Skip to main content

Advertisement

SpringerLink
Log in

Latent Class Analysis of Individual-Level Characteristics Predictive of Intervention Outcomes in Urban Male Adolescents

  • Published:
Research on Child and Adolescent Psychopathology Aims and scope Submit manuscript

Abstract

Preventive intervention research dictates that new techniques are needed to elucidate what types of interventions work best for whom to prevent behavioral problems. The current investigation applies a latent class modeling structure to identify the constellation of characteristics—or profile—in urban male adolescents (n = 125, aged 15) that interrelatedly predict responses to a brief administration of an evidence-based program, Positive Adolescent Choices Training (PACT). Individual-level characteristics were selected as predictors on the basis of their association with risk behaviors and their implication in intervention outcomes (e.g., mental health, stress exposure, temperament, cognitive function, stress reactivity and emotion perception). Outcome measures included virtual reality vignettes and questionnaire-style role play scenarios to gauge orientations around aggressive conflict resolution, communication, emotional control, beliefs supporting aggression and hostility. A three-class model was found to best fit the data: “NORMative” (NORM), with relatively low symptomatology; “Mental Health” problems (MH-I) with elevated internalizing symptoms; and “Mental Health-E + Cognitive Deficit” (MH-E + Cog) with elevated mental health symptoms paired with cognitive decrements. The NORM class had positive PACT effects for communication, conflict resolution, and aggressive beliefs. Moderation was evidenced by lack of positive PACT effects for the MH-I and MH-E + Cog groups. Also, PACT classes with MH issues showed marginally significant worsening of aggressive beliefs compared to control students in the same class. Results suggest that a latent class model may identify “signatures” or profiles of traits, experiences and other influences that collectively—and more realistically—predict variable intervention outcomes with implications for more effectively targeting interventions than singular factors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data Availability

Yes

Code Availability

Yes

Notes

  1. Two additional mean stress measures were constructed consisting of stressors related to violence and stressors not associated with violence. Because results were comparable, only those for the overall stress measure are presented.

References

  • Allara, E., Beccaria, F., Molinar, R., Marinaro, L., Ermacora, A., Coppo, A., & Faggiano, F. (2019). A school-based program to promote well-being in preadolescents: Results from a cluster quasi-experimental controlled study. The Journal of Primary Prevention, 40(2), 151–170.

    Article  Google Scholar 

  • Beauchaine, T. P., Neuhaus, E., Brenner, S. L., & Gatzke-Kopp, L. (2008). Ten good reasons to consider biological processes in prevention and intervention research. Development and Psychopathology, 20, 745–774.

    Article  Google Scholar 

  • Blair, C. (2003). Behavioral inhibition and behavioral activation in young children: Relations with self-regulation and adaptation to preschool in children attending Head Start. Developmental Psychobiology, 3, 301–311.

    Article  Google Scholar 

  • Botvin, G. J. (2000). Preventing drug abuse in schools: Social and competence enhancement approaches targeting individual-level etiologic factors. Addictive Behaviors, 25, 887–897.

    Article  Google Scholar 

  • Botvin, G. J., Baker, E., Dusenbury, L., Botvin, E. M., & Diaz, T. (1995). Long-term follow-up results of a randomized drug abuse prevention trial in a white middle-class population. Journal of the American Medical Association, 273, 1106–1112.

    Article  Google Scholar 

  • Carver, C. S., Meyer, B., & Antoni, M. H. (2000). Responsiveness to threats and incentives, expectancy of recurrence, and distress and disengagement: Moderator effects in women with early stage breast cancer. Journal of Consulting and Clinical Psychology, 68(6), 965–975.

    Article  Google Scholar 

  • Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. Journal of Personality and Social Psychology, 67, 319–333.

    Article  Google Scholar 

  • Coddington, R. D. (1972). The significance of life events as etiologic factors in the diseases of children. I. A survey of professional workers. Journal of Psychosomatic Research, 16(1), 7–18.

