On the adoption of partial least squares in psychological research: Caveat emptor

By Mikko Rönkkö, C. Mcintosh, John Antonakis. 2015

116 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research, Social Psychology

View Paper: https://www.semanticscholar.org/paper/3853debc7787a5cff94afa35481cc345c32a7e15

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

In Management Science and Operations Research

In Social Psychology

Citations in the corpus

Estimation issues with PLS and CBSEM: Where the bias lies! ☆ (2016, background): “426), that its use is restricted to few domains (Rönkkö et al., 2016; Rönkkö et al., 2015) and that “PLS is not useful for statistical estimation and testing” (Rönkkö et al.”
How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research (2020, background): “, the model parameters cannot be uniquely retrieved from the population indicator variance–covariance matrix [71,72].”
An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research (2017, methodology): “, 2016; Rönkkö and Evermann, 2013) argue that it is not a (factor-based) latent variable method, lacks goodnessof-fit measures and produces biased parameter estimates (Rönkkö et al., 2016; Rönkkö and Evermann, 2013; Rönkkö et al., 2015).”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, methodology): “…continue to adhere to the reflex-like application of factor model populations to judge the relative performance of PLS (e.g., McIntosh et al. 2014; Rönkkö et al. 2015) and other composite-based techniques, such as GSCA, and sum scores regression (e.g., Rönkkö and Evermann 2013; Rönkkö et al.…”
Factors versus Composites: Guidelines for Choosing the Right Structural Equation Modeling Method (2019, methodology): “To analyze their data, some researchers might be tempted to run a series of regressions using sum or mean scores of a set of observed variables measuring some conceptual variable as the input (Rönkkö et al., 2015).”
Partial least squares path modeling: Time for some serious second thoughts (2016, methodology): “However, as originally presented by Herman Wold, PLS was not intended to impose constraints on the data, making the technique incompatible with the idea of model testing (Dijkstra, 2014; see R€onkk€o et al., 2015).”
Covariance-Based Structural Equation Modeling in the Journal of Advertising: Review and Recommendations (2017, background): “Statistically, the structure matches the overidentification that results from constraints corresponding to the theory (Ronkko, McIntosh, and Antonakis 2015).”