Abstract
Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.
References
Aiken, L. S., West, S. G., Woodward, C. K., Reno, R. R., & Reynolds, K. D. (1994). Increasing screening mammography in asymptomatic women: Evaluation of a second-generation theory-based program. Health Psychology, 13, 526–538.
Arbuckle, J. L. (1999). AMOS 4 [Computer software]. Chicago: Small-Waters Corp.
Aroian, L. A. (1947). The probability function of the product of two normally distributed variables. Annals of Mathematical Statistics, 18, 265–271.
Assad, K. K., Donnellan, M. B., & Conger, R. D. (2007). Optimism: An enduring resource for romantic relationships. Journal of Personality & Social Psychology, 93, 285–297.
Azen, R. (2003, August). Multiple mediator models: A comparison of testing approaches. Poster presented at the 111th Annual Convention of the American Psychological Association, Toronto, Ontario.
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.
Bishop, Y. M., Fienberg, S. E., & Holland, P. W. (1975). Discrete multivariate analysis: Theory and practice. Cambridge, MA: MIT Press.
Bollen, K. A. (1987). Total, direct, and indirect effects in structural equation models. Sociological Methodology, 17, 37–69.
Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
Bollen, K. A., & Stine, R. (1990). Direct and indirect effects: Classical and bootstrap estimates of variability. Sociological Methodology, 20, 115–140.
Briggs, N. (2006). Estimation of the standard error and confidence interval of the indirect effect in multiple mediator models. Dissertation Abstracts International, 37, 4755B.
Brown, R. L. (1997). Assessing specific mediational effects in complex theoretical models. Structural Equation Modeling, 4, 142–156.
Carson, J. W., Carson, K. M., Gil, K. M., & Baucom, D. H. (2007). Self-expansion as a mediator of relationship improvements in a mindfulness intervention. Journal of Marital & Family Therapy, 33, 517–528.
Chao, G. T., O’Leary-Kelly, A. M., Wolf, S., Klein, H. J., & Gardner, P. D. (1994). Organizational socialization: Its content and consequences. Journal of Applied Psychology, 79, 730–743.
Cheung, M. W.-L. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling, 14, 227–246.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558–577.
Collins, L. M., Graham, J. W., & Flaherty, B. P. (1998). An alternative framework for defining mediation. Multivariate Behavioral Research, 33, 295–312.
Constantine, M. G. (2007). Racial microaggressions against African American clients in cross-racial counseling relationships. Journal of Counseling Psychology, 54, 1–16.
Craig, C. C. (1936). On the frequency function of xy. The Annals of Mathematical Statistics, 7, 1–15.
de Zavala, A. G., & Van Bergh, A. (2007). Need for cognitive closure and conservative political beliefs: Differential mediation by personal worldviews. Political Psychology, 28, 587–608.
Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171–185.
Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Boca Raton, FL: Chapman & Hall.
Fox, J. (1985). Effects analysis in structural equation models II: Calculation of specific indirect effects. Sociological Methods & Research, 14, 81–95.
Gudmundsdottir, B., Beck, J. G., Coffey, S. F., Miller, L., & Palyo, S. A. (2004). Quality of life and post trauma symptomatology in motor vehicle accident survivors: The mediating effects of depression and anxiety. Depression & Anxiety, 20, 187–189.
Holbert, R. L., Shah, D. V., & Kwak, N. (2003). Political implications of prime-time drama and sitcom use: Genres of representation and opinions concerning women’s rights. Journal of Communication, 53, 45–60.
Holbert, R. L., & Stephenson, M. T. (2003). The importance of indirect effects in media effects research: Testing for mediation in structural equation modeling. Journal of Broadcasting & Electronic Media, 47, 556–572.
Judd, C. M., & Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5, 602–619.
Kalyanaraman, S., & Sundar, S. S. (2006). The psychological appeal of personalized content in web portals: Does customization affect attitudes and behavior? Journal of Communication, 56, 110–132.
