The Impossible Mediation Test: A Method for Dealing with Plausibly Confounded Mediation David S. Yeager University of Texas at Austin Jon A. Krosnick Stanford University May, 2017 Author Note David S. Yeager, Department of Psychology, University of Texas at Austin. Jon A. Krosnick, Department of Communications, Political Science and Psychology, Stanford University. The authors are grateful to Sebastian Lundmark for his help programming the questionnaire and coordinating data collection, and to Neil Malhotra, Elliot Tucker-Drob, Paul Hanselman, Charles Judd, Robert Crosnoe, and Teppei Yamamoto for discussions that contributed to these ideas. Hae Yeon Lee conducted analyses for the depression mediation, and Quinn Hirschi collected pilot data. Data included in this manuscript were collected with the support of the National Institute of Child Health and Human Development (Grant No. 10.13039/100000071 R01HD084772-02) and by a William T. Grant Foundation scholars grant. We report how we determined our sample sizes, all data exclusions (if any), all manipulations, and all measures in the studies. Hypotheses were pre-registered prior to data collection (https://osf.io/q3ums/) Address correspondence to David S. Yeager (
[email protected]), 108 E. Dean Keeton Stop A8000, Austin, TX, 78712-1043. Impossible Mediation 2 Abstract In much psychology research, mediators are measured, not manipulated. Therefore, the paths from mediators to outcomes—the so-called b paths—can be confounded by omitted variable bias, as in any other correlational analysis. The present research builds on the logic of falsification tests in econometrics and sensitivity analysis in statistics to propose the impossible mediation test, which can quantify the amount of confounded mediation.