Skip to main content

Advertisement

SpringerLink
Log in

Evaluating Mediation in Longitudinal Multivariate Data: Mediation Effects for the Aban Aya Youth Project Drug Prevention Program

  • Published:
Prevention Science Aims and scope Submit manuscript

Abstract

This study illustrates a method to evaluate mediational mechanisms in a longitudinal prevention trial, the Aban Aya Youth Project (AAYP). In previous studies, interventions of AAYP were found to be effective in reducing the growth of violence, substance use and unsafe sex among African American adolescents. In this article, we hypothesized that the effects of the interventions in reducing the growth of substance use behavior were achieved through their effects in changing intermediate processes such as behavioral intentions, attitudes toward the behavior, estimates of peers’ behaviors, best friends’ behaviors, and peer group pressure. In evaluating these mediational mechanisms, difficulties arise because the growth trajectories of the substance use outcome variable and some of the mediating variables were curvilinear. In addition, all of the multivariate mediational measures had planned missing data so that a score from the multiple items for a mediator could not be formed easily. In this article, we introduce a latent growth modeling (LGM) approach; namely, a two-domain LGM mediation model, in which the growth curves of the outcome and the mediator are simultaneously modeled and the mediation effects are evaluated. Results showed that the AAYP intervention effects on adolescent drug use were mediated by normative beliefs of prevalence estimates, friends’ drug use behavior, perceived friends’ encouragement to use, and attitudes toward the behavior.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. doi:10.1037/0022-3514.51.6.1173.

    Article  PubMed  CAS  Google Scholar 

  • Botvin, G.J., Dusenbury, L., Baker, E., James-Ortiz, S., Botvin, E.M., & Kerner, J. (1992). Smoking prevention among urban minority youth: Assessing effects on outcome and mediating variables. Health Psychology, 11, 290–299. doi:10.1037/0278-6133.11.5.290.

    Article  PubMed  CAS  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, 1006–1112. doi:10.1001/jama.273.14.1106.

    Article  Google Scholar 

  • 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). Newbury Park, CA: Sage.

    Google Scholar 

  • Cheong, J., MacKinnon, D.P., & Khoo, S.T. (2003). Investigation of mediational processes using parallel process latent growth curve modeling. Structural Equation Modeling, 10, 238–262. doi:10.1207/S15328007SEM1002_5.

    Article  Google Scholar 

  • Cole, D.A., & Maxwell, S.E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Abnormal Psychology, 112, 558–577. doi:10.1037/0021-843X.112.4.558.

    Article  Google Scholar 

  • Duncan, T.E., & Duncan, S.C. (1995). Modeling the processes of development via latent variable growth curve methodology. Structural Equation Modeling, 2, 187–213.

    Article  Google Scholar 

  • Flay, B.R. (2000). Approaches to substance use prevention utilizing school curriculum plus social environment change. Addictive Behaviors, 25, 861–885. doi:10.1016/S0306-4603(00)00130-1.

    Article  PubMed  CAS  Google Scholar 

  • Flay, B.R., & Petraitis, J. (1994). The theory of triadic influence: A new theory of health behavior with implications for preventive interventions. In G. S. Albrecht (Eds.), Advances in medical sociology, Vol IV: A reconsideration of models of health behavior change (pp. 19–44). Greenwich, CT: JAI.

    Google Scholar 

  • Flay, B.R., Graumlich, S., Segawa, E., Burns, J.L., Holliday, M.Y., & Aban Aya Investigators. (2004). Effects of two prevention programs on high-risk behaviors among African-American youth: A randomized trial. Archives of Pediatrics & Adolescent Medicine, 158, 377–384. doi:10.1001/archpedi.158.4.377.

    Article  Google Scholar 

  • Hancock, G.R., Kuo, W., & Lawrence, F.R. (2001). An illustration of second-order latent growth models. Structural Equation Modeling, 8, 470–489. doi:10.1207/S15328007SEM0803_7.

    Article  Google Scholar 

  • Hansen, W.B., & McNeal, R.B. (1996). The law of maximum expected potential effect: Constraints placed on program effectiveness by mediator relationships. Health Education Research, 11, 501–507. doi:10.1093/her/11.4.501.

    Article  Google Scholar 

  • Hansen, W.B., & McNeal, R.B. (1997). How D.A.R.E. works: An examination of program effects on mediating variables. Health Education & Behavior, 24, 165–176. doi:10.1177/109019819702400205.

    Article  CAS  Google Scholar 

  • Hawkins, J.D., Catalano, R.F., & Miller, J.Y. (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. doi:10.1037/0033-2909.112.1.64.

    Article  PubMed  CAS  Google Scholar 

  • Hedeker, D., & Gibbons, R.D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64–78. doi:10.1037/1082-989X.2.1.64.

    Article  Google Scholar 

  • Hedeker, D., & Gibbons, R.D. (2006). Longitudinal data analysis. New York: Wiley.

    Google Scholar 

  • Komro, K.A., Perry, C.L., Williams, C.L., Stigler, M.H., Farbakhsh, K., & Veblen-Mortenson, S. (2001). How did Project Northland reduce alcohol use among adolescents? Analysis of mediating variables. Health Education Research, 16, 59–70. doi:10.1093/her/16.1.59.

    Article  PubMed  CAS  Google Scholar 

  • Krull, J.L., & MacKinnon, D.P. (1999). Multilevel mediation modeling in group-based intervention studies. Evaluation Review, 23, 418–444.

    Article  PubMed  CAS  Google Scholar 

  • MacKinnon, D.P. (2008). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum Associates.

