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Mediation Analysis: Inferring Causal Processes in Marketing from Experiments

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Advanced Methods for Modeling Markets

Part of the book series: International Series in Quantitative Marketing ((ISQM))

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:

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Notes

  1. 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)

    Article  Google Scholar 

  • Angrist, J.D., Pischke, J.-S.: Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press, Princeton, Oxford (2009)

    Google Scholar 

  • Bagozzi, R.P.: Structural equation models in experimental research. J. Mark. Res. 14, 209–226 (1977)

    Article  Google Scholar 

  • Bagozzi, R.P.: Measurement and meaning in information systems and organizational research: methodological and philosophical foundations. MIS Q. 35, 261–292 (2011)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Bergkvist, L.: Appropriate use of single-item measures is here to stay. Mark. Lett. 26, 245–255 (2015)

    Article  Google Scholar 

  • Bollen, K.A.: Structural Equations with Latent Variables. John Wiley & Sons, New York (1989)

    Book  Google Scholar 

  • Bollen, K.A.: Instrumental variables in sociology and the social sciences. Annu. Rev. Sociol. 38, 37–72 (2012)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Cohen, J.: The statistical power of abnormal-social psychology research: a review. J. Abnorm. Soc. Psychol. 65, 145–153 (1962)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • Eisend, M., Tarrahi, F.: Meta-analysis selection bias in marketing research. Int. J. Res. Mark. 31, 317–326 (2014)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Fanelli, D.: Negative results are disappearing from most disciplines and countries. Scientometrics. 90, 891–904 (2012)

    Article  Google Scholar 

  • Fiedler, K., Schott, M., Meiser, T.: What mediation analysis can (not) do. J. Exp. Soc. Psychol. 47, 1231–1236 (2011)

    Article  Google Scholar 

  • Fornell, C., Larcker, D.F.: Structural equation models with unobserved variables and measurement error: algebra and statistics. J. Mark. Res. 18, 382–380 (1981)

    Article  Google Scholar 

  • Fritz, M.S., MacKinnon, D.P.: Required sample size to detect the mediated effect. Psychol. Sci. 18, 233–239 (2007)

    Article  Google Scholar 

  • Gelman, A., Carlin, J.: Beyond power calculations: assessing type s (sign) and type m (magnitude) errors. Perspect. Psychol. Sci. 9, 641–651 (2014)

    Article  Google Scholar 

  • Greene, W.H.: Econometric Analysis, 7th edn. Pearson Education ltd, Boston (2012)

    Google Scholar 

  • 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

    Google Scholar 

  • Hayes, A.F.: Introduction to Mediation, Moderation and Conditional Process Analysis: A Regression-Based Approach. New York: The Guilford Press (2013)

    Google Scholar 

  • Hemphill, J.F.: Interpreting the magnitudes of correlation coefficients. Am. Psychol. 58, 78–80 (2003)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Imai, K., Keele, L., Yamamoto, T.: Identification, inference and sensitivity analysis for causal mediation effects. Stat. Sci. 25, 51–71 (2010b)

    Article  Google Scholar 

  • Imai, K., Tingley, D., Yamamoto, T.: Experimental designs for identifying causal mechanism. J R Stat Soc A. 176, 5–51 (2013)

    Article  Google Scholar 

  • Ioannidis, J.P.A.: Why most published research findings are false. PLoS Med. 2, 696–701 (2005)

    Google Scholar 

  • Judd, C.M., Kenny, D.: Process analysis: estimating mediation in intervention evaluations. Eval. Rev. 5, 602–619 (1981)

    Article  Google Scholar 

  • Kenny, D.A., Judd, C.M.: Power anomalies in testing mediation. Psychol. Sci. 25, 334--339 (2014)

    Google Scholar 

  • Kerr, N.L.: HARKing: hypothesizing after the results are known. Personal. Soc. Psychol. Rev. 2, 196–217 (1998)

    Article  Google Scholar 

  • Kline, R.B.: The mediation myth. Basic Appl. Soc. Psychol. 37(4), 202–213 (2015)

