Mediational analysis quantifies proposed causal mechanisms through which treatments act on outcomes. In the presence of clustered data, conventional multiple regression mediational methods break down, requiring the use of hierarchical linear modeling techniques. As an additional consideration, nonlinear relationships in multilevel mediation models require unique specifications that are ignored if modeled linearly. Improper specification of nonlinear relationships can lead to a consistently overestimated mediated effect. This has direct implications for inferences regarding intervention causality and efficacy. The current investigation proposes a nonlinear multilevel mediation model to account for nonlinear relationships in clustered data. A simulation study is proposed to compare the statistical performance of the proposed nonlinear multilevel mediation model with that of conventional methods.