Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses!

TLDR

Mediation and conditional process analyses are popular for examining mechanisms, yet the dual use of regression and SEM is surprising given known limitations of regression with latent variables. The article argues that confusion over SEM’s mediation efficacy stems from a focus on factor-based methods and that tandem SEM and PROCESS use is unnecessary. Researchers typically augment SEM with PROCESS regression analyses to estimate mediation models. Composite-based SEM, particularly PLS‑SEM, overcomes limitations of regression and factor-based SEM, making it the preferred and superior method for mediation and conditional process models and eliminating the need for PROCESS.

Abstract

Mediation and conditional process analyses have become popular approaches for examining the mechanisms by which effects operate and the factors that influence them. To estimate mediation models, researchers often augment their structural equation modeling (SEM) analyses with additional regression analyses using the PROCESS macro. This duality is surprising considering that research has long acknowledged the limitations of regression analyses when estimating models with latent variables. In this article, we argue that much of the confusion regarding SEM’s efficacy for mediation analyses results from a singular focus on factor-based methods, and there is no need for a tandem use of SEM and PROCESS. Specifically, we highlight that composite-based SEM methods overcome the limitations of both regression and factor-based SEM analyses when estimating even highly complex mediation models. We further conclude that composite-based SEM methods such as partial least squares (PLS-SEM) are the preferred and superior approach when estimating mediation and conditional process models, and that the PROCESS approach is not needed when mediation is examined with PLS-SEM.