Journal of Abnormal Child Psychology

, Volume 36, Issue 5, pp 759–770

Empirically Derived Subtypes of Child Academic and Behavior Problems: Co-Occurrence and Distal Outcomes

  • Wendy M. Reinke
  • Keith C. Herman
  • Hanno Petras
  • Nicholas S. Ialongo
Article

Abstract

The aim of this study was to identify classes of children at entry into first grade with different patterns of academic and behavior problems. A latent class analysis was conducted with a longitudinal community sample of 678 predominantly low-income African American children. Results identified multiple subclasses of children, including a class with co-occurring academic and behavior problems. Gender differences were found in relation to the number of identified classes and the characteristics of academic and behavior problems for children. Several of the identified classes, particularly the co-occurring academic and behavior problems subclass for both genders, predicted negative long-term outcomes in sixth grade, including academic failure, receipt of special education services, affiliation with deviant peers, suspension from school, and elevated risk for conduct problems. The finding that subclasses of academic and behavior problems predict negative long-term outcomes validates the importance of the identified classes and the need to target interventions for children presenting with the associated class characteristics. Implications for early identification, prevention, and intervention for children at risk for academic failure and disruptive behavior problems are discussed.

Keywords

Academic underachievement Behavior problems Latent class analysis 

References

  1. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides, & R. E. Schumacker (Eds.) Advanced structural equation modeling: Issues and techniques. Mahwah, NJ: Erlbaum.Google Scholar
  2. August, G. J., & Garfinkel, B. D. (1990). Comorbidity of ADHD and reading disability among clinic-referred children. Journal of Abnormal Child Psychology, 18, 29–45.PubMedCrossRefGoogle Scholar
  3. August, G. J., Realmuto, G. M., Hektner, J. M., & Bloomquist, M. L. (2001). An integrated components preventive intervention for aggressive elementary school children: The Early Risers Program. Journal of Consulting and Clinical Psychology, 69, 614–626.PubMedCrossRefGoogle Scholar
  4. Arnold, D. H. (1997). Co-occurrence of externalizing behavior problems and emergent academic difficulties in young high-risk boys: A preliminary evaluation of patterns and mechanisms. Journal of Applied Developmental Psychology, 18, 317–330.CrossRefGoogle Scholar
  5. Berger, M., Yule, W., & Rutter, M. (1975). Attainment and adjustment in two geographical areas: II. The prevalence of specific reading retardation. British Journal of Psychiatry, 126, 510–519.PubMedCrossRefGoogle Scholar
  6. Bub, K., McCartney, K., & Willet, J. (2007). Behavior problem trajectories and first grade cognitive ability and achievement skills: A latent growth curve analysis. Journal of Educational Psychology, 99, 653–670.CrossRefGoogle Scholar
  7. Capaldi, D. M., & Patterson, G. R. (1989). Psychometric properties of fourteen latent constructs from the Oregon Youth Study. NY: Springer-Verlag.Google Scholar
  8. Carroll, J. M., Maughan, B., Goodman, R., & Meltzer, H. (2005). Literacy difficulties and psychiatric disorders: Evidence for comorbidity. Journal of Child Psychology and Psychiatry, 46, 524–532.PubMedCrossRefGoogle Scholar
  9. Comprehensive Test of Basic Skills. 4th ed. (1990). Monterey, CA: CTB/McGraw-Hill.Google Scholar
  10. Conduct Problems Prevention Research Group (1999). Initial impact of the fast track prevention trial for conduct problems: II. Classroom effects. Journal of Consulting and Clinical Psychology, 67, 648–657.CrossRefGoogle Scholar
  11. DuPaul, G. J., & Eckert, T. L. (1997). The effects of school-based interventions for attention deficit Hyperactivity disorder: A meta-analysis. School Psychology Review, 26, 5–27.Google Scholar
  12. DuPaul, G. J., & Stoner, G. (2003). ADHD in the schools: Assessment and intervention strategies (2 nd ed.). New York: Guilford Press.Google Scholar
  13. DuPaul, G. J., Power, T. J., Anastopoulos, A. D., Reid, R., McGoey, K. E., & Ikeda, M. J. (1997). Teacher ratings of attention deficit hyperactivity disorder symptoms: Factor structure and normative data. Psychological Assessment, 9, 436–444.CrossRefGoogle Scholar
