Abstract
Mediation analysis is applied to make causal inferences about the process that accounts for the effect that an intervention has on an outcome. Such an intervention can range from short-term tactics such as the content of a new on-line campaign or the size of a temporary price-cut, to far-ranging strategic interventions about customer loyalty programs, product introductions, brand extensions, retail chain mergers and so forth. Outcomes may involve any self-reported or observed state, trait, belief or action of consumers, managers, and firms. Mediation analysis is academically important because it enables tests of theories about causal processes, and it is policy relevant because improved insight into these causal processes might lead to more effective and efficient interventions. It has become an indispensable tool in the marketing researcher’s toolbox because of this hope for insight into the causal process, and because of foundational publications on mediation analysis, the development of statistical procedures to test for mediation, and because of the availability of these procedures in common statistical software (e.g., Baron and Kenny 1986; Hayes 2012, 2013; MacKinnon 2008; Preacher et al. 2007; Rucker et al. 2011; Shrout and Bolger 2002; Zhao et al. 2010). Mediation analysis is central in many academic disciplines. It is considered:
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
See also Chap. 6.
References
Abelson, R.P.: A variance explanation paradox: when a little is a lot. Psychol. Bull. 97, 129–133 (1985)
Angrist, J.D., Pischke, J.-S.: Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press, Princeton, Oxford (2009)
Bagozzi, R.P.: Structural equation models in experimental research. J. Mark. Res. 14, 209–226 (1977)
Bagozzi, R.P.: Measurement and meaning in information systems and organizational research: methodological and philosophical foundations. MIS Q. 35, 261–292 (2011)
Baron, R.M., Kenny, D.A.: The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173–1182 (1986)
Bergkvist, L.: Appropriate use of single-item measures is here to stay. Mark. Lett. 26, 245–255 (2015)
Bollen, K.A.: Structural Equations with Latent Variables. John Wiley & Sons, New York (1989)
Bollen, K.A.: Instrumental variables in sociology and the social sciences. Annu. Rev. Sociol. 38, 37–72 (2012)
Bollen, K.A., Pearl, J.: Eight myths about causality and structural equation models. In: Morgan, S. (ed.) Handbook of Causal Analysis for Social Research, pp. 301–328. Springer., Chapter 15, New York (2013)
Bullock, J.G., Green, D.P., Ha, S.E.: Yes, but what’s the mechanism? (don’t expect an easy answer). J. Pers. Soc. Psychol. 98, 550–558 (2010)
Bullock, J.G., Ha, S.E.: Mediation analysis is harder than it looks. In: Druckman, J.N., Green, D.P., Kuklinski, J.H., Lupia, A. (eds.) Cambridge Handbook of Experimental Political Science, pp. 508–521. Cambridge University Press, Cambridge (2011)
Button, K.S., Ioannidis, J.P.A., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S.J., Munafo, M.R.: Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 1-12, (2013)
Cashen, L.H., Geiger, S.W.: Statistical power and the testing of null hypotheses: a review of contemporary management research and recommendations for future studies. Organ. Res. Methods. 7, 151–167 (2004)
Cohen, J.: The statistical power of abnormal-social psychology research: a review. J. Abnorm. Soc. Psychol. 65, 145–153 (1962)
Cohen, J., Cohen, P., West, S.G., Aiken, L.S.: Applied Multiple Regression/correlation Analysis for the Behavioral Sciences, 3rd edn. Lawrence Erlbaum Associates, Mahwah, NJ (2003)
Eisend, M., Tarrahi, F.: Meta-analysis selection bias in marketing research. Int. J. Res. Mark. 31, 317–326 (2014)
Emsley, R., Dunn, G.: Evaluation of potential mediators in randomised trials of complex interventions (psychotherapies). In: Berzuini, C., Dawid, P., Bernardelli, L. (eds.) Causality: Statistical Perspectives and Applications, pp. 290–309. John Wiley & Sons, Chicester (2012)
Fanelli, D.: Negative results are disappearing from most disciplines and countries. Scientometrics. 90, 891–904 (2012)
Fiedler, K., Schott, M., Meiser, T.: What mediation analysis can (not) do. J. Exp. Soc. Psychol. 47, 1231–1236 (2011)
Fornell, C., Larcker, D.F.: Structural equation models with unobserved variables and measurement error: algebra and statistics. J. Mark. Res. 18, 382–380 (1981)
Fritz, M.S., MacKinnon, D.P.: Required sample size to detect the mediated effect. Psychol. Sci. 18, 233–239 (2007)