  • Conrod, P. J., O’Leary-Barrett, M., Newton, N., Topper, L., Castellanos-Ryan, N., Mackie, C., & Girard, A. (2013). Effectiveness of a selective, personality-targeted prevention program for adolescent alcohol use and misuse: a cluster randomized controlled trial. JAMA Psychiatry, 70(3), 334–342. https://doi.org/10.1001/jamapsychiatry.2013.651

    Article  PubMed  Google Scholar 

  • Curtis, K., Osadchuk, A., & Katz, J. (2011). An eight-week yoga intervention is associated with improvements in pain, psychological functioning and mindfulness, and changes in cortisol levels in women with fibromyalgia. Journal of Pain Research, 4, 189–201.

    Article  Google Scholar 

  • Davidson, R. (1994). Asymetric brain function, affective style and psychopathology: The role of early experience and plasticity. Development and Psychopathology, 58, 741–758.

    Article  Google Scholar 

  • De Haan, M., Luciana, M., Malone, S., Matheny, L., & Richards, L. (1994). Development, plasticity, and risk: Commentary on Huttenlocher, Pollitt and Gorman and Gottesman and Goldman. In C. A. Nelson (Ed.), Threats to Optimal Development: Integrating Biological, Psychological and Social Risk Factors (pp. 161–178). Lawrence Erlbaum.

    Google Scholar 

  • Dishion, T. J., Mun, C. J., Ha, T., & Tein, J. Y. (2019). Observed Family and Friendship Dynamics in Adolescence: a Latent Profile Approach to Identifying “Mesosystem” Adaptation for Intervention Tailoring. Prevention Science, 20(1), 41–55. https://doi.org/10.1007/s11121-018-0927-0.PMID:29968007;PMCID:PMC6951801

    Article  PubMed  PubMed Central  Google Scholar 

  • Ekman, P., & Friesen, W. V. (1975). Pictures of facial affect. Consulting Psychologists Press.

    Google Scholar 

  • Fingeret, M. C., Warren, C. S., Cepeda-Benito, A., & Gleaves, D. H. (2006). Eating disorder prevention research: A meta-analysis. Journal of Treatment and Prevention, 14, 191–213.

    Google Scholar 

  • Fishbein, D. H., Hyde, C., Eldreth, D., Paschall, M. J., Tarter, R., Das, A., Hubal, R., Ialongo, N., Hubbard, S., & Yung, B. (2006). Neurocognitive skills moderate urban male adolescents’ responses to preventive intervention materials. Drug and Alcohol Dependence, 82, 47–60.

    Article  Google Scholar 

  • Foxcroft, D. R., Ireland, D., Lister-Sharp, D. J., Lowe, G., & Breen, R. (2003). Longer-term primary prevention for alcohol misuse in young people: A systematic review. Addiction, 98, 397–411.

    Article  Google Scholar 

  • Gardner, F., Connell, A., Trentacosta, C. J., Shaw, D. S., Dishion, T. J., & Wilson, M. N. (2009). Moderators of outcome in a brief family-centered intervention for preventing early problem behavior. Journal of Consulting and Clinical Psychology, 77, 543–553.

    Article  Google Scholar 

  • Greenberg, M., Domitrovich C.E., Graczyk, P., & Zins, J. (1999). The study of implementation in school-based preventive interventions: Theory, research, and practice. Report to the Center for Mental Health Services (CMHS), Substance Abuse Mental Health Services Administration, U.S. Department of Health and Human Services.

  • Greenfield, S. F., Potter, J. S., Lincoln, M. F., Popuch, R. E., Kuper, L., & Gallop, R. J. (2008). High psychiatric symptom severity is a moderator of substance abuse treatment outcomes among women in single vs. mixed gender group treatment. American Journal of Drug and Alcohol Abuse, 34(5), 594–602.

  • Hitti, S. A., Sullivan, T. N., McDonald, S. E., & Farrell, A. D. (2018). Longitudinal relations between beliefs supporting aggression and externalizing outcomes: Indirect effects of anger dysregulation and callous‐unemotional traits. Aggressive Behavior, 45(1), 93–102.

    Article  Google Scholar 

  • Institute of Medicine. (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC.

