Journal of Abnormal Child Psychology

, Volume 41, Issue 2, pp 223–237

Evidence for a Multi-Dimensional Latent Structural Model of Externalizing Disorders

  • Katie Witkiewitz
  • Kevin King
  • Robert J. McMahon
  • Johnny Wu
  • Jeremy Luk
  • Karen L. Bierman
  • John D. Coie
  • Kenneth A. Dodge
  • Mark T. Greenberg
  • John E. Lochman
  • Ellen E. Pinderhughes
  • the Conduct Problems Prevention Research Group
Article

Abstract

Strong associations between conduct disorder (CD), antisocial personality disorder (ASPD) and substance use disorders (SUD) seem to reflect a general vulnerability to externalizing behaviors. Recent studies have characterized this vulnerability on a continuous scale, rather than as distinct categories, suggesting that the revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) take into account the underlying continuum of externalizing behaviors. However, most of this research has not included measures of disorders that appear in childhood [e.g., attention-deficit/hyperactivity disorder (ADHD) or oppositional defiant disorder (ODD)], nor has it considered the full range of possibilities for the latent structure of externalizing behaviors, particularly factor mixture models, which allow for a latent factor to have both continuous and categorical dimensions. Finally, the majority of prior studies have not tested multidimensional models. Using lifetime diagnoses of externalizing disorders from participants in the Fast Track Project (n = 715), we analyzed a series of latent variable models ranging from fully continuous factor models to fully categorical mixture models. Continuous models provided the best fit to the observed data and also suggested that a two-factor model of externalizing behavior, defined as (1) ODD+ADHD+CD and (2) SUD with adult antisocial behavior sharing common variance with both factors, was necessary to explain the covariation in externalizing disorders. The two-factor model of externalizing behavior was then replicated using a nationally representative sample drawn from the National Comorbidity Survey-Replication data (n = 5,692). These results have important implications for the conceptualization of externalizing disorders in DSM-5.

Keywords

Externalizing Classification Mixture modeling Substance abuse Substance dependence 

