics, 31, 885–924.CrossRefGoogle Scholar
Hardin, J., & Rocke, D. M. (2002). Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. Computational Statistics and Data Analysis, 44, 625–638.CrossRefGoogle Scholar
Haselager, G. J. T., Cillessen, A. H. N., Van Lieshout, C. F. M., Riksen-Walraven, J. M. A., & Hartup, W. W. (2002). Heterogeneity among peer-rejected boys across middle childhood: Developmental pathways of social behavior. Developmental Psychology, 38, 446–456.CrossRefGoogle ScholarPubMed
Hill, K. G., White, H. R., Chung, I.-J., Hawkins, D., & Catalano, R. F. (2000). Early adult outcomes of adolescent heavy drinking: Person- and variable-centered analyses of heavy drinking trajectories. Alcoholism: Clinical and Experimental Research, 24, 892–901.Google Scholar
Hipp, J. R., & Bauer, D. J. (2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11, 36–53.CrossRefGoogle ScholarPubMed
Hix-Small, H., Duncan, T. E., Duncan, S. C., & Okut, H. (2004). A multivariate associative finite growth mixture modeling approach examining adolescent alcohol and marijuana use. Journal of Psychopathology and Behavioral Assessment, 26, 255–270.CrossRefGoogle Scholar
Insightful Corporation (1988–2006). S-PLUS 7 for Windows. Seattle, WA: Author.Google Scholar
Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997). STEMM: A general finite mixture structural equation model. Journal of Classification, 14, 23–50.CrossRefGoogle Scholar
Jessor, R., van den Bos, J., Vanderryn, J., Costa, F. M., & Turbin, M. S. (1995). Protective factors in adolescent problem behavior: Moderator effects and developmental change. Developmental Psychology, 31, 923–933.CrossRefGoogle Scholar
Jester, J. M., Nigg, J. T., Adams, K., Fitzgerald, H. E., Puttler, L. I., Wong, M. M., et al. (2005). Inattention/hyperactivity and aggression from early childhood to adolescence: Heterogeneity of trajectories and differential influence of family environment characteristics. Development and Psychopathology, 17, 99–125.CrossRefGoogle ScholarPubMed
Johnston, L. D., O'Malley, P. M., & Bachman, J. G. (2006). Monitoring the Future national survey results on drug use, 1975–2005. Bethesda, MD: National Institute on Drug Abuse.Google Scholar
Kandel, D. (1975). Stages in adolescent involvement in drug use. Science, 190, 912–914.CrossRefGoogle ScholarPubMed
Kandel, D. B. (2002). Examining the gateway hypothesis: Stages and pathways of drug involvement (pp. 3–15). In Kandel, D. B. (Ed.), Stages and pathways of drug involvement. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kaplow, J. B., Curran, P. J., Dodge, K. A., & the Conduct Problems Prevention Research Group (2002). Child, parent, and peer predictors of early-onset substance use: A multisite longitudinal study. Journal of Abnormal Child Psychology, 30, 199–216.CrossRefGoogle ScholarPubMed
Kessler, R. C., McGonagle, K. A., Zhao, S., Nelson, C. B., Hughes, M., Eshleman, S., et al. (1994). Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Archives of General Psychiatry, 51, 8–19.CrossRefGoogle ScholarPubMed
Kim, H. K., Capaldi, D. M., & Stoolmiller, M. (2003). Depressive symptoms across adolescence and young adulthood in men: Predictions from parental and contextual risk factors. Development and Psychopathology, 15, 469–495.CrossRefGoogle Scholar
Kim, J., & Cicchetti, D. (2006). Longitudinal trajectories of self-system processes and depressive symptoms among maltreated and nonmaltreated children. Child Development, 77, 624–639.CrossRefGoogle ScholarPubMed
Leisch, F. (2004). FlexMix: A general framework for finite mixture models and latent class regression in R. Journal of Statistical Software, 11, 1–18.CrossRefGoogle Scholar
Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of American Statistical Association, 83, 1198–1202.CrossRefGoogle Scholar
Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.Google Scholar
Loeber, R., & Southamer-Loeber, M. (1998). Development of juvenile aggression and violence. American Psychologist, 53, 242–259.CrossRefGoogle ScholarPubMed
Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21–39.CrossRefGoogle ScholarPubMed