  • Ivanov, I., Yehuda, R., Greenblatt, E., Davidow, J., Makotkine, I., Alfi, L., & Newcorn, J. H. (2001). The effect of trauma on stress reactivity in aggressive youth. Psychiatry, 189(3), 396–402.

    Google Scholar 

  • Jones, R. M., Somerville, L. H., Li, J., Ruberry, E. J., Libby, V., Glover, G., Voss, H. U., Ballon, D. J., & Casey, B. J. (2011). Behavioral and neural properties of social reinforcement learning. Journal Neuroscience, 31(37), 13039–13045.

    Article  Google Scholar 

  • Kellam, S. G., & Anthony, J. C. (1998). Targeting early antecedents to prevent tobacco smoking: Findings from an epidemiologically-based randomized field trial. American Journal of Public Health, 88, 1490–1495.

    Article  Google Scholar 

  • Lammers, J., Goossens, F., Conrod, P., Engels, R., Wiers, R. W., & Kleinjan, M. (2017). Effectiveness of a selective alcohol prevention program targeting personality risk factors: Results of interaction analyses. Addictive Behaviors, 71, 82–88. https://doi.org/10.1016/j.addbeh.2017.02.030 Epub 2017 Feb 28 PMID: 28282524.

    Article  PubMed  Google Scholar 

  • Le Fauve, C. E., Litten, R. Z., Randall, C. L., Moak, D. H., Salloum, I. M., & Green, A. I. (2004). Pharmacological treatment of alcohol abuse/dependence with psychiatric comorbidity. Alcoholism: Clinical and Experimental Research, 28(2), 302–312.

  • Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.

    Article  Google Scholar 

  • Logan, G. D., & Burkell, J. (1986). Dependence and independence in responding to double stimulation: A comparison of stop, change, and dual-task paradigms. Journal of Experimental Psychology: Human Perception and Performance, 12, 549–563.

    Google Scholar 

  • Lynam, D. R., Milich, R., Zimmerman, R., Novak, S. P., Logan, T. K., Martin, C., et al. (1999). Project DARE: No effects at 10-year follow-up. Journal of Consulting and Clinical Psychology, 67, 590–593.

    Article  Google Scholar 

  • McCutcheon, A. C. (1987). Latent class analysis. Sage.

    Book  Google Scholar 

  • Myers, M. G., Brown, S. A., & Mott, M. A. (1995). Preadolescent conduct disorder behaviors predict relapse and progression of addiction for adolescent alcohol and drug abusers. Alcoholism: Clinical and Experimental Research, 19(6), 1528–1536.

  • Nylund, K. L., Asparouhov, T., & Muthen, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569.

    Article  Google Scholar 

  • Orozco, S., & Lukas, S. (2000). Gender differences in acculturation and aggression as predictors of drug use in minorities. Drug and Alcohol Dependence, 59, 165–172.

    Article  Google Scholar 

  • Paaver, M., Eensoo, D., Kaasik, K., Vaht, M., Mäestu, J., & Harro, J. (2012). Preventing risky driving: A novel and efficient brief intervention focusing on acknowledgement of personal risk factors. Accident Analysis and Prevention, June 11. [Epub ahead of print]

  • Papandonatos, G. D., Williams, D. M., Jennings, E. G., Napolitano, M. A., Bock, B. C., Dunsiger, S., & Marcus, B. H. (2011). Mediators of physical activity behavior change: Findings from a 12-month randomized controlled trial. Health Psychology, Dec. 12.

  • Rebok, G. W., Hawkins, R. E., Krener, P., Mayer, L. S., & Kellam, S. G. (1996). The effect of concentration problems on the malleability of aggressive and shy behaviors in an epidemiologically-based preventive trial. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 193–203.

    Article  Google Scholar 

  • Riggs, N. R., & Greenberg, M. T. (2009). Neurocognition as a moderator and mediator of adolescent substance misuse prevention. American Journal of Drug and Alcohol Abuse, 35, 209–213. PMID: 20180672.

    Article  Google Scholar 

  • Rubia, K. (2009). The neurobiology of meditation and its clinical effectiveness in psychiatric disorders. Biological Psychology, 82(1), 1–11.

