Why generalized structured component analysis is not universally preferable to structural equation modeling

By Jörg Henseler. 2012

156 citations recorded by the source corpus.

Generalized structured component analysis has emerged in marketing and psychometric literature as an alternative to structural equation modeling. A recent simulation study recommends that, in most cases, this analysis is preferable to structural equation modeling because it outperforms the latter when the model is misspecified. This article examines the characteristics of generalized structured component analysis and reveals that the surprising previous findings are attributable to an incomplete experimental design and an error incurred during the software implementation of generalized structured component analysis. Simulated data show that generalized structured component analysis provides inconsistent estimates. In some instances, model misspecification can nearly neutralize this inconsistency, but in others it will reinforce the inconsistency. Moreover, generalized structured component analysis is hardly suitable for mediation analysis because it substantially overestimates the direct effect. Thus, generalized structured component analysis cannot be recommended universally over structural equation modeling.

Subfields: Food Science, Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/06eb7c2999a19b0f75d75d9cd6472a57ab33d1ee

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Citations in the corpus

Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling (2017, background): “As a consequence, researchers who use composites as stand-ins for latent variables will obtain inconsistent model coefficients and risk inflated Type I and Type II errors (Henseler 2012).”
On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012) (2014, background): “Overall, it seems reasonable to conclude that while CB-SEM is traditionally viewed as a confirmatory tool, the contrary is actually true in practice.4 2 The same holds for the FIT, and the adjusted FIT indices (Hwang et al., 2010) of generalized structured component analysis (Henseler, 2012).”
Using PLS path modeling in new technology research: updated guidelines (2016, methodology): “…1 by alternative indicator weight generators, such as principal component analysis (Tenenhaus, 2008), generalized structured component analysis (Hwang and Takane, 2004; Henseler, 2012), regularized generalized canonical correlation analysis (Tenenhaus and Tenenhaus, 2011), or even plain sum scores.”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, methodology): “…extensive body of literature provides the technical underpinnings of PLS (e.g., Lohmöller 1989; Tenenhaus et al. 2005;Wold 1982) and GSCA (e.g., Henseler 2012; Hwang 2009; Hwang Table 1 Comparison between factor-based SEM and PLS-SEM Criterion Factor-based SEM PLS Sample references Model…”
Common Beliefs and Reality About PLS (2014, background): “If things were really this way, R&E would be justified because model misspecification cannot be corrected by choosing one estimation technique over another (Henseler, 2012).”
How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research (2020, result): “Similar findings observed for GSCA are applicable to PLS-PM [92].”
Testing measurement invariance of composites using partial least squares (2016, methodology): “Building on recent literature on the nature of composite models generally (Henseler, 2012; Hwang and Takane, 2004) and PLS specifically (Henseler et al.”
PLS-SEM: Looking Back and Moving Forward (2014, background): “So far, variance-based SEMmethods are unable to consistently estimate factor models (Henseler, 2012; McDonald, 1996), and covariance-based SEM methods have difficulties modeling endogenous composites (Rigdon, 2013).”
The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling (2017, methodology): “The latter involves different techniques such as regression on summed scale (Tenenhaus, 2008), generalized structured component analysis based structural equation modeling (GSCA-SEM) (Henseler, 2012; Hwang & Takane, 2004), and partial least square path modeling (PLS-PM) (Wold, 1982; Henseler, Ringle”
Confirmatory composite analysis using partial least squares: setting the record straight (2020, unknown): “2017; Henseler 2012).”