Kenny, D. A., Kashy, D. A., & Bolger, N. (1998). Data analysis in social psychology. In D. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (4th ed., Vol. 1, pp. 233–265). New York: McGraw-Hill.
Klein, H. J., Fan, J., & Preacher, K. J. (2006). The effects of early socialization experiences on content mastery and outcomes: A mediational approach. Journal of Vocational Behavior, 68, 96–115.
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, Inc.
MacKinnon, D. P. (1994). Analysis of mediating variables in prevention and intervention research. In A. Cázares & L. Beatty (Eds.), Scientific methods for prevention intervention research (NIDA Monograph No. 139, pp. 127–153). Rockville, MD: National Institute on Drug Abuse.
MacKinnon, D. P. (2000). Contrasts in multiple mediator models. In J. Rose, L. Chassin, C. C. Presson, & S. J. Sherman (Eds.), Multivariate applications in substance use research: New methods for new questions (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., 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.
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., 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.
Morgeson, F. P., Delaney-Klinger, K., & Hemingway, M. A. (2005). The importance of job autonomy, cognitive ability, and job-related skill for predicting role breadth and job performance. Journal of Applied Psychology, 90, 399–406.
Muthén, L. K., & Muthén, B. O. (2007). Mplus user’s guide (5th ed.). Los Angeles: Muthén & Muthén.
Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2003). Mx: Statistical modeling (6th ed.). Richmond, VA: Department of Psychiatry, Virginia Commonwealth University.
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717–731.
Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communication research. In A. F. Hayes, M. D. Slater, & L. B. Snyder (Eds.), The Sage sourcebook of advanced data analysis methods for communication research (pp. 13–54). Thousand Oaks, CA: Sage.
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Assessing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185–227.
Raver, J. L., & Gelfand, M. J. (2005). Beyond the individual victim: Linking sexual harassment, team processes, and team performance. Academy of Management Journal, 48, 387–400.
Reynolds, K. D., Bishop, D. B., Chou, C.-P., Xie, B., Nebeling, L., & Perry, C. L. (2004). Contrasting mediating variables in two 5-a-day nutrition intervention programs. Preventive Medicine, 39, 882–893.
Rutter, A., & Hine, D. W. (2005). Sex differences in workplace aggression: An investigation of moderation and mediation effects. Aggressive Behavior, 31, 254–270.
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 equations models. In S. Leinhart (Ed.), Sociological methodology 1982 (pp. 290–312). San Francisco: Jossey-Bass.
Sobel, M. E. (1986). Some new results on indirect effects and their standard errors in covariance structure models. In N. Tuma (Ed.), Sociological Methodology 1986 (pp. 159–186). Washington, DC: American Sociological Association.
Springer, M. D. (1979). The algebra of random variables. New York: Wiley.
Taylor, A. B., Mackinnon, D. P., & Tein, J.-Y. (2008). Tests of the three-path mediated effect. Organizational Research Methods, 11, 241–269.
West, S. G., & Aiken, L. S. (1997). Toward understanding individual effects in multicomponent prevention programs: Design and analysis strategies. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 167–209). Washington, DC: American Psychological Association.
Williams, J. (2004). Resampling and distribution of the product methods for testing indirect effects in complex models. Unpublished doctoral dissertation, Arizona State University, Tempe, AZ.
Williams, J., & MacKinnon, D. P. (2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling, 15, 23–51.
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This work was funded in part by National Institute on Drug Abuse Grant DA16883, awarded to K.J.P. while at the University of North Carolina at Chapel Hill.
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Preacher, K.J., Hayes, A.F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40, 879–891 (2008). https://doi.org/10.3758/BRM.40.3.879
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DOI: https://doi.org/10.3758/BRM.40.3.879
Keywords
- Indirect Effect
- Structural Equation Modeling
- Residual Covariance
- Total Indirect Effect
- Multiple Mediator Model