Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-669899f699-tpknm Total loading time: 0 Render date: 2025-04-29T15:34:36.602Z Has data issue: false hasContentIssue false

35 - Mediation Analysis Is Harder Than It Looks

Published online by Cambridge University Press:  05 June 2012

By
John G. Bullock  and
Shang E. Ha
Edited by
James N. Druckman ,
Donald P. Greene ,
James H. Kuklinski  and
Arthur Lupia
John G. Bullock
Affiliation:
Yale University
Shang E. Ha
Affiliation:
City University of New York
James N. Druckman
Affiliation:
Northwestern University, Illinois
Donald P. Greene
Affiliation:
Yale University, Connecticut
James H. Kuklinski
Affiliation:
University of Illinois, Urbana-Champaign
Arthur Lupia
Affiliation:
University of Michigan, Ann Arbor
Get access

Summary

Mediators are variables that transmit causal effects from treatments to outcomes. Those who undertake mediation analysis seek to answer “how” questions about causation: how does this treatment affect that outcome? Typically, we desire answers of the form “the treatment affects a causally intermediate variable, which in turn affects the outcome.” Identifying these causally intermediate variables is the challenge of mediation analysis.

Conjectures about political mediation effects are as old as the study of politics. But codification of procedures by which to test hypotheses about mediation is a relatively new development. The most common procedures are now ubiquitous in psychology (Quiñones-Vidal et al. 2004) and increasingly popular in the other social sciences, not least political science.

Unfortunately, the most common procedures are not very good. They call for indirect effects – the portions of treatment effects that are transmitted through mediators – to be estimated via multiequation regression frameworks. These procedures do not require experimental manipulation of mediators; instead, they encourage the study of mediation with data from unmanipulated mediators (MacKinnon et al. 2002, 86; Spencer, Zanna, and Fong 2005). The procedures are therefore prone to producing biased estimates of mediation effects. Warnings about this problem have been issued for decades by statisticians, psychologists, and political scientists.