    Article  Google Scholar 

  • Larcker, D.F., Rusticus, T.C.: On the use of instrumental variables in accounting research. J. Account. Econ. 49, 186–205 (2010)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • MacCallum, R.C., Austin, J.T.: Applications of structural equations modeling in psychological research. Annu. Rev. Psychol. 51, 201–236 (2000)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Maxwell, S.E.: The persistence of underpowered studies in psychological research: causes, consequences, and remedies. Psychol. Methods. 9, 147–163 (2004)

    Article  Google Scholar 

  • MacKinnon, D.P.: Introduction to Statistical Mediation Analysis. Lawrence Erlbaum Associates, New York (2008)

    Google Scholar 

  • Meehl, P.E.: Why summaries of research on psychological theories are often uninterpretable. Psychol. Rep. 66, 195–244 (1990)

    Article  Google Scholar 

  • Meyer, R.J.: Editorial: a field guide to publishing in an era of doubt. J. Mark. Res. 52, 577–579 (2015)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Morgan, S.L., Winship, C.: Counterfactuals and Causal Inference. Methods and Principles for Social Research. Cambridge University Press, Cambridge (2007)

    Book  Google Scholar 

  • Muthén, B., Asparouhov, T.: Causal effects in mediation modeling: an introduction with applications to latent variables. Struct. Equ. Model. 22, 12–23 (2015)

    Article  Google Scholar 

  • Muthén, L., Muthén, B.O.: MPlus User’s Guide, 7th edn. Muthén and Muthén, Los Angeles, CA (2014)

    Google Scholar 

  • Open Science Collaboration: Estimating the reproducibility of psychological science. Science. 349, 1–8 (2015)

    Article  Google Scholar 

  • Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, New York (2000)

    Google Scholar 

  • Pearl, J.: Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009)

    Article  Google Scholar 

  • Peterson, R.A.: A meta-analysis of cronbach’s coefficient alpha. J. Consum. Res. 21, 381–391 (1994)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Preacher, K., Kelley, K.: Effect size measures for mediation models: quantitative strategies for communicating indirect effects. Psychol. Methods. 16, 93–115 (2011)

    Article  Google Scholar 

  • Preacher, K., Rucker, D.D., Hayes, A.: Addressing moderated mediation hypotheses: theory, methods, and prescriptions. Multivar. Behav. Res. 42, 185–227 (2007)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Roberts, S., Pashler, H.: How persuasive is a good fit? a comment on theory testing. Psychol. Rev. 107, 358–367 (2000)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • Seggie, S.H., Griffith, D.A., Jap, S.D.: Passive and active opportunism in interorganizational exchange. J. Mark. 77, 73–90 (2013)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Smith, E.R.: Beliefs, attributions, and evaluations: nonhierarchical models of mediation in social cognition. J. Pers. Soc. Psychol. 43, 248–259 (1982)

    Article  Google Scholar 

  • Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15, 72–101 (1904)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • VanderWeele, T.J., Valeri, L., Ogburn, E.L.: The role of measurement error and misclassification in mediation analysis. Epidemiology. 23, 561–564 (2012)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Wanous, J.P., Hudy, M.J.: Single-item reliability: a replication and extension. Organ. Res. Methods. 4, 361–375 (2001)

    Article  Google Scholar 

  • Wells, W.D.: Discovery-oriented consumer research. J. Consum. Res. 19, 489–504 (1993)

    Article  Google Scholar 

  • Wright, S.: Correlation and causation. J. Agric. Res. 20, 557–585 (1921)

    Google Scholar 

  • Zhao, X., Lynch Jr., J., Chen, Q.: Reconsidering Baron and Kenny: myths and truths about mediation analysis. J. Consum. Res. 37, 197–206 (2010)

    Google Scholar 

  • Zhang, J., Wedel, M., Pieters, R.: Sales effects of attention to feature advertisements: a Bayesian mediation analysis. J. Mark. Res. 46, 669–681 (2009)

    Google Scholar 

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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

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