The impact of ignoring random slopes in a 1 → 1 → 1 mediation model in the presence of upper-level mediator-outcome confounding

In behavioral sciences, researchers frequently employ mediation analysis with longitudinal data. A common scenario involves the 1 → 1 → 1 mediation model, where the Predictor X, Mediator(s) M, and Outcome Y are all measured at different occasions (Level 1) within individuals (Level 2). The standard 1 → 1 → 1 mediation model approach fits two multilevel models, one for the mediator and one for the outcome, with two random intercepts and three random slopes in total. However researchers often exclude random slopes from multilevel models and only include random intercepts to account for non-independence across observations of the same individual. We demonstrate that ignoring random slopes in the 1 → 1 → 1 mediation model can result in biased average indirect effect estimators, as well as underestimated standard errors. We provide code from open source and free statistical software that can be used by practitioners to fit the 1 → 1 → 1 mediation model.