A Comparison of Multilevel Mediation Modeling Methods

Recommendations for Applied Researchers

Published in: Multivariate Behavioral Research, Volume 54, Issue 3, pages 338–359 (2019). doi: 10.1080/00273171.2018.1527676

Posted on RAND.org on February 17, 2021

by Christina K. Zigler, Feifei Ye

Read More

Access further information on this document at Multivariate Behavioral Research

This article was published outside of RAND. The full text of the article can be found at the link above.

Multilevel structural equation modeling (MSEM) has been proposed as a valuable tool for estimating mediation in multilevel data and has known advantages over traditional multilevel modeling, including conflated and unconflated techniques (CMM & UMM). Recent methodological research has focused on comparing the three methods for 2-1-1 designs, but in regards to 1-1-1 mediation designs, there are significant gaps in the published literature that prevent applied researchers from making educated decisions regarding which model to employ in their own specific research design. A Monte Carlo study was performed to compare MSEM, UMM, and CMM on relative bias, confidence interval coverage, Type I Error, and power in a 1-1-1 model with random slopes under varying data conditions. Recommendations for applied researchers are discussed and an empirical example provides context for the three methods.

This report is part of the RAND Corporation external publication series. Many RAND studies are published in peer-reviewed scholarly journals, as chapters in commercial books, or as documents published by other organizations.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.