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Mediation Analysis in Experimental Research

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Handbook of Market Research

Abstract

This chapter introduces the conceptual and statistical basics of mediation analysis in the context of experimental research. Adopting the respective terminology, mediation analysis can be referred to as an array of quantitative methods developed to investigate the causal mechanism(s) through which an independent variable influences a dependent variable. The chapter takes a regression-based approach to mediation analysis and focuses on mediation models likely to be tested in experiments (i.e., the single mediator model, parallel and serial multiple mediator models, and conditional process models). Yet, the scope of mediation analysis beyond an experimental setting will also be touched upon. Furthermore, the chapter addresses the question how to strengthen causal inference in mediation analysis through design, the collection of additional evidence, and statistical methods. It closes with a discussion of common topics of relevance when implementing mediation analysis such as sample size and power, mean centering in conditional process analysis, coding of categorical independent variables, advantages and disadvantages of a regression-based approach to mediation analysis, and software options to perform mediation analysis.

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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(6), 1173–1182.

    Google Scholar 

  • Berger, J. (2014). Word of mouth and interpersonal communication: A review and directions for future research. Journal of Consumer Psychology, 24(4), 586–607.

    Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

    Book  Google Scholar 

  • Bollen, K. A., & Stine, R. (1990). Direct and indirect effects: Classical and bootstrap estimates of variability. Sociological Methodology, 20(1), 115–140.

    Google Scholar 

  • Bullock, J. G., Green, D. P., & Ha, S. E. (2010). Yes, but what’s the mechanism? (Don’t expect an easy answer). Journal of Personality and Social Psychology, 98(4), 550–558.

    Google Scholar 

  • Cavanaugh, L. A. (2014). Because I (don’t) deserve it: How relationship reminders and deservingness influence consumer indulgence. Journal of Marketing Research, 51(2), 218–232.

    Google Scholar 

  • Chandon, P., Wansink, B., & Laurent, G. (2000). A benefit congruency framework of sales promotion effectiveness. Journal of Marketing, 64(4), 65–81.

    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. Journal of Abnormal Psychology, 112(4), 558–577.

    Google Scholar 

  • Cole, D. A., & Preacher, K. J. (2014). Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error. Psychological Methods, 19(2), 300–315.

    Google Scholar 

  • Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis for field settings. Boston: Houghton Mifflin.

    Google Scholar 

  • Dalal, D. K., & Zickar, M. J. (2012). Some common myths about centering predictor variables in moderated multiple regression and polynomial regression. Organizational Research Methods, 15(3), 339–362.

    Google Scholar 

  • Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. New York: Guilford Press.

    Google Scholar 

  • Echambadi, R., & Hess, J. D. (2007). Mean-centering does not alleviate collinearity problems in moderated multiple regression models. Marketing Science, 26(3), 438–445.

    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), 1–22.

    Google Scholar 

  • Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82(397), 171–185.

    Google Scholar 

  • Fairchild, A. J., & MacKinnon, D. P. (2009). A general model for testing mediation and moderation effects. Prevention Science, 10(2), 87–99.

    Google Scholar 

  • Frazier, P., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51(1), 115–134.

    Google Scholar 

  • Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18(3), 233–239.

    Google Scholar 

  • Fritz, M. S., Taylor, A. B., & MacKinnon, D. P. (2012). Explanation of two anomalous results in statistical mediation analysis. Multivariate Behavioral Research, 47(1), 61–87.

    Google Scholar 

  • Fritz, M. S., Cox, M. G., & MacKinnon, D. P. (2015). Increasing statistical power in mediation models without increasing sample size. Evaluation & the Health Professions, 38(3), 343–366.

    Google Scholar 

  • Fritz, M. S., Kenny, D. A., & MacKinnon, D. P. (2016). The combined effects of measurement error and omitting confounders in the single-mediator model. Multivariate Behavioral Research, 51(5), 681–697.

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Upper Saddle River: Pearson Prentice Hall.

    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(4), 501–507.

    Google Scholar 

  • Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420.

    Google Scholar 

  • Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press.

    Google Scholar 

  • Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50(1), 1–22.

    Google Scholar 

  • Hayes, A. F. (2017). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs. https://doi.org/10.1080/03637751-2017-1352100.

  • Hayes, A. F., & Cai, L. (2007). Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722.

    Google Scholar 

  • Hayes, A. F., & Preacher, K. J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45(4), 627–660.

    Google Scholar 

  • Hayes, A. F., & Preacher, K. J. (2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67(3), 451–470.

    Google Scholar 

  • Hayes, A. F., & Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis does method really matter? Psychological Science, 24(10), 1918–1927.

    Google Scholar 

  • Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal, 25(1), 76–81.

    Google Scholar 

  • Hoyle, R. H., & Kenny, D. A. (1999). Sample size, reliability, and tests of statistical mediation. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 195–222). Thousand Oaks: Sage.

    Google Scholar 

  • Iacobucci, D. (2012). Mediation analysis and categorical variables: The final frontier. Journal of Consumer Psychology, 22(4), 582–594.

    Google Scholar 

  • Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equations models perform better than regressions. Journal of Consumer Psychology, 17(2), 139–153.