    Article  Google Scholar 

  • Sinha, R. (2001). How does stress increase risk of drug abuse and relapse? Psychopharmacology (Berl), 158, 343–359.

    Article  Google Scholar 

  • Sinha, R., Robinson, J., & O’Malley, S. (1998). Stress response dampening: Effects of gender and family history of alcoholism and anxiety disorders. Psychopharmacology (Berl), 137(4), 311–320.

    Article  Google Scholar 

  • Slaby, R., & Guerra, N. (1988). Cognitive mediators of aggression in adolescent offenders: 1. Assessment. Developmental Psychology, 24, 580–588.

    Article  Google Scholar 

  • Slaby, R. G., & Wilson-Brewer, R. (1992). Vignettes developed for the evaluation of violence prevention curricula targeting middle school students. Education Development Center.

    Google Scholar 

  • Sonuga-Barke, E., & Taylor, E. (1992). Hyperactivity and delay aversion-I: The effect of delay on choice. Journal of Child Psychology and Psychiatry, 33, 387–398.

    Article  Google Scholar 

  • Staiger, P. K., Kambouropoulos, N., & Dawe, S. (2007). Should personality traits be considered when refining substance misuse treatment programs? Drug and Alcohol Review, 26(1), 17–23.

    Article  Google Scholar 

  • Stevenson, M., & Crnic, K. (2012). Intrusive fathering, children’s self-regulation and social skills: A mediation analysis. Journal of Intellectual Disability Research, March 28.

  • Tennant, R., Goens, C., Barlow, J., Day, C., & Stewart-Brown, S. L. (2007). A systematic review of reviews of interventions to promote mental health and prevent mental health problems in children and young people. Journal of Public Mental Health, 6, 25–32.

    Article  Google Scholar 

  • Thornton, T. N., Craft, C. A., Dahlberg, L. L., Lynch, B. S., & BaerBest, K. (2000). Practices for Youth Violence Prevention: A Sourcebook for Community Action. Centers for Disease Control and Prevention.

    Google Scholar 

  • Wechsler, D. (1991). Wechsler Intelligence Scale for Children—III. The Psychological Corporation.

    Google Scholar 

  • Werch, C. E., & Owen, D. M. (2002). Iatrogenic effects of alcohol and drug prevention programs. Journal of Studies on Alcohol, 63(5), 581–590.

    Article  Google Scholar 

  • 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(4), 585–602.

    Article  Google Scholar 

  • Yung, B., & Hammond, R. (1998). Breaking the cycle: A culturally sensitive prevention program for African American children and adolescents. In J. Lutzker (Ed.), Handbook of child abuse research and treatment (pp. 319–340). Plenum Publishing.

    Chapter  Google Scholar 

Download references

Funding

Funding for this study was provided by the National Institute on Drug Abuse (5 R01 DA14813).

Author information

Authors and Affiliations

  1. University of North Carolina, Chapel Hill, North Carolina, USA

    Diana H. Fishbein

  2. National Prevention Science Coalition to Improve Lives, Chapel Hill, North Carolina, USA

    Diana H. Fishbein

  3. The Pennsylvania State University, State College, USA

    Diana H. Fishbein

  4. RTI International, Research Triangle Park, Durham, North Carolina, USA

    Jason Williams

Authors
  1. Diana H. Fishbein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jason Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Dr. Diana Fishbein initiated, conceived of and conducted this research and authored a large part of the manuscript and Dr. Jason Williams conducted the analyses and wrote the statistical and results sections.

Corresponding author

Correspondence to Diana H. Fishbein.

Ethics declarations

Conflict of Interest

There is no potential, perceived or real conflict of interest in any part of this study or manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fishbein, D.H., Williams, J. Latent Class Analysis of Individual-Level Characteristics Predictive of Intervention Outcomes in Urban Male Adolescents. Res Child Adolesc Psychopathol 49, 1139–1149 (2021). https://doi.org/10.1007/s10802-021-00808-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10802-021-00808-x

Keywords

Search

Navigation