References

  1. Achenbach, T. M. (1966). The classification of children’s psychiatric symptoms: A factor-analytic study. Psychological Monographs, 80, 1–37.PubMedGoogle Scholar
  2. Achenbach, T. M. (1991). Manual for the child behavior checklist/4-18 and 1991 profile. Burlington, VT: University of Vermont, Department of Psychiatry.Google Scholar
  3. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.Google Scholar
  4. American Psychiatric Association. (2010). Cross-cutting dimensional assessment in DSM-5. In DSM-5 development. Retrieved from http://www.dsm5.org/ProposedRevisions/Pages/CrossCuttingDimensionalAssessmentinDSM-5.aspx.
  5. Barkley, R., Fischer, M., Smallish, L., & Fletcher, K. (2002). The persistence of attention deficit/hyperactivity disorder into young adulthood as a function of reporting source and definition of disorder. Journal of Abnormal Psychology, 111, 279–289.PubMedCrossRefGoogle Scholar
  6. Beauchaine, T. P., Hinshaw, S. P., & Pang, K. L. (2010). Comorbidity of attention-deficit/hyperactivity disorder and early-onset conduct disorder: Biological, environmental, and developmental mechanisms. Clinical Psychology: Science and Practice, 17, 327–336.CrossRefGoogle Scholar
  7. Bentler, P. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.PubMedCrossRefGoogle Scholar
  8. Bezdjian, S., Krueger, R. F., Derringer, J., Malone, S., McGue, M., & Iacono, W. G. (2011). The structure of DSM-IV ADHD, ODD, and CD criteria in adolescent boys: A hierarchical approach. Psychiatry Research, 188, 411–421.PubMedCrossRefGoogle Scholar
  9. Brown, T. A., & Barlow, D. H. (2005). Categorical versus dimensional classification of mental disorders in DSM-V and beyond. Journal of Abnormal Psychology, 114, 551–556.PubMedCrossRefGoogle Scholar
  10. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Beverly Hills, CA: Sage.Google Scholar
  11. Burt, S. A., McGue, M., Krueger, R. F., & Iacono, W. (2005). How are parent–child conflict and childhood externalizing symptoms related over time? results from a genetically informative cross-lagged study. Development and Psychopathology, 17, 145–165.PubMedCrossRefGoogle Scholar
  12. Burke, J., & Loeber, R. (2010). Oppositional defiant disorder and the explanation of the comorbidity between behavioral disorders and depression. Clinical Psychology: Science and Practice, 17, 319–326. doi:10.1111/j.1468-2850.2010.01223.x.CrossRefGoogle Scholar
  13. Chassin, L., Pitts, S. C., & Prost, J. (2002). Binge 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.PubMedCrossRefGoogle Scholar
  14. Conduct Problems Prevention Research Group. (1992). A developmental and clinical model for the prevention of conduct disorders: The FAST Track program. Development and Psychopathology, 4, 509–527.CrossRefGoogle Scholar
  15. Conduct Problems Prevention Research Group. (1999). Initial impact of the Fast Track prevention trial for conduct problems: I. The high-risk sample. Journal of Consulting and Clinical Psychology, 67, 631–647.CrossRefGoogle Scholar
  16. Conduct Problems Prevention Research Group. (2000). Merging universal and indicated prevention programs: The Fast Track model. Addictive Behaviors, 25, 913–927.CrossRefGoogle Scholar
  17. Farmer, R. F., Seeley, J. R., Kosty, D. B., & Lewinsohn, P. M. (2009). Refinements in the hierarchical structure of externalizing psychiatric disorders: Patterns of lifetime liability from mid-adolescence through early adulthood. Journal of Abnormal Psychology, 118, 699–710.PubMedCrossRefGoogle Scholar
  18. First, M. B. (2010). Defining mental disorder in DSM-V. Psychological Medicine, 40, 1779–1782.PubMedCrossRefGoogle Scholar
  19. Fischer, M., Barkley, R. A., Smallish, L., & Fletcher, K. (2002). Young adult follow-up of hyperactive children: Self-reported psychiatric disorders, comorbidity, and the role of childhood conduct problems and teen CD. Journal of Abnormal Child Psychology, 30, 463–475.PubMedCrossRefGoogle Scholar
  20. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  21. Jablensky, A. (2009). A meta-commentary on the proposal for a meta-structure for DSM-V and ICD-11. Psychological Medicine, 39, 2099–2103.PubMedCrossRefGoogle Scholar
  22. Jackson, K. M., Sher, K. J., & Schulenberg, J. E. (2008). Conjoint developmental trajectories of young adult substance use. Alcoholism, Clinical and Experimental Research, 32, 723–737.PubMedCrossRefGoogle Scholar
  23. Kamphuis, J. H., & Noordhof, A. (2009). On categorical diagnoses in DSM-V: Cutting dimensions at useful points? Psychological Assessment, 21, 294–301.PubMedCrossRefGoogle Scholar
  24. Kaplow, J. B., Curran, P. J., Angold, A., & Costello, E. J. (2001). The prospective relation between dimensions of anxiety and the initiation of adolescent alcohol use. Journal of Clinical Child Psychology, 30, 316–326.PubMedCrossRefGoogle Scholar
  25. Kendler, K. S., Aggen, S. H., Knudsen, G. P., Roysmb, E., Neale, M. C., & Reichborn-Kjennerud, T. (2011). The structure of genetic and environmental risk factors for syndromal and subsyndromal common DSM-IV axis I and all axis II disorders. The American Journal of Psychiatry, 168, 29–39.PubMedCrossRefGoogle Scholar
