Multilevel Modeling of Individual and Group Level Mediated Effects

TLDR

The study develops a multilevel mediation framework that integrates single‑level mediation procedures with multilevel modeling to test mediational effects in clustered data. They implement multilevel mediation models, compare them to single‑level models through simulations across different variable levels and cluster sizes, and apply the method to data from a high‑school football steroid‑use prevention intervention. Multilevel mediation yields accurate standard errors, outperforming single‑level models—especially with group‑level variables, larger clusters, and higher intraclass correlations—and the real‑world example demonstrates its superior accuracy.

Abstract

This article combines procedures for single-level mediational analysis with multilevel modeling techniques in order to appropriately test mediational effects in clustered data. A simulation study compared the performance of these multilevel mediational models with that of single-level mediational models in clustered data with individual- or group-level initial independent variables, individual- or group-level mediators, and individual level outcomes. The standard errors of mediated effects from the multilevel solution were generally accurate, while those from the single-level procedure were downwardly biased, often by 20% or more. The multilevel advantage was greatest in those situations involving group-level variables, larger group sizes, and higher intraclass correlations in mediator and outcome variables. Multilevel mediational modeling methods were also applied to data from a preventive intervention designed to reduce intentions to use steroids among players on high school football teams. This example illustrates differences between single-level and multilevel mediational modeling in real-world clustered data and shows how the multilevel technique may lead to more accurate results.