Gelman, A., Carlin, J.: Beyond power calculations: assessing type s (sign) and type m (magnitude) errors. Perspect. Psychol. Sci. 9, 641–651 (2014)
Greene, W.H.: Econometric Analysis, 7th edn. Pearson Education ltd, Boston (2012)
Hayes, A. F.: PROCESS: A Versatile Computational Tool for Observed variable Mediation, Moderation, and Conditional Process Modeling (2012), Retrieved from http://www.afhayes.com/public/process 2012.pdf
Hayes, A.F.: Introduction to Mediation, Moderation and Conditional Process Analysis: A Regression-Based Approach. New York: The Guilford Press (2013)
Hemphill, J.F.: Interpreting the magnitudes of correlation coefficients. Am. Psychol. 58, 78–80 (2003)
Iacobucci, D., Saldanha, N., Deng, X.: A meditation on mediation: evidence that structural equations model perform better than regressions. J. Consum. Psychol. 17, 140–154 (2007)
Imai, K., Keele, L., Tingley, D.: A general approach to causal mediation analysis. Psychol. Methods. 15, 309–334 (2010a)
Imai, K., Keele, L., Yamamoto, T.: Identification, inference and sensitivity analysis for causal mediation effects. Stat. Sci. 25, 51–71 (2010b)
Imai, K., Tingley, D., Yamamoto, T.: Experimental designs for identifying causal mechanism. J R Stat Soc A. 176, 5–51 (2013)
Ioannidis, J.P.A.: Why most published research findings are false. PLoS Med. 2, 696–701 (2005)
Judd, C.M., Kenny, D.: Process analysis: estimating mediation in intervention evaluations. Eval. Rev. 5, 602–619 (1981)
Kenny, D.A., Judd, C.M.: Power anomalies in testing mediation. Psychol. Sci. 25, 334--339 (2014)
Kerr, N.L.: HARKing: hypothesizing after the results are known. Personal. Soc. Psychol. Rev. 2, 196–217 (1998)
Kline, R.B.: The mediation myth. Basic Appl. Soc. Psychol. 37(4), 202–213 (2015)
Larcker, D.F., Rusticus, T.C.: On the use of instrumental variables in accounting research. J. Account. Econ. 49, 186–205 (2010)
Ledgerwood, A., Shrout, P.E.: The trade-off between accuracy and precision in latent variable models of mediation processes. J. Pers. Soc. Psychol. 101(6), 1174–1188 (2011)
Lee, S.W.S., Schwarz, N.: Bidirectionality, mediation, and moderation of metaphorical effects: the embodiment of social suspicion and fishy smells. J. Pers. Soc. Psychol. 103, 737–749 (2012)
Lykken, D.T.: What’s wrong with psychology anyway? In: Cicchetti, D., Grove, W.M. (eds.) Thinking Clearly about Psychology, Volume 1: Matters of Public Interest, pp. 3–39. University of Minnesota Press, Minneappolis (1991)
MacCallum, R.C., Austin, J.T.: Applications of structural equations modeling in psychological research. Annu. Rev. Psychol. 51, 201–236 (2000)
Mauro, R.: Understanding L.O.V.E. (left out variables error): a method for estimating the effects of omitted variables. Psychol. Bull. 108, 314–329 (1990)
Maxwell, S.E.: The persistence of underpowered studies in psychological research: causes, consequences, and remedies. Psychol. Methods. 9, 147–163 (2004)
MacKinnon, D.P.: Introduction to Statistical Mediation Analysis. Lawrence Erlbaum Associates, New York (2008)
Meehl, P.E.: Why summaries of research on psychological theories are often uninterpretable. Psychol. Rep. 66, 195–244 (1990)
Meyer, R.J.: Editorial: a field guide to publishing in an era of doubt. J. Mark. Res. 52, 577–579 (2015)
Middlewood, B.L., Gasper, K.: Making information matter: symmetrically appealing layouts promote issue relevance, which facilitates action and attention to argument quality. J. Exp. Soc. Psychol. 53, 100–106 (2014)
Morgan, S.L., Winship, C.: Counterfactuals and Causal Inference. Methods and Principles for Social Research. Cambridge University Press, Cambridge (2007)
Muthén, B., Asparouhov, T.: Causal effects in mediation modeling: an introduction with applications to latent variables. Struct. Equ. Model. 22, 12–23 (2015)
Muthén, L., Muthén, B.O.: MPlus User’s Guide, 7th edn. Muthén and Muthén, Los Angeles, CA (2014)
Open Science Collaboration: Estimating the reproducibility of psychological science. Science. 349, 1–8 (2015)
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, New York (2000)
Pearl, J.: Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009)
Peterson, R.A.: A meta-analysis of cronbach’s coefficient alpha. J. Consum. Res. 21, 381–391 (1994)
Pieters, R.: Meaningful mediation analysis: strenghtening the weakest link in causal inference from experiments. Unpublished Report, Tilburg School of Economics and Management, Tilburg University, Tilburg, The Netherlands (2016)
Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P.: Sources of method bias in social science research and recommendations on how to control it. Annu. Rev. Psychol. 63, 539–569 (2012)
Preacher, K., Kelley, K.: Effect size measures for mediation models: quantitative strategies for communicating indirect effects. Psychol. Methods. 16, 93–115 (2011)
Preacher, K., Rucker, D.D., Hayes, A.: Addressing moderated mediation hypotheses: theory, methods, and prescriptions. Multivar. Behav. Res. 42, 185–227 (2007)
Richard, F.D., Bond Jr., C.F., Stoles-Zoota, J.: One hundred years of social psychology quantitatively described. Rev. Gen. Psychol. 7, 331–363 (2003)
Richardson, H.A., Simmering, M.J., Sturman, M.C.: A tale of three perspectives: examining post hoc statistical techniques for detection and correction of common method variance. Organ. Res. Methods. 12, 762–800 (2009)
Roberts, S., Pashler, H.: How persuasive is a good fit? a comment on theory testing. Psychol. Rev. 107, 358–367 (2000)
Rossi, P.E.: Even the rich can make themselves poor: a critical examination of the use of IV methods in marketing. Mark. Sci. 33, 655–672 (2014)
Rucker, D.D., Preacher, K.J., Tormala, Z.L., Petty, R.E.: Mediation analysis in social psychology: current practices and new recommendations. Soc. Personal. Psychol. Compass. 5(6), 359–371 (2011)
Sawyer, A.G., Lynch Jr., J.G., Brinberg, D.: A Bayesian analysis of the information value of manipulation and confounding checks in theory tests. J. Consum. Res. 21, 581–595 (1995)
Seggie, S.H., Griffith, D.A., Jap, S.D.: Passive and active opportunism in interorganizational exchange. J. Mark. 77, 73–90 (2013)
Shook, C.L., Ketchen Jr., D.J., Hult, G.T., Kacmar, K.M.: An assessment of the use of structural equation modeling in strategic management research. Strateg. Manag. J. 25, 397–404 (2004)
Shrout, P.E., Bolger, N.: Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychol. Methods. 7, 422–445 (2002)
Simmons, J.P., Nelson, L.D., Simonsohn, U.: False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011)
Smith, E.R.: Beliefs, attributions, and evaluations: nonhierarchical models of mediation in social cognition. J. Pers. Soc. Psychol. 43, 248–259 (1982)
Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15, 72–101 (1904)
Spencer, S.J., Zanna, M.P., Fong, G.T.: Establishing a causal chain: why experiments are often more effective than mediational analyses in examining psychological processes. J. Pers. Soc. Psychol. 89, 845–851 (2005)
Ten Have, T.R., Joffe, M.M.: A review of causal estimation of effects in mediation analyses. Stat. Methods Med. Res. 21, 77–107 (2010)
Valeri, L., VanderWeele, T.: Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol. Methods. 18, 137–150 (2013)
VanSteelandt, S.: Estimation for direct and indirect effects. In: Berzuini, C., Dawid, P., Bernardelli, L. (eds.) Causality: Statistical Perspectives and Applications, pp. 127–150. John Wiley & Sons, Chicester (2012)
VanderWeele, T.J., Valeri, L., Ogburn, E.L.: The role of measurement error and misclassification in mediation analysis. Epidemiology. 23, 561–564 (2012)
Viswesvaran, C., Ones, D.S.: Measurement error in “big five factors” personality assessment: reliability generalization across studies and measures. Educ. Psychol. Meas. 60, 224–235 (2000)
Vul, E., Harris, C., Winkielman, P., Pashler, H.: Puzzingly high correlations in fmri studies of emotion, personality, and social cognition. Perspect. Psychol. Sci. 4, 274–291 (2009)
Wanous, J.P., Hudy, M.J.: Single-item reliability: a replication and extension. Organ. Res. Methods. 4, 361–375 (2001)
Wells, W.D.: Discovery-oriented consumer research. J. Consum. Res. 19, 489–504 (1993)
Wright, S.: Correlation and causation. J. Agric. Res. 20, 557–585 (1921)
Zhao, X., Lynch Jr., J., Chen, Q.: Reconsidering Baron and Kenny: myths and truths about mediation analysis. J. Consum. Res. 37, 197–206 (2010)
Zhang, J., Wedel, M., Pieters, R.: Sales effects of attention to feature advertisements: a Bayesian mediation analysis. J. Mark. Res. 46, 669–681 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Pieters, R. (2017). Mediation Analysis: Inferring Causal Processes in Marketing from Experiments. In: Leeflang, P., Wieringa, J., Bijmolt, T., Pauwels, K. (eds) Advanced Methods for Modeling Markets. International Series in Quantitative Marketing. Springer, Cham. https://doi.org/10.1007/978-3-319-53469-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-53469-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53467-1
Online ISBN: 978-3-319-53469-5
eBook Packages: Business and ManagementBusiness and Management (R0)