Magnusson, D. (2000). The individual as the organizing principle in psychological inquiry: A holistic approach. In Bergman, L. R., Cairns, R. B., Nilsson, L.-G., & Nystedt, L. (Eds.), Developmental science and the holistic approach (pp. 33–48). Mahwah, NJ: Erlbaum.Google Scholar
Masten, A. S., Burt, K. B., Roisman, G. I., Obradovic, J., Long, J. D., & Tellegen, A. (2004). Resources and resilience in the transition to adulthood: Continuity and change. Development and Psychopathology, 16, 1071–1094.CrossRefGoogle ScholarPubMed
McLachlan, G. J., & Chang, S. U. (2004). Mixture modeling for cluster analysis. Statistical Methods in Medical Research, 13, 347–361.CrossRefGoogle ScholarPubMed
McLachlan, G. J., Do, K., & Ambroise, C. (2004). Analyzing microarray gene expression data. New York: Wiley.CrossRefGoogle Scholar
McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: Wiley.CrossRefGoogle Scholar
Meilă, M. (2003). Comparing clusterings by the variation of information. In Schölkopf, B. & Warmuth, M. K. (Eds.), Learning theory and kernel machines: Proceedings of the 16th Annual Conference on Computational Learning Theory (pp. 173–187). New York: Springer.CrossRefGoogle Scholar
Meilă, M. (2007). Comparing clusterings—An information based distance. Journal of Multivariate Analysis, 98, 873–895.CrossRefGoogle Scholar
Meredith, W. (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58, 525–543.CrossRefGoogle Scholar
Miller, P., & Plant, M. (2002). Heavy cannabis use among UK teenagers: An exploration. Drug and Alcohol Dependence, 65, 235–242.CrossRefGoogle ScholarPubMed
Miller-Tutzauer, C., Leonard, K. E., & Windle, M. (1991). Marriage and alcohol use: A longitudinal study of “maturing out.” Journal of Studies on Alcohol, 52, 434–440.CrossRefGoogle ScholarPubMed
Moffitt, T. E. (1993). Adolescence-limited and life-course persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674–701.CrossRefGoogle ScholarPubMed
Monti, P. M., Miranda, R. Jr., Nixon, K., Sher, K. J., Swartzwelder, S., Tapert, S. F., et al. (2005). Adolescence: Booze, brain, and behavior. Alcoholism: Clinical and Experimental Research, 29, 207–220.CrossRefGoogle Scholar
Muthén, B. O. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Methods, 8, 369–377.CrossRefGoogle ScholarPubMed
Muthén, B. O. (2006). Latent variable hybrids: Overview of old and new models. Presented at the University of Maryland Center for Integrated Latent Variable Research conference, “Mixture Models in Latent Variable Research,” May 18–19, 2006.Google Scholar
Muthén, L. K., & Muthén, B. O. (1998–2006). Mplus user's guide (4th ed.). Los Angeles: Author.Google Scholar
Muthén, B. O., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463–469.CrossRefGoogle ScholarPubMed
Nagin, D. S. (1999). Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods, 4, 139–157.CrossRefGoogle Scholar
Nagin, D. S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Nagin, D. S., Farrington, D. P., & Moffitt, T. E. (1995). Life-course trajectories of different types of offenders. Criminology, 33, 111–139.CrossRefGoogle Scholar
Nagin, D. S., & Tremblay, R. E. (1999). Trajectories of boys' physical aggression, and opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency. Child Development, 70, 1181–1196.CrossRefGoogle ScholarPubMed
Needham, R. M. (1965). Computer methods for classification and grouping. In Hymes, I. (Ed.), The use of computers in anthropology (Vol. 11, pp. 91–110). The Hague: Mouton.Google Scholar
Newcomb, M. D., & Bentler, P. M. (1988). Consequences of adolescent drug use: Impact on the lives of young adults. Newbury Park, CA: Sage.Google Scholar
Orlando, M., Tucker, J. S., Ellickson, P. L., & Klein, D. J. (2004). Developmental trajectories of cigarette smoking and their correlates from early adolescence to young adulthood. Journal of Consulting and Clinical Psychology, 72, 400–410.CrossRefGoogle ScholarPubMed
Posner, M. I., & Rothbart, M. K. (2000). Developing mechanisms of self-regulation. Development and Psychopathology, 12, 427–441.CrossRefGoogle Scholar
Potter, C. C., & Jenson, J. M. (2003). Cluster profiles of multiple problem youth. Criminal Justice and Behavior, 30, 230–250.CrossRefGoogle Scholar
Radloff, L. S. (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.CrossRefGoogle Scholar