    Google Scholar 

  • MacKinnon, D.P., & Dwyer, J.H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144–158. doi:10.1177/0193841X9301700202.

    Article  Google Scholar 

  • MacKinnon, D.P., Johnson, C.A., Pentz, M.A., Dwyer, J.H., Hansen, W.B., Flay, B.R., et al. (1991). Mediating mechanisms in a school-based drug prevention program: First year effects of the Midwestern Prevention Project. Health Psychology, 10, 64–172. doi:10.1037/0278-6133.10.3.164.

    Article  Google Scholar 

  • MacKinnon, D.P., Warsi, G., & Dwyer, J.H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30, 41–62. doi:10.1207/s15327906mbr3001_3.

    Article  Google Scholar 

  • MacKinnon, D.P., Lockwood, C.M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99–128. doi:10.1207/s15327906mbr3901_4.

    Article  Google Scholar 

  • McArdle, J.J. (1986). Latent variable growth within behavior genetic models. Behavior Genetics, 16, 163–200. doi:10.1007/BF01065485.

    Article  PubMed  CAS  Google Scholar 

  • McArdle, J.J. (1988). Handbook of multivariate experimental psychology (2nd ed., pp. 561–614). New York: Plenum.

    Google Scholar 

  • McArdle, J.J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110–133. doi:10.2307/1130295.

    Article  PubMed  CAS  Google Scholar 

  • McNeal, R.B., Hansen, W.B., Harrington, N.G., & Giles, S.M. (2004). How All Stars works: An examination of program effects on mediating variables. Health Education Quarterly, 31, 5–178.

    Google Scholar 

  • Muthen, B.O. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557–585. doi:10.1007/BF02296397.

    Article  Google Scholar 

  • Muthen, B.O. (1991). Analysis of longitudinal data using latent variable models with varying parameters. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change (pp. 1–17). Washington, DC: American Psychological Association.

    Google Scholar 

  • Muthen, L. K., & Muthen, B. O. (1998–2003). Mplus user’s guide. Version 2. Los Angeles: Authors.

  • Preacher, K.J., Wichman, A.L., MacCallum, R.C., & Briggs, N.E. (2008). Latent growth curve modeling. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Rogosa, D. (1988). Myths about longitudinal research. In K. W. Schaie, R. T. Campbell, W. Meredith, & S. C. Rawlings (Eds.), Methodological issues in aging research (pp. 171–205). New York: Springer.

    Google Scholar 

  • Rogosa, D., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 90, 726–748. doi:10.1037/0033-2909.92.3.726.

    Article  Google Scholar 

  • Rubin, D.B. (1976). Inference and missing data. Biometrika, 63, 581–592. doi:10.1093/biomet/63.3.581.

    Article  Google Scholar 

  • Schafer, J.L. (1997). Analysis of incomplete multivariate data. London: Chapman and Hall.

    Google Scholar 

  • Sobel, M.E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhard (Eds.), Sociological methodology (pp. 290–312). Washington, DC: American Sociological Association.

    Google Scholar 

  • 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. doi:10.1037/0033-2909.116.2.363.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 W. Taylor St., Chicago, IL, 60612, USA

    Li C. Liu

  2. College of Health and Human Sciences, Oregon State University, 245 Waldo, Corvallis, OR, 97331, USA

    Brian R. Flay

Authors
  1. Li C. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian R. Flay
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Aban Aya Investigators

Corresponding author

Correspondence to Li C. Liu.

Additional information

Aban Aya investigators include:

Shaffdeen A. Amuwo, Ph.D., Clinical Associate Professor, Associate Dean for community, Government and Alumni Affairs, University of Illinois at Chicago;

Carl C. Bell, M.D., Professor, Psychiatry & Public Health; and CEO, Community Mental Health Council;

Michael L. Berbaum, Ph.D., Director, Methodology Research Core, Institute for Health Research and Policy, University of Illinois at Chicago;

Richard T. Campbell, Ph.D., Professor, Biostatistics; and Methodology Research Core, Institute for Health Research and Policy, University of Illinois at Chicago;

Julia Cowell, R.N., Ph.D., Professor, Nursing (now at Rush University);

Judith Cooksey, M.D., Assistant Professor, Public Health (now at University of Maryland);

Barbara L. Dancy, Ph.D., Associate Professor, Nursing, University of Illinois at Chicago;

Sally Graumlich, Ed.D., Senior Research Associate, Institute for Health Research and Policy, University of Illinois at Chicago;

Donald Hedeker, Ph.D., Professor, Biostatistics, Public Health; and Methodology Research Core, Institute for Health Research and Policy, University of Illinois at Chicago;

Robert J. Jagers, Ph.D., Associate Professor, African American Studies and Psychology (now at University of Michigan);

Susan R. Levy, Ph.D., Professor, Public Health, University of Illinois at Chicago;

Roberta L. Paikoff, Ph.D., Associate Professor, Psychiatry, University of Illinois at Chicago;

Indru Punwani, D.D.S., Professor, Pediatric Dentistry, University of Illinois at Chicago;

Eisuke Segawa, Research Specialist, Institute for Health Research and Policy, University of Illinois at Chicago;

Roger P. Weissberg, Ph.D., Professor, Psychology, University of Illinois at Chicago.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, L.C., Flay, B.R. & Aban Aya Investigators. Evaluating Mediation in Longitudinal Multivariate Data: Mediation Effects for the Aban Aya Youth Project Drug Prevention Program. Prev Sci 10, 197–207 (2009). https://doi.org/10.1007/s11121-009-0125-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11121-009-0125-1

Keywords

Search

Navigation