Assessing the overall fit of composite models estimated by partial least squares path modeling

Florian Schuberth, Manuel E. Rademaker, Jörg Henseler

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
12 Downloads (Pure)


This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Research limitations/implications
Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

Practical implications
To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.
Original languageEnglish
Number of pages25
JournalEuropean Journal Of Marketing
Publication statusE-pub ahead of print - 13 Apr 2022


  • Partial least squares path modeling
  • Composite models
  • Emergent variables
  • Goodness of fit
  • Model fit assessment


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