  14. Farrington, D. P., & Loeber, R. (2000). Some benefits of dichotomization in psychiatric and criminological research. Criminal Behaviour and Mental Health, 10, 100–122.CrossRefGoogle Scholar
  15. Frick, P., Kampaus, R. W., Lahey, B. B., Loeber, R., Christ, M. G., Hart, E., et al. (1991). Academic underachievement and disruptive behavior disorders. Journal of Consulting and Clinical Psychology, 59, 289–294.PubMedCrossRefGoogle Scholar
  16. Good, R. H., Gruba, J., & Kaminski, R. A. (2001). Best practices in using Dynamic Indicators of Basic Early Literacy Skills (DIBELS) in an outcomes-driven model. In A. Thomas, & J. Grimes (Eds.) Best practices in school psychology IV (pp. 679–700). Washington, DC: National Association of School Psychologist.Google Scholar
  17. Guo, J., Wall, M., & Amemiya, Y. (2006). Latent class regression on latent factors. Biostatistics, 7, 145–163.PubMedCrossRefGoogle Scholar
  18. Heiervang, E., Stevenson, J., Lund, A., & Hugdahl, K. (2001). Behaviour problems in children with dyslexia. Nordic Journal of Psychiatry, 55, 251–256.PubMedCrossRefGoogle Scholar
  19. Hinshaw, S. P. (1992a). Externalizing behavior problems and academic underachievement in childhood and adolescence: Causal relationships and underlying mechanisms. Psychological Bulletin, 111, 127–155.PubMedCrossRefGoogle Scholar
  20. Hinshaw, S. P. (1992b). Academic underachievement, attention deficits, and aggression: Comorbidity and implications for intervention. Journal of Consulting and Clinical Psychology, 6, 893–903.CrossRefGoogle Scholar
  21. Ialongo, N. S., Werthamer, L., & Kellam, S. G. (1999). Proximal impact of two first-grade preventive interventions on the early risk behaviors for later substance abuse, depression, and antisocial behavior. American Journal of Community Psychology, 27, 599–641.PubMedCrossRefGoogle Scholar
  22. Kellam, S., & Rebok, G. (1992). Building developmental and etiological theory through epidemiological based preventive intervention trials. In J. McCord, & R. E. Tremblay (Eds.) Preventing antisocial behavior: Interventions from birth through adolescence (pp. 162–195). New York: Neale Watson Academic Publishers.Google Scholar
  23. Konstantareas, M., & Homatidis, S. (1989). Parental perception of learning-disabled children’s adjustment problems and related stress. Journal of Abnormal Child Psychology, 17, 177–186.PubMedCrossRefGoogle Scholar
  24. Little, R. J. (1995). Modeling the dropout mechanism in repeated-measures studies. Journal of the American Statistical Association, 90, 1112–1121.CrossRefGoogle Scholar
  25. Loeber, R., & Keenan, K. (1994). Interaction between conduct disorder and its comorbid conditions: Effects of age and gender. Clinical Psychology Review, 14, 497–523.CrossRefGoogle Scholar
  26. Maughan, B., Pickles, A., Hagell, A., Rutter, M., & Yule, W. (1996). Reading problems and antisocial behaviour: Developmental trends in comorbidity. Journal of Child Psychology and Psychiatry, 37, 405–518.PubMedCrossRefGoogle Scholar
  27. McCutcheon, A. (1987). Latent class analysis. Beverly Hills, CA: Sage.Google Scholar
  28. Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674–701.PubMedCrossRefGoogle Scholar
  29. Muthén, B., & Muthén, L. K. (2006). Mplus users guide. Los Angeles, CA: Muthén and Muthén.Google Scholar
  30. Muthén, B., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analysis: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882–891.CrossRefGoogle Scholar
  31. Muthén, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 6, 463–469.CrossRefGoogle Scholar
  32. Nylund, K., Muthén, B., Bellmore, A., Nishina, A., Graham, S., & Juvoven, J. (2005). The state of victimization during middle school: A latent transition mixture model approach. Paper presented at the Annual Convention of the Society for Prevention Research, Washington, DC.Google Scholar
  33. Nylund, K. L., Asparouhov, T., & Muthen, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569.Google Scholar
  34. Ostrander, R., Weinfurt, K. P., Yarnold, P. R., & August, G. J. (1998). Diagnosing attention deficit disorders with the Behavioral Assessment System for Children and the Child Behavior Checklist: Test and construct validity analysis using optimal discriminant classification trees. Journal of Consulting and Clinical Psychology, 66, 660–672.PubMedCrossRefGoogle Scholar