  26. Kessler, R. C., & Ustan, T. B. (2004). The World Mental Health (WMH) survey initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research, 13, 93–121.PubMedCrossRefGoogle Scholar
  27. Kessler, R. C., Berglund, P., Chiu, W. T., Demler, O., Heeringa, S., Hiripi, E., et al. (2004). The US National Comorbidity Survey Replication (NCS-R): Design and field procedures. International Journal of Methods in Psychiatric Research, 13, 69–92.PubMedCrossRefGoogle Scholar
  28. King, K. M., Luk, J. W., Wu, J., Witkiewitz, K., Racz, S. J., McMahon, R. J., the Conduct Problems Prevention Research Group. (2012). The co-occurrence of externalizing behaviors during childhood: Factor structure and invariance over time. Manuscript in preparation.Google Scholar
  29. Krueger, R. F., & Bezdjian, S. (2009). Enhancing research and treatment of mental disorders with dimensional concepts: Toward DSM-V and ICD-11. World Psychiatry, 8, 3–6.PubMedGoogle Scholar
  30. Krueger, R. F., Hicks, B. M., Patrick, C. J., Carlson, S. R., Iacono, W. G., & McGue, M. (2002). Etiologic connections among substance dependence, antisocial behavior, and personality: Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411–424.PubMedCrossRefGoogle Scholar
  31. Krueger, R. F., Markon, K. E., Patrick, C. J., Benning, S. D., & Kramer, M. D. (2007). Linking antisocial behavior, substance use, and personality: An integrative quantitative model of the adult externalizing spectrum. Journal of Abnormal Psychology, 116, 645–666.PubMedCrossRefGoogle Scholar
  32. Krueger, R. F., Markon, K. E., Patrick, C. J., & Iacono, W. G. (2005). Externalizing psychopathology in adulthood: A dimensional-spectrum conceptualization and its implications for DSM-V. Journal of Abnormal Psychology, 114, 537–550.PubMedCrossRefGoogle Scholar
  33. Krueger, R. F., & South, S. C. (2009). Externalizing disorders: Cluster 5 of the proposed meta-structure for DSM-V and ICD-11. Psychological Medicine, 39, 2061–2070.PubMedCrossRefGoogle Scholar
  34. Lahey, B. B., Rathouz, P. J., Applegate, B., Van Hulle, C. A., Garriock, H. A., Urbano, R. C., et al. (2008). Testing structural models of DSM-IV symptoms of common forms of child and adolescent psychopathology. Journal of Abnormal Child Psychology, 36, 187–206.PubMedCrossRefGoogle Scholar
  35. Lahey, B. B., Van Hulle, C. A., Singh, A. L., Waldman, I. D., & Rathouz, P. J. (2011). Higher-order genetic and environmental structure of prevalent forms of child and adolescent psychopathology. Archives of General Psychiatry, 68, 181–189.PubMedCrossRefGoogle Scholar
  36. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778.CrossRefGoogle Scholar
  37. Loeber, R., & Burke, J. D. (2011). Developmental pathways in juvenile externalizing and internalizing problems. Journal of Research on Adolescence, 21, 34–46.PubMedCrossRefGoogle Scholar
  38. Lubke, G. H., Hudziak, J. J., Derks, E. M., van Bijsterveldt, T. C., & Boomsma, D. I. (2009). Maternal ratings of attention problems in ADHD: Evidence for the existence of a continuum. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 1085–1093.PubMedCrossRefGoogle Scholar
  39. Lubke, G. H., & Muthén, B. O. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling, 14, 26–47.Google Scholar
  40. Lubke, G. H., & Neale, M. C. (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood? Multivariate Behavioral Research, 41, 499–532.CrossRefGoogle Scholar
  41. Lubke, G. H., & Neale, M. C. (2008). Distinguishing between latent classes and continuous factors with categorical outcomes: Class invariance of parameters of factor mixture models. Multivariate Behavioral Research, 43, 592–620.PubMedCrossRefGoogle Scholar
  42. Lubke, G. H., & Spies, J. (2008). Choosing a ‘correct’ factor mixture model: Power, limitations, and graphical data exploration. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 343–362). Charlotte, NC: Information Age Publishing.Google Scholar
  43. MacLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York, NY: Wiley.CrossRefGoogle Scholar
  44. Markon, K. E., Chmielewski, M., & Miller, C. J. (2011). The reliability and validity of discrete and continuous measures of psychopathology: A quantitative review. Psychological Bulletin, 137, 856–879.PubMedCrossRefGoogle Scholar
  45. Markon, K. E., & Krueger, R. F. (2005). Categorical and continuous models of liability to externalizing disorders: A direct comparison in NESARC. Archives of General Psychiatry, 62, 1352–1359.PubMedCrossRefGoogle Scholar
  46. Marti, C. N., Stice, E., & Springer, D. W. (2010). Substance use and abuse trajectories across adolescence: A latent trajectory analysis of a community-recruited sample of girls. Journal of Adolescence, 33, 449–461.PubMedCrossRefGoogle Scholar
  47. Masyn, K., Henderson, C. E., & Greenbaum, P. E. (2010). Exploring the latent structures of psychological constructs in social development using the dimensional-categorical spectrum. Social Development, 19, 470–493.CrossRefGoogle Scholar
  48. Muthén, B., & Muthén, L. (2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Authors.Google Scholar
