Meaningful Mediation Analysis: Plausible Causal Inference and Informative Communication

Statistical mediation analysis has become the technique of choice in consumer research to make causal inferences about the influence of a treatment on an outcome via one or more mediators. This tutorial aims to strengthen two weak links that impede statistical mediation analysis from reaching its full potential. The first weak link is the path from mediator to outcome, which is a correlation. Six conditions are described that this correlation needs to meet in order to make plausible causal inferences: directionality, reliability, unconfoundedness, distinctiveness, power, and mediation. Recommendations are made to increase the plausibility of causal inferences based on statistical mediation analysis. Sweetspot analysis is proposed to establish whether an observed mediator-outcome correlation falls within the region of statistically meaningful correlations. The second weak link is the communication of mediation results. Four components of informative communication of mediation analysis are described: effect decomposition, effect size, difference testing, and data sharing. Recommendations are made to improve the communication of mediation analysis. A review of 166 recently published mediation analyses in the Journal of Consumer Research, a reanalysis of two published datasets, and Monte Carlo simulations support the conclusions and recommendations.