Using a Cross-Classified Multilevel Mediation Model (CC-M3) with longitudinal data having changes in cluster membership.
Kim, M., Winkler, C., Uanhoro, J., Peri, J. & Lochman, J. (2021). Using a Cross-Classified Multilevel Mediation Model (CC-M3) with longitudinal data having changes in cluster membership. Structural Equation Modeling: A Multidisciplinary Journal. Advance online publication. https://doi.org/10.1080/10705511.2021.1965886
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. We demonstrate the use of CC-M3 in the applied example.