Goodness-of-fit indices for partial least squares path modeling

By Jörg Henseler, Marko Sarstedt. Computational statistics (Zeitschrift), 2013

1,801 citations recorded by the source corpus.

This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoFrel), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoFrel are not suitable for model validation. However, the GoF can be useful to assess how well a PLS path model can explain different sets of data.

Subfields: Management Science and Operations Research, Transportation

View Paper: https://www.semanticscholar.org/paper/c19ee34340beab30b65f6afb43a798ddbb8c835b

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In Management Science and Operations Research

In Transportation

Citations in the corpus

Partial least squares structural equation modeling in HRM research (2020, background): “%) report Tenenhaus et al.’s (2005) goodness-of-fit index, which, however, offers no indication of the model fit (Henseler & Sarstedt, 2013).”
Partial least squares path modeling: Time for some serious second thoughts (2016, methodology): “Other authors suggest the weights are optimal (e.g., Henseler and Sarstedt, 2013, p. 566), but do not explain why and for which specific purpose.”
Re-examining online customer experience to include purchase frequency and perceived risk (2015, methodology): “The concept of model fit in PLS-SEM differs from that of CB-SEM, as no universal fit statistic exists (Henseler and Sarstedt, 2013; Wilden et al., 2013).”