Goodness of Fit (GoF)


The goodness of fit (GoF) has been developed as an overall measure of model fit for PLS-SEM. However, as the GoF cannot reliably distinguish valid from invalid models and since its applicability is limited to certain model setups, researchers should avoid its use as a goodness of fit measure. The GoF may be useful for a PLS multigroup analysis (PLS-MGA).


Also see the information on model fit.

"Unlike CB-SEM, PLS-SEM does not optimize a unique global scalar function. The lack of a global scalar function and the consequent lack of global goodness-of-fit measures are traditionally considered major drawbacks of PLS-SEM. When using PLS-SEM, it is important to recognize that the term fit has different meanings in the contexts of CB-SEM and PLS-SEM. Fit statistics for CB-SEM are derived from the discrepancy between the empirical and the model-implied (theoretical) covariance matrix, whereas PLS-SEM focuses on the discrepancy between the observed (in the case of manifest variables) or approximated (in the case of latent variables) values of the dependent variables and the values predicted by the model in question (Hair et al., 2012a). While a global goodness-of-fit measure for PLS-SEM has been proposed (Tenenhaus et al., 2004), research shows that the measure is unsuitable for identifying misspecified models (Henseler and Sarstedt, 2012; see Chapter 6 for a discussion of the measure and its limitations). As a consequence, researchers using PLS-SEM rely on measures indicating the model’s predictive capabilities to judge the model’s quality." (Henseler et al., 2014).

Henseler and Sarstedt (2012) explain in detail that the so global goodness of fit (GoF) for PLS by Tenenhaus et al. (2004) does not represent a fit measure and should not be used as such. However, Henseler and Sarstedt (2012) also show that the GoF may be useful for a PLS multi-group analysis (PLS-MGA) when researchers compare the PLS-SEM results of different data groups for the same PLS path model.



  • Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). [A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)(, 2^nd^ Ed., Sage: Thousand Oaks.

  • Henseler, J., and Sarstedt, M. (2013). Goodness-of-Fit Indices for Partial Least Squares Path Modeling, Computational Statistics, 28(2): 565-580.

  • Tenenhaus, M., Amato, S., and Esposito Vinzi, V. (2004). A Global Goodness-of-Fit Index for PLS Structural Equation Modeling, Proceedings of the XLII SIS Scientific Meeting. Padova: CLEUP, 739-742.

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