Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses!. (May 2020)

• Record Type: Journal Article
• Authors: Sarstedt, Marko; Hair, Joseph F; Nitzl, Christian; Ringle, Christian M; Howard, Matt C.
• Is Part Of: International journal of market research. Volume 62:Number 3(2020)
• Page Start: 288
• Page End: 299
• Publication Date: 2020-05
• Subjects: conditional process analysis -- measurement error -- mediation analysis -- partial least squares -- PROCESS -- structural equation modeling; Marketing research -- Periodicals; 658.8305
• DOI: 10.1177/1470785320915686
• Languages: English
• ISSNs: 1470-7853

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.