  35. Patterson, G. R., Capaldi, D., & Bank, L. (1991). An early starter model for predicting delinquency. In D. Pepler & R. K. Rubin (Eds.), The development and treatment of childhood aggression. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  36. Pelham, W. E., Fabiano, G. A., & Massetti, G. M. (2005). Evidence-based assessment of attention-deficit/hyperactivity disorder in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 34, 449–476.PubMedCrossRefGoogle Scholar
  37. Rapport, M. D., Denney, C., DuPaul, G. J., & Gardner, M. (1994). Attention deficit disorder and methylphenidate: Normalization rates, clinical effectiveness, and response prediction in 76 children. Journal of the American Academic of Child and Adolescent Psychiatry, 33, 882–893.CrossRefGoogle Scholar
  38. Reboussin, B. A., Lohman, K. K., & Wolfson, M. (2006). Modeling adolescent drug use patterns in cluster-unit trials with multiple sources of correlation using robust latent class regression. Annals of Epidemiology, 16, 850–859.PubMedCrossRefGoogle Scholar
  39. Reid, J., Patterson, G., & Snyder, J. (2002). Antisocial behavior in children and adolescents: A developmental analysis and model for intervention. Washington, D.C.: American Psychological Association.Google Scholar
  40. Rowland, A.S., Lesesne, C.A., & Abramowitz, A.G. (2002). The epidemiology of attention-deficit/hyperactivity disorder (ADHD): A public health view. Mental Retardation Developmental Research Review, 8, 162–170.CrossRefGoogle Scholar
  41. Rutter, M., & Yule, W. (1970). Reading retardation and antisocial behaviour: The nature of association. In M. Rutter, J. Tizard, & K. Whitmore (Eds.) Education, health, and behaviour (pp. 240–255). London: Longmans.Google Scholar
  42. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.PubMedCrossRefGoogle Scholar
  43. Schwartz, G. (1978). Estimating the dimensions of a model. The Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
  44. Svetaz, M. V., Ireland, M., & Blum, R. (2000). Adolescents with learning disabilities: Risk and protective factors associated with emotional well-being: Findings from the National Longitudinal Study of Adolescent Health. Journal of Adolescent Health, 27, 340–348.PubMedCrossRefGoogle Scholar
  45. U.S. Department of Education. (2002). No Child Left Behind: Executive summary. Retrieved from www.ed.gov/nclb/overview/intro/execsumm.html
  46. Vermunt, J. K., & Magdison, J. (2002). Latent class cluster analysis. In J. A. Hagenaars, & A. L. McCutcheon (Eds.) Applied latent class analysis (pp. 89–106). Cambridge, UK: Cambridge University Press.Google Scholar
  47. Walrath, C. M., Petras, H., Mandell, D. S., Stephens, R. L., Holden, E. W., & Leaf, P. J. (2004). Gender differences in patterns of risk factors among children receiving mental health services: Latent class analysis. Journal of Behavioral Health Services & Research, 31, 297–311.Google Scholar
  48. Werthamer-Larsson, L., Kellam, S. G., & Wheeler, L. (1991). Effect of first-grade classroom environment on child shy behavior, aggressive behavior, and concentration problems. American Journal of Community Psychology, 19, 585–602.PubMedCrossRefGoogle Scholar
  49. Wilcutt, E. G., & Pennington, B. F. (2000). Psychiatric comorbidity in children and adolescents with reading disability. Journal of Child Psychology and Psychiatry, 41, 1039–1048.CrossRefGoogle Scholar
  50. Wilcutt, E. G., Pennington, B. F., & DeFries, J. C. (2000). Twin study of the etiology of comorbidity between reading disability and attention-deficit/hyperactivity disorder. American Journal of Medical Genetics, 96, 293–301.CrossRefGoogle Scholar
  51. Williams, S., & McGee, R. (1994). Reading attainment and juvenile delinquency. Journal of Child Psychology and Psychiatry, 35, 441–459.PubMedCrossRefGoogle Scholar
  52. Yang, X., Shaftel, J., Glasnapp, D., & Poggio, J. (2005). Qualitative or quantitative differences? Latent class analysis of mathematical ability for special education students. Journal of Special Education, 38, 194–207.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Wendy M. Reinke
    • 1
  • Keith C. Herman
    • 1
  • Hanno Petras
    • 2
  • Nicholas S. Ialongo
    • 3
  1. 1.University of Missouri-ColumbiaColumbiaUSA
  2. 2.Department of Criminology and Criminal JusticeUniversity of MarylandCollege ParkUSA
  3. 3.Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreUSA

Personalised recommendations