    Google Scholar 

  • Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334.

    Google Scholar 

  • Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2011). Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review, 105(4), 765–789.

    Google Scholar 

  • Imai, K., Tingley, D., & Yamamoto, T. (2013). Experimental designs for identifying causal mechanisms. Journal of the Royal Statistical Society: Series A (Statistics in Society), 176(1), 5–51.

    Google Scholar 

  • Jacoby, J., & Sassenberg, K. (2011). Interactions do not only tell us when, but can also tell us how: Testing process hypotheses by interaction. European Journal of Social Psychology, 41(2), 180–190.

    Google Scholar 

  • James, L. R., Mulaik, S. A., & Brett, J. M. (2006). A tale of two methods. Organizational Research Methods, 9(2), 233–244.

    Google Scholar 

  • Jose, P. E. (2013). Doing statistical mediation and moderation. New York: Guilford Press.

    Google Scholar 

  • Judd, C. M., & Kenny, D. A. (1981). Process analysis estimating mediation in treatment evaluations. Evaluation Review, 5(5), 602–619.

    Google Scholar 

  • Kelley, K. (2007). Methods for the behavioral, educational, and social sciences: An R package. Behavior Research Methods, 39(4), 979–984.

    Google Scholar 

  • Kenny, D. A. (2008). Reflections on mediation. Organizational Research Methods, 11(2), 353–358.

    Google Scholar 

  • Kenny, D. A., & Judd, C. M. (2014). Power anomalies in testing mediation. Psychological Science, 25(2), 334–339.

    Google Scholar 

  • Kisbu-Sakarya, Y., MacKinnon, D. P., & Miočević, M. (2014). The distribution of the product explains normal theory mediation confidence interval estimation. Multivariate Behavioral Research, 49(3), 261–268.

    Google Scholar 

  • Koschate-Fischer, N., & Schandelmeier, S. (2014). A guideline for designing experimental studies in marketing research and a critical discussion of selected problem areas. Journal of Business Economics, 84(6), 793–826.

    Google Scholar 

  • Koschate-Fischer, N., Stefan, I. V., & Hoyer, W. D. (2012). Willingness to pay for cause-related marketing: The impact of donation amount and moderating effects. Journal of Marketing Research, 49(6), 910–927.

    Google Scholar 

  • Koschate-Fischer, N., Huber, I. V., & Hoyer, W. D. (2016). When will price increases associated with company donations to charity be perceived as fair? Journal of the Academy of Marketing Science, 44(5), 608–626.

    Google Scholar 

  • Koschate-Fischer, N., Hoyer, W. D., Stokburger-Sauer, N. E., & Engling, J. (2017). Do life events always lead to change in purchase? The mediating role of change in consumer innovativeness, the variety seeking tendency, and price consciousness. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-017-0548-3.

  • Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59(10), 877–883.

    Google Scholar 

  • Kraemer, H. C., Kiernan, M., Essex, M., & Kupfer, D. J. (2008). How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychology, 27(2S), 101–108.

    Google Scholar 

  • Lemmer, G., & Gollwitzer, M. (2017). The “true” indirect effect won’t (always) stand up: When and why reverse mediation testing fails. Journal of Experimental Social Psychology, 69, 144–149.

    Google Scholar 

  • Lichtenstein, D. R., Netemeyer, R. G., & Burton, S. (1995). Assessing the domain specificity of deal proneness: A field study. Journal of Consumer Research, 22(3), 314–326.

    Google Scholar 

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

    Google Scholar 

  • MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17(2), 144–158.

    Google Scholar 

  • MacKinnon, D. P., & Pirlott, A. G. (2015). Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis. Personality and Social Psychology Review, 19(1), 30–43.

    Google Scholar 

  • MacKinnon, D. P., Warsi, G., & Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30(1), 41–62.

    Google Scholar 

  • MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science, 1(4), 173–181.

    Google Scholar 

  • 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(1), 83–104.

    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(1), 99–128.

    Google Scholar 

  • MacKinnon, D. P., Fritz, M. S., Williams, J., & Lockwood, C. M. (2007a). Distribution of the product confidence limits for the indirect effect: Program PRODCLIN. Behavior Research Methods, 39(3), 384–389.

    Google Scholar 

  • MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007b). Mediation analysis. Annual Review of Psychology, 58, 593–614.

    Google Scholar 

  • MacKinnon, D. P., Kisbu-Sakarya, Y., & Gottschall, A. C. (2013). Developments in mediation analysis. In T. D. Little (Ed.), The Oxford handbook of quantitative methods in psychology: Volume 2: Statistical analysis (pp. 338–360). New York: Oxford University Press.

    Google Scholar 

  • Mathieu, J. E., & Taylor, S. R. (2006). Clarifying conditions and decision points for mediational type inferences in organizational behavior. Journal of Organizational Behavior, 27(8), 1031–1056.

    Google Scholar 

  • Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44.

    Google Scholar 

  • Maxwell, S. E., Cole, D. A., & Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46(5), 816–841.

    Google Scholar 

  • Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110(1), 40–48.

