The Mediation Myth

REFERENCES

• Abelson, R. P. (1997). A retrospective on the significance test ban of 1999 (If there were no significance tests, they would be invented). In L. L. Harlow S. A. Mulaik & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 117–141). Mahwah, NJ: Erlbaum.
   Google Scholar
• Alwin, D. F., & Hauser, R. M. (1975). The decomposition of effects in path analysis. American Sociological Review, 40, 37–47. doi:10.2307/2094445
   Web of Science ®Google Scholar
• Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444–455. doi:10.1080/01621459.1996.10476902
   Web of Science ®Google Scholar
• Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086–1120. doi:10.1016/j.leaqua.2010.10.010
   Web of Science ®Google Scholar
• 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, 1173–1182. doi:10.1037/0022-3514.51.6.1173
   PubMed Web of Science ®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, 550–558. doi:10.1037/a0018933
   PubMed Web of Science ®Google Scholar
• Cheung, M. W. L. (2009). Comparison of methods for constructing confidence intervals of standardized indirect effects. Behavior Research Methods, 41, 425–438. doi:10.3758/BRM.41.2.425
   PubMed Web of Science ®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, 558–577. doi:10.1037/0021-843X.112.4.558
   PubMed Web of Science ®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, 300–315. doi:10.1037/a0033805
   PubMed Web of Science ®Google Scholar
• Edwards, J. R. (2009). Seven deadly myths of testing moderation in organizational research. In C. E. Lance & R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences (pp. 143–164). New York, NY: Taylor & Francis.
   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, 1–22. doi:10.1037/1082-989X.12.1.1
   PubMed Web of Science ®Google Scholar
• Erceg-Hurn, D. M., & Mirosevich, V. M. (2008). Modern robust statistical methods: An easy way to maximize the accuracy and power of your research. American Psychologist, 63, 591–601. doi:10.1037/0003-066X.63.7.591
   PubMed Web of Science ®Google Scholar
• Fincham, J. E. (2007). Patient compliance with medications: Issues and opportunities. Binghamton, NY: Pharmaceutical Products Press.
   Google Scholar
• Finney, J. M. (1972). Indirect effects in path analysis. Sociological Methods & Research, 1, 175–186. doi:10.1177/004912417200100202
   Google Scholar
• Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51, 115–134. doi:10.1037/0022-0167.51.1.115
   Web of Science ®Google Scholar
• Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18, 233–239. doi:10.1111/j.1467-9280.2007.01882.x
   PubMed Web of Science ®Google Scholar
• Haller, H., & Krauss, S. (2002). Misinterpretations of significance: A problem students share with their teachers? Methods of Psychological Research Online, 7, 1–17. Retrieved from http://www.dgps.de/fachgruppen/methoden/mpr-online/
   Google Scholar
• Hayduk, L. A., & Littvay, L. (2012). Should researchers use single indicators, best indicators, or multiple indicators in structural equation models? BMC Medical Research Methodology, 12(159). doi:10.1186/1471-2288-12-159
   PubMed Web of Science ®Google Scholar
• Hayes, A. F. (2013). Introduction to mediation, moderation, and process control analysis: A regression-based approach. New York, NY: Guilford Press.
   Google Scholar
• Hoekstra, R., Kiers, H. A. L, & Johnson, A. (2012). Are assumptions of well-known statistical techniques checked, and why (not)? Frontiers in Psychology, 3, Article 137. Retrieved from http://journal.frontiersin.org/article/10.3389/fpsyg.2012.00137/full
   PubMed Web of Science ®Google Scholar
• Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309–334. doi:10.1037/a0020761
   PubMed Web of Science ®Google Scholar
• Jo, B., Stuart, E. A., MacKinnon, D. P., & Vinokur, A. D. (2011). The use of propensity scores in mediation analysis. Multivariate Behavioral Research, 46, 425–452. doi:10.1080/00273171.2011.576624
   PubMed Web of Science ®Google Scholar
• Kenny, D. A. (2014). Mediation. Retrieved from http://davidakenny.net/cm/mediate.htm#CI
   Google Scholar
• Keselman, H. J., Huberty, C. J., Lix, L. M., Olejnik, S., Cribbie, R. A., Donahue, B. & Levin, J. R. (1998). Statistical practices of education researchers: An analysis of the ANOVA, MANOVA, and ANCOVA analyses. Review of Educational Research, 68, 350–368. doi:10.3102/00346543068003350
   Web of Science ®Google Scholar
• Kline, R. B. (2013). Beyond significance testing: Statistics reform in the behavioral sciences (2nd ed.). Washington, DC: American Psychological Association.