Type
Chapter
Information
Cambridge Handbook of Experimental Political Science , pp. 508 - 522
Publisher: Cambridge University Press
Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Angrist, Joshua D., Imbens, Guido W., and Rubin, Donald B.. 1996. “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association 91: 444–55.CrossRefGoogle Scholar
Angrist, Joshua D., Lavy, Victor, and Schlosser, Analia. 2010. “Multiple Experiments for the Causal Link between the Quantity and Quality of Children.” Journal of Labor Economics 28: 773–824.CrossRefGoogle Scholar
Angrist, Joshua D., and Pischke, Jörn-Steffen. 2009. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton University Press.Google Scholar
Baron, Reuben M., and Kenny, David A.. 1986. “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations.” Journal of Personality and Social Psychology 51: 1173–82.CrossRefGoogle ScholarPubMed
Bartels, Larry M. 1991. “Instrumental and ‘Quasi-Instrumental’ Variables.” American Journal of Political Science 35: 777–800.CrossRefGoogle Scholar
Bolger, Niall, and Amarel, David. 2007. “Effects of Social Support Visibility on Adjustment to Stress: Experimental Evidence.” Journal of Personality and Social Psychology 92: 458–75.CrossRefGoogle ScholarPubMed
Bound, John, Jaeger, David A., and Baker, Regina M.. 1995. “Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variable Is Weak.” Journal of the American Statistical Association 90: 443–50.Google Scholar
Brader, Ted A., and Tucker, Joshua A.. 2008. “Reflective and Unreflective Partisans? Experimental Evidence on the Links between Information, Opinion, and Party Identification.” Manuscript, New York University.
Brader, Ted, Valentino, Nicholas A., and Suhay, Elizabeth. 2008. “What Triggers Public Opposition to Immigration? Anxiety, Group Cues, and Immigration Threat.” American Journal of Political Science 52: 959–78.CrossRefGoogle Scholar
Bullock, John G., Green, Donald P., and Ha, Shang E.. 2008. “Experimental Approaches to Mediation: A New Guide for Assessing Causal Pathways.” Unpublished manuscript, Yale University.
Bullock, John G., Green, Donald P., and Ha, Shang E.. 2010. “Yes, But What's the Mechanism? (Don't Expect an Easy Answer).” Journal of Personality and Social Psychology 98: 550–58.CrossRefGoogle ScholarPubMed
Burdein, Inna, Lodge, Milton, and Taber, Charles. 2006. “Experiments on the Automaticity of Political Beliefs and Attitudes.” Political Psychology 27: 359–71.CrossRefGoogle Scholar
Campbell, Angus, Philip, E.Converse, Warren Miller, and Donald Stokes. 1960. The American Voter. Chicago: The University of Chicago Press.Google Scholar
Clarke, Kevin A. 2009. “Return of the Phantom Menace: Omitted Variable Bias in Political Research.” Conflict Management and Peace Science 26: 46–66.CrossRefGoogle Scholar
Cohen, Geoffrey L. 2003. “Party over Policy: The Dominating Impact of Group Influence on Political Beliefs.” Journal of Personality and Social Psychology 85: 808–22.CrossRefGoogle ScholarPubMed
Downs, Anthony. 1957. An Economic Theory of Democracy. New York: HarperCollins.Google Scholar
Fowler, James H., and Dawes, Christopher T.. 2008. “Two Genes Predict Voter Turnout.” Journal of Politics 70: 579–94.CrossRefGoogle Scholar
Frangakis, Constantine E., and Rubin, Donald B.. 2002. “Principal Stratification in Causal Inference.” Biometrics 58: 21–29.CrossRefGoogle ScholarPubMed
Gelman, Andrew, and Hill, Jennifer. 2007. Data Analysis Using Regression and Multilevel Hierarchical Models. New York: Cambridge University Press.Google Scholar
Gerber, Alan S., and Green, Donald P.. 2000. “The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout: A Field Experiment.” American Political Science Review 94: 653–62.CrossRefGoogle Scholar
Gerber, Alan S., Huber, Gregory A., and Washington, Ebonya. 2010. “Party Affiliation, Partisanship, and Political Beliefs: A Field Experiment.” American Political Science Review 104: 720–44.CrossRef
Glynn, Adam N. 2010. “The Product and Difference Fallacies for Indirect Effects.” Unpublished manuscript, Harvard University.
Green, Donald P., Ha, Shang E., and Bullock, John G.. 2010. “Enough Already about ‘Black Box’ Experiments: Studying Mediation Is More Difficult Than Most Scholars Suppose.” Annals of the American Academy of Political and Social Sciences 628: 200–8.CrossRefGoogle Scholar
