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A General Model for Testing Mediation and Moderation Effects

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Abstract

This paper describes methods for testing mediation and moderation effects in a dataset, both together and separately. Investigations of this kind are especially valuable in prevention research to obtain information on the process by which a program achieves its effects and whether the program is effective for subgroups of individuals. A general model that simultaneously estimates mediation and moderation effects is presented, and the utility of combining the effects into a single model is described. Possible effects of interest in the model are explained, as are statistical methods to assess these effects. The methods are further illustrated in a hypothetical prevention program example.

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References

  • Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.

    Google Scholar 

  • Aroian, L. A. (1947). The probability function of the product of two normally distributed variables. Annals of Mathematical Statistics, 18, 265–271. doi:10.1214/aoms/1177730442.

    Article  Google Scholar 

  • 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 

  • Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142–163.

    Article  PubMed  Google Scholar 

  • Beck, C.T. (1994). Achieving statistical power through research design sensitivity. Journal of Advanced Nursing, 20, 912–916.

    Article  PubMed  CAS  Google Scholar 

  • Borich, G. D., & Godbout, R. C. (1974). Extreme groups designs and the evaluation of statistical power. Educational and Psychological Measurement, 34, 663–675.

    Article  Google Scholar 

  • Champoux, J. E. & Peters, W. S. (1987). Form, effect size, and power in moderated regression analysis. Journal of Occupational Psychology, 60, 243–255.

    Google Scholar 

  • Chen, H. T. (1990). Theory driven evaluations. Newbury Park, CA: Sage.

    Google Scholar 

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. A. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally.

    Google Scholar 

  • Dearing, E., & Hamilton, L. C. (2006). Contemporary advances and classic advice for analyzing mediating and moderating variables. Monographs of the Society for Research in Child Development, (December), 71, 88–104.

    Google Scholar 

  • Donaldson, S. I. (2001). Mediator and moderator analysis in program development. In S. Sussman (Ed.), Handbook of program development for health and behavior research and practice (pp. 470–496). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12, 1–22. doi:10.1037/1082-989X.12.1.1.

    Article  PubMed  Google Scholar 

  • Evans, M. G. (1985). A Monte Carlo study of the effects of correlated method variance in moderated multiple regression analysis. Organizational Behavior and Human Decisions Processes, 36, 305–323.

    Article  Google Scholar 

  • Fairchild, A. J. (2008). A comparison of frameworks for the joint analysis of mediation and moderation effects. Unpublished doctoral dissertation. Arizona State University, Tempe.

  • Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51, 115–134. doi:10.1037/0022-0167.51.1.115.

    Article  Google Scholar 

  • Gogineni, A., Alsup, R., & Gillespie, D. (1995). Mediation and moderation in social work research. Social Work Research, 19, 57–63.

    PubMed  CAS  Google Scholar 

  • Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equations models. Sociological Methodology, 18, 449–484. doi:10.2307/271055.

    Article  Google Scholar 

  • Hoyle, R. H., & Robinson, J. C. (2003). Mediated and moderated effects in social psychological research: Measurement, design, and analysis issues. In C. Sansone, C. Morf, & A. T. Panter (Eds.), Handbook of methods in social psychology (pp. 213–233). Thousand Oaks, CA: Sage.

    Google Scholar 

  • James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation. The Journal of Applied Psychology, 69, 307–321. doi:10.1037/0021-9010.69.2.307.

    Article  Google Scholar 

  • Judd, C. M., & Kenny, D. A. (1981a). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5, 602–619. doi:10.1177/0193841X8100500502.

    Article  Google Scholar 

  • Judd, C. M., & Kenny, D. A. (1981b). Estimating the effects of social intervention. Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Kenny, D. A., Korchmaros, J. D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8, 115–128.

    Article  PubMed  Google Scholar 

  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Mahwah, NJ: Earlbaum.

