Abstract
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/.
Article PDF
Similar content being viewed by others
References
American Psychological Association (2001).Publication manual of the American Psychological Association (5th Ed.). Washington, DC: Author.
Arbuckle, J. L., &Wothke, W. (1999).AMOS 4.0 user’s guide. Chicago: SPSS.
Aroian, L. A. (1944). The probability function of the product of two normally distributed variables.Annals of Mathematical Statistics,18, 265–271.
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.
Bentler, P. M. (1997).EQS for Windows (Version 5.6) [Computer software]. Encino, CA: Multivariate Software.
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.
Bollen, K. A., &Stine, R. (1990). Direct and indirect effects: Classical and bootstrap estimates of variability.Sociological Methodology,20, 115–140.
Brown, R. L. (1997). Assessing specific mediational effects in complex theoretical models.Structural Equation Modeling,4, 142–156.
Collins, L. M., Graham, J. W., &Flaherty, B. P. (1998). An alternative framework for defining mediation.Multivariate Behavioral Research,33,295–312.
Efron, B., &Tibshirani, R. J. (1993).An introduction to the bootstrap. Boca Raton, FL: Chapman & Hall.
Goodman, L. A. (1960). On the exact variance of products.Journal of the American Statistical Association,55,708–713.
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.
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.
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.
James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation.Journal of Applied Psychology,69, 307–321.
Jöreskog, K. G., &Sörbom, D. (1996).LISREL 8 user’s reference guide. Uppsala, Sweden: Scientific Software International.
Judd, C. M., &Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations.Evaluation Review,5,602–619.
Kline, R. B. (1998).Principles and practice of structural equation modeling. New York: Guilford.
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.
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.
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.
MacKinnon, D. P., &Dwyer, J. H. (1993). Estimating mediated effects in prevention studies.Evaluation Review,17,144–158.
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.
MacKinnon, D. P., Krull, J. L., &Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect.Prevention Science,1,173–181.
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.
MacKinnon, D. P., Warsi, G., &Dwyer, J. H. (1995). A simulation study of mediated effect measures.Multivariate Behavioral Research,30,41–62.
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.
Mood, A., Graybill, F. A., &Boes, D. C. (1974).Introduction to the theory of statistics. New York: McGraw-Hill.
Mooney, C. Z., &Duval, R. D. (1993).Bootstrapping: A nonparametric approach to statistical inference. Newbury Park, CA: Sage.
Rozeboom, W. W. (1956). Mediation variables in scientific theory.Psychological Review,63,249–264.
Shrout, P. E., &Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations.Psychological Methods,7,422–445.
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.
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.
Wilkinson, L., &APA Task Force on Statistical Inference (1999). Statistical methods in psychology journals: Guidelines and explanations.American Psychologist,54,594–604.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Preacher, K.J., Hayes, A.F. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers 36, 717–731 (2004). https://doi.org/10.3758/BF03206553
Received:
Accepted:
Issue date:
DOI: https://doi.org/10.3758/BF03206553