Imai, Kosuke, Keele, Luke, Tingley, Dustin, and Yamamoto, Teppei. 2010. “Unpacking the Black Box: Learning about Causal Mechanisms from Experimental and Observational Studies.” Unpublished manuscript, Princeton University. Retrieved from http://imai.princeton.edu/research/files/mediationP.pdf (November 21, 2010).
Imai, Kosuke, Keele, Luke, and Yamamoto, Teppei. 2010. “Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects.” Statistical Science 25: 51–71.CrossRefGoogle Scholar
Imbens, Guido W., and Angrist, Joshua D.. 1994. “Identification and Estimation of Local Average Treatment Effects.” Econometrica 62: 467–75.CrossRefGoogle Scholar
Iyengar, Shanto, and Kinder, Donald R.. 1987. News That Matters: Television and American Opinion. Chicago: The University of Chicago Press.Google Scholar
James, Lawrence R. 1980. “The Unmeasured Variables Problem in Path Analysis.” Journal of Applied Psychology 65: 415–21.CrossRefGoogle Scholar
James, Lawrence R. 2008. “On the Path to Mediation.” Organizational Research Methods 11: 359–63.CrossRefGoogle Scholar
Judd, Charles M., and Kenny, David A.. 1981. “Process Analysis: Estimating Mediation in Treatment Evaluations.” Evaluation Review 5: 602–19.CrossRefGoogle Scholar
Kenny, David A. 2008. “Reflections on Mediation.” Organizational Research Methods 11: 353–58.CrossRefGoogle Scholar
King, Gary, and Zeng, Langche. 2006. “The Dangers of Extreme Counterfactuals.” Political Analysis 14: 131–59.CrossRefGoogle Scholar
LaLonde, Robert J. 1986. “Evaluating the Econometric Evaluations of Training Programs with Experimental Data.” American Economic Review 76: 604–20.Google Scholar
MacKinnon, David P., Lockwood, Chondra M., Hoffman, Jeanne M., West, Stephen G., and Sheets, Virgil. 2002. “A Comparison of Methods to Test Mediation and Other Intervening Variable Effects.” Psychological Methods 7: 83–104.CrossRefGoogle ScholarPubMed
Malhotra, Neil, and Krosnick, Jon A.. 2007. “Retrospective and Prospective Performance Assessments during the 2004 Election Campaign: Tests of Mediation and News Media Priming.” Political Behavior 29: 249–78.CrossRefGoogle Scholar
Miller, Joanne M., and Krosnick, Jon A.. 2000. “News Media Impact on the Ingredients of Presidential Evaluations.” American Journal of Political Science 44: 295–309.CrossRefGoogle Scholar
Morgan, Stephen L., and Winship, Christopher. 2007. Counterfactuals and Causal Inference. New York: Cambridge University Press.CrossRefGoogle Scholar
Nelson, Thomas E. 2004. “Policy Goals, Public Rhetoric, and Political Attitudes.” Journal of Politics 66: 581–605.CrossRefGoogle Scholar
Nelson, Thomas E., Clawson, Rosalee A., and Oxley, Zoe M.. 1997. “Media Framing of a Civil Liberties Conflict and Its Effect on Tolerance.” American Political Science Review 91: 567–94.CrossRefGoogle Scholar
Pearl, Judea. 2010. “The Mediation Formula: A Guide to the Assessment of Causal Pathways in Non-Linear Models.” Unpublished manuscript, University of California, Los Angeles. Retrieved from http://ftp.cs.ucla.edu/~kaoru/r363.pdf (November 21, 2010).
Petty, Richard E., and Wegener, Duane T.. 1998. “Attitude Change: Multiple Roles for Persuasion Variables.” In The Handbook of Social Psychology. vol. 1, 4th ed., eds. Daniel T. Gilbert, Susan T. Fiske, and Gardner Lindzey. New York: McGraw-Hill, 323–90.Google Scholar
Quiñones-Vidal, Elena, López-Garcia, Juan J., Peñaranda-Ortega, Maria, and Tortosa-Gil, Francisco. 2004. “The Nature of Social and Personality Psychology as Reflected in JPSP, 1965–2000.” Journal of Personality and Social Psychology 86: 435–52.CrossRefGoogle ScholarPubMed
Robins, James M. 2003. “Semantics of Causal DAG Models and the Identification of Direct and Indirect Effects.” In Highly Structured Stochastic Systems, eds. Peter J. Green, Nils Lid Hjort, and Sylvia Richardson. New York: Oxford University Press, 70–81.Google Scholar
Rosenbaum, Paul R. 1984. “The Consequences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment.” Journal of the Royal Statistical Society, Series A 147: 656–66.CrossRefGoogle Scholar
Rubin, Donald B. 2004. “Direct and Indirect Causal Effects via Potential Outcomes.” Scandinavian Journal of Statistics 31: 161–70.CrossRefGoogle Scholar
Spencer, Steven J., Zanna, Mark P., and Fong, Geoffrey 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: 845–51.CrossRefGoogle ScholarPubMed
Zanna, Mark P., and Cooper, Joel. 1974. “Dissonance and the Pill: An Attribution Approach to Studying the Arousal Properties of Dissonance.” Journal of Personality and Social Psychology 29: 703–9.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×