Raffaelli, M., & Crockett, L. J. (2003). Sexual risk taking in adolescence: The role of self-regulation and attraction to risk. Developmental Psychology, 39, 1036–1046.CrossRefGoogle ScholarPubMed
Reyna, V. F., & Farley, F. (2006). Risk and rationality in adolescent decision making. Psychological Science in the Public Interest, 7, 1–44.CrossRefGoogle ScholarPubMed
Rindskopf, D. (2003). Mixture or homogeneous? Comment on Bauer and Curran (2003). Psychological Methods, 8, 364–368.CrossRefGoogle ScholarPubMed
Sadeh, A., Gruber, R., & Raviv, A. (2002). Sleep, neurobehavioral functioning, and behavior problems in school-age children. Child Development, 73, 405–417.CrossRefGoogle ScholarPubMed
Sampson, R. J., Laub, J. H., & Wimer, C. (2006). Does marriage reduce crime? A counterfactual approach to within-individual causal effects. Criminology, 44, 465–508.CrossRefGoogle Scholar
Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall.CrossRefGoogle Scholar
Schulenberg, J., O'Malley, P. M., Bachman, J. G., Wadsworth, K. N., & Johnston, L. D. (1996). Getting drunk and growing up: Trajectories of frequent heavy drinking during the transition to young adulthood. Journal of Studies on Alcohol, 57, 289–304.CrossRefGoogle ScholarPubMed
Schulenberg, J., Sameroff, A. J., & Cicchetti, D. (2004). The transition to adulthood as a critical juncture in the course of psychopathology and mental health. Development and Psychopathology, 16, 799–806.CrossRefGoogle ScholarPubMed
Shiffman, S., Kassel, J., Paty, J., Gnys, P., & Zettler-Segal, M. (1994). Smoking typology profiles of chippers and regular smokers. Journal of Substance Abuse, 6, 21–35.CrossRefGoogle ScholarPubMed
SPSS Inc. (1989–2004). SPSS 13.0 for Windows. Chicago: Author.Google Scholar
Steinley, D. (2003). Local optima in K-means clustering: What you don't know may hurt you. Psychological Methods, 8, 294–304.CrossRefGoogle ScholarPubMed
Stoolmiller, M., Kim, H. K., & Capaldi, D. M. (2005). The course of depressive symptoms in men from early adolescence to young adulthood: Identifying latent trajectories and early predictors. Journal of Abnormal Psychology, 114, 331–345.CrossRefGoogle ScholarPubMed
Tarter, R. E., & Vanyukov, M. (1994). Alcoholism: A developmental disorder. Journal of Consulting and Clinical Psychology, 62, 1096–1107.CrossRefGoogle ScholarPubMed
Tonidandel, S., & Overall, J. E. (2004). Determining the number of clusters by sampling with replacement. Psychological Methods, 9, 238–249.CrossRefGoogle ScholarPubMed
Tubman, J. G., Vicary, J. R., von Eye, A., & Lerner, J. V. (1990). Longitudinal substance use and adult adjustment. Journal of Substance Abuse, 2, 317–334.CrossRefGoogle ScholarPubMed
Tubman, J. G., & Windle, M. (1995). Continuity of difficult temperament in adolescence: Relations with depression, life events, family support, and substance use across a one-year period. Journal of Youth and Adolescence, 24, 133–153.CrossRefGoogle Scholar
Tucker, J. S., Ellickson, P. L., & Klein, D. J. (2002). Smoking cessation during the transition from adolescence to young adulthood. Nicotine and Tobacco Research, 4, 321–332.Google ScholarPubMed
Tucker, J. S., Orlando, M., & Ellickson, P. L. (2003). Patterns and correlates of binge drinking trajectories from early adolescence to young adulthood. Health Psychology, 22, 79–87.CrossRefGoogle ScholarPubMed
van der Kloot, W. A., Spaans, A. M. J., & Heiser, W. J. (2005). Instability of hierarchical cluster analysis due to input order of the data: The PermuCLUSTER solution. Psychological Methods, 10, 468–476.CrossRefGoogle ScholarPubMed
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed.). New York: Springer.CrossRefGoogle Scholar
von Eye, A. (2002). Configural frequency analysis: Methods, models, and applications. Mahwah, NJ: Erlbaum.Google Scholar
von Eye, A., & Bergman, L. R. (2003). Research strategies in developmental psychopathology: Dimensional identity and the person-oriented approach. Development and Psychopathology, 15, 553–580.CrossRefGoogle ScholarPubMed
von Eye, A. & Mun, E. Y. (2005). Analyzing rater agreement. Mahwah, NJ: Erlbaum.Google Scholar