   Google Scholar
• Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, NY: Guilford Press.
   Google Scholar
• Knight, C. R., & Winship, C. (2013). The causal implications of mechanistic thinking: Identification using directed acyclic graphs (DAGs). In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 275–299). New York, NY: Springer.
   Google Scholar
• Lambdin, C. (2012). Significance tests as sorcery: Science is empirical—significance tests are not. Theory & Psychology, 22, 67–90. doi:10.1177/0959354311429854
   Web of Science ®Google Scholar
• Lau, R. S., & Cheung, G. W. (2012). Estimating and comparing specific mediation effects in complex latent variable models. Organizational Research Methods, 15, 3–16. doi:10.1177/1094428110391673
   Web of Science ®Google Scholar
• Little, T. D. (2013). Longitudinal structural equation modeling. New York, NY: Guilford Press.
   Google Scholar
• Loftus, G. R. (1993). Editorial comment. Memory & Cognition, 21, 1–3. doi:10.3758/BF03211158
   Google Scholar
• MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. doi:10.1146/annurev.psych.58.110405.085542
   PubMed Web of Science ®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, 30–43. doi:10.1177/1088868314542878
   PubMed Web of Science ®Google Scholar
• Mauro, R. (1990). Understand L.O.V.E. (left out variables error): A method for estimating the effects of omitted variables. Psychological Bulletin, 108, 314–329. doi:10.1037/0033-2909.108.2.314
   Web of Science ®Google Scholar
• Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12, 23–44. doi:10.1037/1082-989X.12.1.23
   PubMed Web of Science ®Google Scholar
• Pearl, J. (2000). Causality: Models, reasoning, and inference. New York, NY: Cambridge University Press.
   Google Scholar
• Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods, 19, 459–481. doi:10.1037/a0036434
   PubMed Web of Science ®Google Scholar
• Petersen, M. L., Sinisi, S. E., & van der Laan, M. J. (2006). Estimation of direct causal effects. Epidemiology, 17, 276–284. doi:10.1097/01.ede.0000208475.99429.2d
   PubMed Web of Science ®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, 879–891. doi:10.3758/BRM.40.3.879
   PubMed Web of Science ®Google Scholar
• Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93–115. doi:10.1037/a0022658
   PubMed Web of Science ®Google Scholar
• Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185–227. doi:10.1080/00273170701341316
   PubMed Web of Science ®Google Scholar
• Rigdon, E. E. (1995). A necessary and sufficient identification rule for structural models estimated in practice. Multivariate Behavioral Research, 30, 359–383. doi:10.1207/s15327906mbr3003_4
   PubMed Web of Science ®Google Scholar
• Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550–560. doi:10.1097/00001648-200009000-00011
   PubMed Web of Science ®Google Scholar
• Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322–331. doi:10.1198/016214504000001880
   Web of Science ®Google Scholar
• Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6, 144–164. doi:10.1080/15427600902911247
   Web of Science ®Google Scholar
• Shadish, W. R., & Sweeney, R. B. (1991). Mediators and moderators in meta-analysis: There's a reason we don't let dodo birds tell us which psychotherapies should have prizes. Journal of Consulting and Clinical Psychology, 59, 883–893. doi:10.1037/0022-006X.59.6.883
   PubMed Web of Science ®Google Scholar
• Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22, 1359–1366. doi:10.1177/0956797611417632
   PubMed Web of Science ®Google Scholar
• Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological methodology (pp. 290–312). San Francisco, CA: Jossey-Bass.
   Google Scholar
• Stone-Romero, E. F., & Rosopa, P. J. (2011). Experimental tests of mediation models: Prospects, problems, and some solutions. Organizational Research Methods, 14, 631–646. doi:10.1177/1094428110372673
   Web of Science ®Google Scholar
• Trafimow, D., & Marks, M. (2015). Editorial. Basic and Applied Social Psychology, 37, 1–2. doi:10.1080/01973533.2015.1012991
   Web of Science ®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, 137–150. doi:10.1037/a0031034
   PubMed Web of Science ®Google Scholar
• Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9, 476–487. doi:10.1016/j.elerap.2010.07.003
   Web of Science ®Google Scholar
• Wright, S. (1923). The theory of path coefficients: A reply to Niles's criticism. Genetics, 8, 239–255.
   PubMedGoogle Scholar
• Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5, 161–215. doi:10.1214/aoms/1177732676
   Google Scholar
• Ziliak, S., & McCloskey, D. N. (2008). The cult of statistical significance: How the standard error costs us jobs, justice, and lives. Ann Arbor: University of Michigan Press.
   Google Scholar