December 5, 2025Open Access

PLS-SEM: The Holy Grail for Advanced Analysis

LMLucy Matthews JHJoe Hair RMRyan Matthews

Key Points

Partial least squares enhances structural equation modeling techniques for better data relationships, including mediation.
Key evidence reveals benefits of using PLS-SEM, highlighting its superiority over covariance-based modeling.
Comprehensive guidelines outline appropriate applications of PLS-SEM and its analytical tools in various contexts.
These findings underscore the necessity for researchers to consider PLS-SEM for intricate data analysis strategies.

Abstract

Advanced analytical techniques are reviewed for researchers wanting to expand their knowledge of how partial least squares structural equation modeling (PLS-SEM) facilitates better understanding of complex data relationships. We provide a brief overview of the differences between covariance-based structural equation modeling (CB-SEM) and PLS-SEM along with guidelines for the appropriate application of each. The focus is on mediation, moderation, multi-group analysis, and hierarchical component models. We also summarize several emerging analytical tools available with PLS-SEM. The ease of applying these advanced analytical techniques in many different research contexts makes PLS-SEM the “holy grail” for advanced analysis.

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