Using a Cross-Classified Multilevel Mediation Model (CC-M3) With Longitudinal Data

• In Event: Statistical Methods for Complex Multilevel Structures and Data Types

Abstract

Cluster memberships associated with the mediation effect are often changed due to the temporal distance between the cause-and-effect variables in longitudinal data. Nevertheless, current practices in multilevel mediation analysis mostly assume a purely hierarchical data structure. A Monte Carlo simulation study is conducted to examine the consequence of ignoring the changes in cluster memberships in multilevel mediation analysis. Results show that the proposed method, Cross-Classified Multilevel Mediation Model (CC-M3), outperforms the conventional multilevel model with substantially smaller relative biases in parameter estimates (about 50% less) and a more consistent and higher coverage rate. Findings of this simulation study inform the empirical researchers that the changes in cluster-membership needs to be appropriately taken into consideration in mediation analysis.

Authors

• Minjung Kim, The Ohio State University

• Christa Winkler, The Ohio State University

• Joshua Peri, The Ohio State University - Columbus

• James Uanhoro, The Ohio State University