SPSS and SAS procedures for estimating indirect effects in simple mediation models



Researchers often conduct mediation analysis in order to indirectly assess the effect of a proposed cause on some outcome through a proposed mediator. The utility of mediation analysis stems from its ability to go beyond the merely descriptive to a more functional understanding of the relationships among variables. A necessary component of mediation is a statistically and practically significant indirect effect. Although mediation hypotheses are frequently explored in psychological research, formal significance tests of indirect effects are rarely conducted. After a brief overview of mediation, we argue the importance of directly testing the significance of indirect effects and provide SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals, as well as the traditional approach advocated by Baron and Kenny (1986). We hope that this discussion and the macros will enhance the frequency of formal mediation tests in the psychology literature. Electronic copies of these macros may be downloaded from the Psychonomic Society’s Web archive atwww.psychonomic.org/archive/.


Life Satisfaction Indirect Effect Mediation Analysis Cognitive Therapy Sobel Test 
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Supplementary material

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  1. American Psychological Association (2001).Publication manual of the American Psychological Association (5th Ed.). Washington, DC: Author.Google Scholar
  2. Arbuckle, J. L., &Wothke, W. (1999).AMOS 4.0 user’s guide. Chicago: SPSS.Google Scholar
  3. Aroian, L. A. (1944). The probability function of the product of two normally distributed variables.Annals of Mathematical Statistics,18, 265–271.CrossRefGoogle Scholar
  4. Baron, R. M., &Kenny, D. A. (1986). The moderator—mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.Journal of Personality & Social Psychology,51,1173–1182.CrossRefGoogle Scholar
  5. Bentler, P. M. (1997).EQS for Windows (Version 5.6) [Computer software]. Encino, CA: Multivariate Software.Google Scholar
  6. Bollen, K. A. (1987). Total, direct, and indirect effects in structural equation models. In C. C. Clogg (Ed.),Sociological methodology 1987 (pp. 37–69). Washington, DC: American Sociological Association.Google Scholar
  7. Bollen, K. A., &Stine, R. (1990). Direct and indirect effects: Classical and bootstrap estimates of variability.Sociological Methodology,20, 115–140.CrossRefGoogle Scholar
  8. Brown, R. L. (1997). Assessing specific mediational effects in complex theoretical models.Structural Equation Modeling,4, 142–156.CrossRefGoogle Scholar
  9. Collins, L. M., Graham, J. W., &Flaherty, B. P. (1998). An alternative framework for defining mediation.Multivariate Behavioral Research,33,295–312.CrossRefGoogle Scholar
  10. Efron, B., &Tibshirani, R. J. (1993).An introduction to the bootstrap. Boca Raton, FL: Chapman & Hall.Google Scholar
  11. Goodman, L. A. (1960). On the exact variance of products.Journal of the American Statistical Association,55,708–713.CrossRefGoogle Scholar
  12. Holmbeck, G. N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures.Journal of Consulting & Clinical Psychology,65,599–610.CrossRefGoogle Scholar
  13. Holmbeck, G. N. (2002). Post-hoc probing of significant moderational and mediational effects in studies of pediatric populations.Journal of Pediatric Psychology,27,87–96.PubMedCrossRefGoogle Scholar
  14. Hoyle, R. H., &Kenny, D. A. (1999). Sample size, reliability, and tests of statistical mediation. In R. Hoyle (Ed.),Statistical strategies for small sample research (pp. 195–222). Thousand Oaks, CA: Sage.Google Scholar
  15. James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation.Journal of Applied Psychology,69, 307–321.CrossRefGoogle Scholar
  16. Jöreskog, K. G., &Sörbom, D. (1996).LISREL 8 user’s reference guide. Uppsala, Sweden: Scientific Software International.Google Scholar
  17. Judd, C. M., &Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations.Evaluation Review,5,602–619.CrossRefGoogle Scholar
  18. Kline, R. B. (1998).Principles and practice of structural equation modeling. New York: Guilford.Google Scholar
  19. Lockwood, C. M., &MacKinnon, D. P. (1998). Bootstrapping the standard error of the mediated effect.Proceedings of the 23rd annual meeting of SAS Users Group International (pp. 997–1002). Cary, NC: SAS Institute.Google Scholar
  20. MacKinnon, D. P. (1994). Analysis of mediating variables in prevention and intervention research. In A. Cazares and L. A. Beatty,Scientific methods for prevention intervention research (NIDA Research Monograph 139. DHHS Pub. No. 94-3631, pp. 127–153). Washington, DC: U.S. Government Printing Office.Google Scholar
  21. MacKinnon, D. P. (2000). Contrasts in multiple mediator models. In J. S. Rose, L. Chassin, C. C. Presson, & S. J. Sherman (Eds.),Multivariate applications in substance use research (pp. 141–160). Mahwah, NJ: Erlbaum.Google Scholar
  22. MacKinnon, D. P., &Dwyer, J. H. (1993). Estimating mediated effects in prevention studies.Evaluation Review,17,144–158.CrossRefGoogle Scholar
  23. MacKinnon, D. P., Goldberg, L., Clarke, G. N., Elliot, D. L., Cheong, J., Lapin, A., Moe, E., &Krull, J. L. (2001). Mediating mechanisms in a program to reduce intentions to use anabolic steroids and improve exercise self-efficacy and dietary behavior.Prevention Science,2,15–28.PubMedCrossRefGoogle Scholar
  24. MacKinnon, D. P., Krull, J. L., &Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect.Prevention Science,1,173–181.PubMedCrossRefGoogle Scholar
  25. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., &Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects.Psychological Methods,7,83–104.PubMedCrossRefGoogle Scholar
  26. MacKinnon, D. P., Warsi, G., &Dwyer, J. H. (1995). A simulation study of mediated effect measures.Multivariate Behavioral Research,30,41–62.PubMedCrossRefGoogle Scholar
  27. Meeker, W. Q., Cornwell, L. W., &Aroian, L. A. (1981).Selected tables in mathematical statistics: Vol. VII. The product of two normally distributed random variables. Providence, RI: American Mathematical Society.Google Scholar
  28. Mood, A., Graybill, F. A., &Boes, D. C. (1974).Introduction to the theory of statistics. New York: McGraw-Hill.Google Scholar
  29. Mooney, C. Z., &Duval, R. D. (1993).Bootstrapping: A nonparametric approach to statistical inference. Newbury Park, CA: Sage.Google Scholar
  30. Rozeboom, W. W. (1956). Mediation variables in scientific theory.Psychological Review,63,249–264.PubMedCrossRefGoogle Scholar
  31. Shrout, P. E., &Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations.Psychological Methods,7,422–445.PubMedCrossRefGoogle Scholar
  32. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhart (Ed.),Sociological methodology 1982 (pp. 290–312). San Francisco: Jossey-Bass.Google Scholar
  33. Stone, C. A., &Sobel, M. E. (1990). The robustness of estimates of total indirect effects in covariance structure models estimated by maximum likelihood.Psychometrika,55, 337–352.CrossRefGoogle Scholar
  34. Wilkinson, L., &APA Task Force on Statistical Inference (1999). Statistical methods in psychology journals: Guidelines and explanations.American Psychologist,54,594–604.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2004

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of North CarolinaChapel Hill
  2. 2.Ohio State UniversityColumbusOhio

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