Partial Least Squares is an Estimator for Structural Equation Models: A Comment on Evermann and Ronkko (2021)

In 2012 and 2013, several critical publications questioned many alleged PLS properties. As a consequence, PLS benefited from a boost of developments. It is, therefore, a good time to review these developments. Evermann and Ronkko (2023) devote their paper to this task and formulate guidelines in the form of 14 recommendations. Yet, while they identified the major developments, they overlook a fundamental change, maybe because it is so subtle: the view on PLS. As mentioned by Evermann and Ronkko (2023, p. 1), [PLS] is a statistical method used to estimate linear structural equation models and consequently should not be regarded as a standalone SEM technique following its own assessment criteria. Against this background, we explain which models can be estimated by PLS and PLSc. Moreover, we present the Henseler-Ogasawara specification to estimate composite models by common SEM estimators. Additionally, we review Evermann and Ronkko's (2023) 14 recommendations one by one and suggest updates and improvements where necessary. Further, we address their comments about the latest advancement in composite models and show that PLS is a viable estimator for confirmatory composite analysis. Finally, we conclude that there is little value in distinguishing between covariance-based and variance-based SEM-there is only SEM.

Automated summary

In 2012 and 2013, critical publications questioned the alleged properties of PLS, leading to developments and a need for a review. Evermann and Ronkko (2023) provide guidelines in the form of 14 recommendations, but they overlook the subtle change in the view on PLS. We explain which models can be estimated by PLS and PLSc, and present the Henseler-Ogasawara specification for estimating composite models by common SEM estimators. Additionally, we review and suggest updates to Evermann and Ronkko's (2023) recommendations.