von Eye, A., Mun, E. Y., & Indurkhya, A. (2004). Typifying developmental trajectories—A decision making perspective. Psychology Science, 1, 65–98.Google Scholar
White, H. R., Pandina, R. J., & Chen, P-H. (2002). Developmental trajectories of cigarette use from early adolescence into young adulthood. Drug and Alcohol Dependence, 65, 167–178.CrossRefGoogle ScholarPubMed
Wiesner, M., & Capaldi, D. (2003). Relations of childhood and adolescent factors to offending trajectories of young men. Journal of Research in Crime and Delinquency, 40, 231–262.CrossRefGoogle Scholar
Wiesner, M., & Kim, H. K., & Capaldi, D. (2005). Developmental trajectories of offending: Validation and prediction to young adult alcohol use, drug use, and depressive symptoms. Development and Psychopathology, 17, 1–20.CrossRefGoogle ScholarPubMed
Wills, T. A., & Cleary, S. D. (1997). The validity of self-reports of smoking: Analyses by race/ethnicity in a school sample of urban adolescents. American Journal of Public Health, 87, 56–61.CrossRefGoogle Scholar
Wilsnack, S. C., Plaud, J. J., Wilsnack, R. W., & Klassen, A. D. (1997). Sexuality, gender, and alcohol use. In Wilsnack, R. W. & Wilsnack, S. C. (Eds.), Gender and alcohol (pp. 250–287). New Brunswick, NJ: Rutgers University, Center of Alcohol Studies.Google Scholar
Windle, M. (1989). Predicting temperament-mental health relationships: A covariance structure latent variable analysis. Journal of Research in Personality, 23, 118–144.CrossRefGoogle Scholar
Windle, M. (1991). The difficult temperament in adolescence: Associations with substance use, family support, and problem behaviors. Journal of Clinical Psychology, 47, 310–315.3.0.CO;2-U>CrossRefGoogle ScholarPubMed
Windle, M. (1992). The Revised Dimensions of Temperament Survey (DOTS-R): Simultaneous group confirmatory factor analysis for adolescent gender groups. Psychological Assessment, 4, 228–234.CrossRefGoogle Scholar
Windle, M. (1996). An alcohol involvement typology for adolescents: Convergent validity and longitudinal stability. Journal of Studies on Alcohol, 57, 627–637.CrossRefGoogle ScholarPubMed
Windle, M., & Lerner, R. M. (1986). Reassessing the dimensions of temperamental individuality across the lifespan: The Revised Dimensions of Temperament Survey (DOTS-R). Journal of Adolescent Research, 1, 213–230.CrossRefGoogle Scholar
Windle, M., & Mun, E. Y. (2006). The course of depressive symptoms from ages 14 through 30: Growth mixture modeling and young adulthood outcomes. Unpublished manuscript.Google Scholar
Windle, M., Mun, E. Y., & Windle, R. C. (2005). Adolescent-to-young adulthood heavy drinking trajectories and their prospective predictors. Journal of Studies on Alcohol, 66, 313–322.CrossRefGoogle ScholarPubMed
Windle, M., & Wiesner, M. (2004). Trajectories of marijuana use from adolescence to young adulthood: Predictors and outcomes. Development and Psychopathology, 16, 1007–1027.CrossRefGoogle ScholarPubMed
Winters, K. C., Stinchfield, R. D., Henly, G. A., & Schwartz, R. H. (1991). Validity of adolescent self-reports of alcohol and other drug involvement. The International Journal of Addictions, 25, 1379–1395.CrossRefGoogle Scholar
Wong, M. M., Brower, K. J., Fitzgerald, H. E., & Zucker, R. A. (2004). Sleep problems in early childhood and early onset of alcohol and other drug use in adolescence. Alcoholism: Clinical and Experimental Research, 28, 578–587.CrossRefGoogle ScholarPubMed
World Health Organisation. (1997). Composite International Diagnostic Interview, Version 2.0. Geneva: Author.Google Scholar
Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E., & Ruzzo, W. L. (2001). Model-based clustering and data transformations for gene expression data. Bioinformatics, 17, 977–987.CrossRefGoogle ScholarPubMed
Zucker, R. A., Ellis, D. A., Bingham, C. R., & Fitzgerald, H. E. (1996). The development of alcoholic subtypes. Alcohol Health & Research World, 20, 46–54.Google ScholarPubMed
Zucker, R. A., Fitzgerald, H. E., & Moses, H. D. (1995). Emergence of alcohol problems and the severe alcoholisms: A developmental perspective on etiological theory and life course trajectory. In Cicchetti, D. & Cohen, D. (Eds.), Manual of developmental psychopathology (Vol., 2, pp. 677–711). New York: Wiley.Google Scholar