Sensitivity Plots for Confounder Bias in the Single Mediator Model

Matthew G. Cox, Yasemin Kisbu-Sakarya, Milica MioÄ ević and David P. MacKinnon

Evaluation Review, 2013, vol. 37, issue 5, 405-431

Abstract: Background: Causal inference continues to be a critical aspect of evaluation research. Recent research in causal inference for statistical mediation has focused on addressing the sequential ignorability assumption; specifically, that there is no confounding between the mediator and the outcome variable. Objectives: This article compares and contrasts three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods. Design: Two types of data were used to fully examine the plots for sensitivity analysis. The first type was generated data from a single mediator model with a confounder influencing both the mediator and the outcome variable. The second was data from an actual intervention study. With both types of data, situations are examined where confounding has a large effect and a small effect. Subjects: The nonsimulated data were from a large intervention study to decrease intentions to use steroids among high school football players. We demonstrate one situation where confounding is likely and another situation where confounding is unlikely. Conclusions: We discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods.

Keywords: mediation; indirect effects; causal inference; confounder bias; sensitivity analysis (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:37:y:2013:i:5:p:405-431

DOI: 10.1177/0193841X14524576

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