    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., Fritz, M. S., Williams, J., & Lockwood, C. M. (2007). Distribution of the product confidence limits for the indirect effect: Program PRODCLIN. Behavior Research Methods, 39, 384–389.

    PubMed  Google Scholar 

  • MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding, and suppression effect. Prevention Science, 1, 173–181. doi:10.1023/A:1026595011371.

    Article  PubMed  CAS  Google Scholar 

  • MacKinnon, D. P., & Lockwood, C. M. (2001). Distribution of product tests for the mediated effect: Power and Type I error rates. Unpublished manuscript.

  • MacKinnon, D. P., Lockwood, C. M., & Hoffman, J. M. (1998, June). A new method to test for mediation. Paper presented at the annual meeting of the Society for Prevention Research, Park City, UT.

  • MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test the significance of mediation and other intervening variable effects. Psychological Methods, 7, 83–104. doi:10.1037/1082-989X.7.1.83.

    Article  PubMed  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 

  • McClelland, G. H. & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114, 376–390.

    Article  PubMed  CAS  Google Scholar 

  • Morgan-Lopez, A. A., & MacKinnon, D. P. (2006). Demonstration and evaluation of a method for assessing mediated moderation. Behavior Research Methods, 38, 77–87.

    PubMed  Google Scholar 

  • Muller, D., Judd, C. M., & Yzerbyt, V. Y. (2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89, 852–863. doi:10.1037/0022-3514.89.6.852.

    Article  PubMed  Google Scholar 

  • Preacher, K. J., Rucker, D. D., & Hayes, R. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185–227.

    Google Scholar 

  • Rose, B. M., Holmbeck, G. N., Coakley, R. M., & Franks, E. A. (2004). Mediator and moderator effects in developmental and behavioral pediatric research. Journal of Developmental and Behavioral Pediatrics, 25, 58–67. doi:10.1097/00004703-200402000-00013.

    Article  PubMed  Google Scholar 

  • Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. doi:10.2307/270723.

    Article  Google Scholar 

  • Sobel, M. E. (1986). Some new results on indirect effects and their standard errors in covariance structure models. Sociological Methodology, 16, 159–186. doi:10.2307/270922.

    Article  Google Scholar 

  • Tein, J., Sandler, I. N., MacKinnon, D. P., & Wolchik, S. A. (2004). How did it work? Who did it work for? Mediation in the context of a moderated prevention effect for children of divorce. Journal of Consulting and Clinical Psychology, 72, 617–624. doi:10.1037/0022-006X.72.4.617.

    Article  PubMed  Google Scholar 

  • Wegener, D. T., & Fabrigar, L. R. (2000). Analysis and design for nonexperimental data addressing causal and noncausal hypotheses. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 412–450). New York: Cambridge University Press.

    Google Scholar 

  • West, S. G., & Aiken, L. S. (1997). Toward understanding individual effects in multiple component prevention programs: Design and analysis strategies. In K. Bryant, M. Windle, & S. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 167–209). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  • Yzerbyt, V. Y., Muller, D., & Judd, C. M. (2004). Adjusting researchers’ approach to adjustment: On the use of covariates when testing interactions. Journal of Experimental Social Psychology, 40, 424–431. doi:10.1016/j.jesp.2003.10.001.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Psychology, University of South Carolina, Barnwell College, 1512 Pendleton St., Columbia, SC, 29208, USA

    Amanda J. Fairchild

  2. Research in Prevention Lab, Department of Psychology, Arizona State University, P.O. Box 871104, Tempe, AZ, 85287-1104, USA

    David P. MacKinnon

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  1. Amanda J. Fairchild
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  2. David P. MacKinnon
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Correspondence to Amanda J. Fairchild.

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Fairchild, A.J., MacKinnon, D.P. A General Model for Testing Mediation and Moderation Effects. Prev Sci 10, 87–99 (2009). https://doi.org/10.1007/s11121-008-0109-6

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