  49. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation. Structural Equation Modeling, 14, 535–569.CrossRefGoogle Scholar
  50. Rowe, R., Costello, E. J., Angold, A., Copeland, W. E., & Maughan, B. (2010). Developmental pathways in oppositional defiant disorder and conduct disorder. Journal of Abnormal Psychology, 119, 726–738.PubMedCrossRefGoogle Scholar
  51. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.PubMedCrossRefGoogle Scholar
  52. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
  53. Shaffer, D., & Fisher, P. (1997). NIMH-diagnostic interview schedule for children: Child informant. New York: New York State Psychiatric Institute.Google Scholar
  54. Shaffer, D., Fisher, P., Lucas, C., Dulcan, M., & Schwab-Stone, M. (2000). NIMH diagnostic interview schedule for children version IV (NIMH DISC-IV): Description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 28–38.PubMedCrossRefGoogle Scholar
  55. Shaffer, D., Fisher, P., Lucas, C., & Comer, J. (2003). Scoring manual: Diagnostic interview schedule for children (DISC-IV). New York, NY: Columbia University.Google Scholar
  56. Tackett, J. L. (2010). Toward an externalizing spectrum in DSM-V: Incorporating developmental concerns. Child Development Perspectives, 4, 161–167.CrossRefGoogle Scholar
  57. Tackett, J. L., Krueger, R. F., Sawyer, M. G., & Graetz, B. W. (2003). Subfactors of DSM-IV conduct disorder: Evidence and connections with syndromes from the child behavior checklist. Journal of Abnormal Child Psychology, 31, 647–654.PubMedCrossRefGoogle Scholar
  58. Todd, R. D., Rasmussen, E. R., Neuman, R. J., Reich, W., Hudziak, J. J., Bucholz, K. K., et al. (2001). Familiarity and heritability of subtypes of attention deficit hyperactivity disorder in a population sample of adolescent female twins. The American Journal of Psychiatry, 158, 1891–1898.PubMedCrossRefGoogle Scholar
  59. Tomarken, A. J., & Waller, N. G. (2003). Potential problems with “well fitting” models. Journal of Abnormal Psychology, 112, 578–598.PubMedCrossRefGoogle Scholar
  60. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.CrossRefGoogle Scholar
  61. Tuvblad, C., Zheng, M., Raine, A., & Baker, L. A. (2009). A common genetic factor explains the covariation among ADHD ODD and CD symptoms in 9–10 year old boys and girls. Journal of Abnormal Child Psychology, 37, 153–167.PubMedCrossRefGoogle Scholar
  62. Verona, E., Javdani, S., & Sprague, J. (2011). Comparing factor structures of adolescent psychopathology. Psychological Assessment, 23, 545–551. doi:10.1037/a0022055.PubMedCrossRefGoogle Scholar
  63. Walton, K. E., Ormel, J., & Krueger, R. F. (2011). The dimensional nature of externalizing behaviors in adolescence: Evidence from a direct comparison of categorical, dimensional, and hybrid models. Journal of Abnormal Child Psychology, 39, 553–561.PubMedCrossRefGoogle Scholar
  64. Werthamer-Larsson, L., Kellam, S., & Wheeler, L. (1991). Effect of first-grade classroom environment on shy behavior, aggressive behavior, and concentration problems. American Journal of Community Psychology, 19, 585–602.PubMedCrossRefGoogle Scholar
  65. Widiger, T. A., & Samuel, D. B. (2005). Diagnostic categories or dimensions: A question for DSM-V. Journal of Abnormal Psychology, 114, 494–504.PubMedCrossRefGoogle Scholar
  66. Winters, K. C., Stinchfield, R. D., Latimer, W. W., & Stone, A. (2008). Internalizing and externalizing behaviors and their association with the treatment of adolescents with substance abuse disorder. Journal of Substance Abuse Treatment, 35, 269–278.PubMedCrossRefGoogle Scholar
  67. World Health Organization. (1992). International statistical classification of disease and related health problems, tenth revision (ICD-10). Geneva: Author.Google Scholar
  68. Wu, J., Witkiewitz, K., McMahon, R. J., Dodge, K. A., & the Conduct Problems Prevention Research Group. (2010). A parallel process growth mixture model of conduct problems and substance use with risky sexual behavior. Drug and Alcohol Dependence, 111, 207–214.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Katie Witkiewitz
    • 1
  • Kevin King
    • 2
  • Robert J. McMahon
    • 3
  • Johnny Wu
    • 2
  • Jeremy Luk
    • 2
  • Karen L. Bierman
    • 4
  • John D. Coie
    • 5
  • Kenneth A. Dodge
    • 6
  • Mark T. Greenberg
    • 7
  • John E. Lochman
    • 8
  • Ellen E. Pinderhughes
    • 9
  • the Conduct Problems Prevention Research Group
  1. 1.Department of PsychologyWashington State UniversityVancouverUSA
  2. 2.Department of PsychologyUniversity of WashingtonSeattleUSA
  3. 3.Department of PsychologySimon Fraser University and the Child & Family Research InstituteBurnabyCanada
  4. 4.Department of PsychologyPennsylvania State UniversityUniversity ParkUSA
  5. 5.Department of PsychologyDuke UniversityDurhamUSA
  6. 6.Sanford School of Public PolicyDuke UniversityDurhamUSA
  7. 7.Pennsylvania State UniversityUniversity ParkUSA
  8. 8.Department of PsychologyUniversity of AlabamaTuscaloosaUSA
  9. 9.Eliot Pearson Department of Child DevelopmentTufts UniversityMedfordUSA

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