    Google Scholar 

  • Montoya, A. K., & Hayes, A. F. (2017). Two condition within-participant statistical mediation analysis: A path-analytic framework. Psychological Methods, 22(1), 6–27.

    Google Scholar 

  • Morgan-Lopez, A. A., & MacKinnon, D. P. (2006). Demonstration and evaluation of a method for assessing mediated moderation. Behavior Research Methods, 38(1), 77–87.

    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(6), 852–863.

    Google Scholar 

  • Muthén, L. K., & Muthén, L. (1998). Mplus [computer software]. Los Angeles: Muthén & Muthén.

    Google Scholar 

  • Pek, J., & Hoyle, R. H. (2016). On the (in) validity of tests of simple mediation: Threats and solutions. Social and Personality Psychology Compass, 10(3), 150–163.

    Google Scholar 

  • Pieters, R. (2017). Meaningful mediation analysis: Plausible causal inference and informative communication. Journal of Consumer Research, 44(3), 692–716.

    Google Scholar 

  • Pirlott, A. G., & MacKinnon, D. P. (2016). Design approaches to experimental mediation. Journal of Experimental Social Psychology, 66, 29–38.

    Google Scholar 

  • Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66(1), 825–852.

    Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

    Google Scholar 

  • Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16(2), 93–115.

    Google Scholar 

  • Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77–98.

    Google Scholar 

  • Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185–227.

    Google Scholar 

  • Revelle, W. (2016). psych: Procedures for psychological, psychometric, and personality research (Version 1.6.12). http://personality-project.org/r, http://personality-project.org/r/psych-manual.pdf. Accessed 24 July 2017.

  • Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.

    Google Scholar 

  • Rucker, D. D., Preacher, K. J., Tormala, Z. L., & Petty, R. E. (2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5(6), 359–371.

    Google Scholar 

  • Savary, J., Goldsmith, K., & Dhar, R. (2014). Giving against the odds: When tempting alternatives increase willingness to donate. Journal of Marketing Research, 52(1), 27–38.

    Google Scholar 

  • Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445.

    Google Scholar 

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

    Google Scholar 

  • Spencer, S. J., Zanna, M. P., & Fong, G. T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89(6), 845–851.

    Google Scholar 

  • Stone-Romero, E. F., & Rosopa, P. J. (2008). The relative validity of inferences about mediation as a function of research design characteristics. Organizational Research Methods, 11(2), 326–352.

    Google Scholar 

  • Taylor, A. B., MacKinnon, D. P., & Tein, J.-Y. (2008). Tests of the three-path mediated effect. Organizational Research Methods, 11(2), 241–269.

    Google Scholar 

  • Thoemmes, F. (2015). Reversing arrows in mediation models does not distinguish plausible models. Basic and Applied Social Psychology, 37(4), 226–234.

    Google Scholar 

  • Thoemmes, F., MacKinnon, D. P., & Reiser, M. R. (2010). Power analysis for complex mediational designs using Monte Carlo methods. Structural Equation Modeling, 17(3), 510–534.

    Google Scholar 

  • Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). Mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5), 1–38.

    Google Scholar 

  • Tofighi, D., & MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43(3), 692–700.

    Google Scholar 

  • Tofighi, D., & Thoemmes, F. (2014). Single-level and multilevel mediation analysis. The Journal of Early Adolescence, 34(1), 93–119.

    Google Scholar 

  • Touré-Tillery, M., & McGill, A. L. (2015). Who or what to believe: Trust and the differential persuasiveness of human and anthropomorphized messengers. Journal of Marketing, 79(4), 94–110.

    Google Scholar 

  • Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure-mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, 18(2), 137–150.

    Google Scholar 

  • VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. New York: Oxford University Press.

    Google Scholar 

  • VanderWeele, T. J., & Vansteelandt, S. (2014). Mediation analysis with multiple mediators. Epidemiologic Methods, 2(1), 95–115.

    Google Scholar 

  • Wen, Z., & Fan, X. (2015). Monotonicity of effect sizes: Questioning kappa-squared as mediation effect size measure. Psychological Methods, 20(2), 193–203.

    Google Scholar 

  • Williams, J., & MacKinnon, D. P. (2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling: A Multidisciplinary Journal, 15(1), 23–51.

    Google Scholar 

  • Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20.

    Google Scholar 

  • Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206.

    Google Scholar 

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Authors and Affiliations

  1. University of Erlangen-Nuremberg, Nuremberg, Germany

    Nicole Koschate-Fischer & Elisabeth Schwille

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  1. Nicole Koschate-Fischer
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  2. Elisabeth Schwille
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Correspondence to Nicole Koschate-Fischer .

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  1. Department of Business-to-Business Marketing, Sales, and Pricing, University of Mannheim, Mannheim, Germany

    Christian Homburg

  2. Department of Marketing & Sales Research Group, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Martin Klarmann

  3. Marketing & Sales Department, University of Mannheim, Mannheim, Germany

    Arnd Vomberg

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Koschate-Fischer, N., Schwille, E. (2022). Mediation Analysis in Experimental Research. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-57413-4_34

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