When psychological states are mediators: what we can and cannot do

The interest in underlying mechanisms of psychological phenomena promotes the widespread application of mediation models in psychological research. Researchers in psychology usually employ regression-based approaches (such as Baron and Kenny’s criteria, Sobel test and SEM) to explore causal relationships between independent variables, mediators, and outcome variables. However, regression-based approaches are restrictive due to their strong assumptions about the causal relationship between variables and the limitations in commonly used experiment designs. Previous literature has cautioned researchers about this restriction, but misuses of regression-based approaches persist when the assumptions are not satisfied. The reasons for such misuse can be complex. In addition to researchers potentially not paying attention to the assumptions, they may encounter various challenges in assessing the causal relationship between variables, especially when using psychological states as mediators. These challenges include but are not limited to: (1) Psychological states are latent variables; (2) Some psychological processes are nearly instantaneous; (3) Potential unknown confounders; (4) Common method bias; (5) Bad control problem; and (6) Potential causal heterogeneity. This presentation discusses the limitations of regression-based approaches in exploring a causal relationship and analyzes the challenges in assessing a causal mediation relationship with psychological states as mediators. In addition, it introduces several advanced approaches from the counterfactual framework of causality to address these challenges.