Table 2. Evidence against PLS provided... | Emerald Publishing

Table 2.

Evidence against PLS provided by Y21

Claim in Y21	Conclusion
Indicator weights from PLS Mode B are equivalent to Bartlett factor scores, but only when assuming no cross-loadings or correlated errors across scales, which ML-SEM and Bayesian SEM can accommodate	In real-world analysis situations, we cannot assume cross-loadings or correlated errors are non-existent
	Therefore: PLS should not be used in situation where cross-loadings or correlated errors may exist, as it has no way to account for these features
PLS Mode B composites are most reliable among all weighted averages of observed indicators, for correctly specified models and where all cross-loadings and error correlations are completely channeled through the composite correlation(s)	Even if a model was correct, in real-world finite samples, advantages of differential item weighting are trivial as long as very bad items are first dropped from the data
	Therefore: Differentially weighted composites should be always compared against unit weighted ones using the CEI. Unless meaningful differences are found and can be explained, unit-weighted composites should be chosen for their simplicity
In a situation with two latent variables, PLS Mode B always yields a greater signal to noise ratio than ML-SEM for estimating the regression coefficient between the two latent variables	Y21 confuses effect size with statistical significance. While PLS may lead to higher statistical significance, and thus a greater likelihood of finding and effect, this comes at the expense of a higher chance of false positives. The claim by Y21 does not hold in more realistic models with more than one predictor variable, where the inconsistency of PLS can lead to incorrect conclusions about the existence of an effect
However: “PLS-SEM tends to have an inflated effect size even with normally distributed data …”	Therefore: Results from a PLS analysis are more likely to be false positives than those from other methods such as ML-SEM, ceteris paribus
“PLS-SEM may have inflated type I errors and R-square values even with normally distributed data”
“[PLS] needs a large enough sample size and good quality of data for reliable model/parameter inference (Marcoulides and Saunders, 2006). In particular, samples with heavy-tails or data contamination can strongly affect the goodness of the estimates by the LS method”	PLS should especially not be used with small samples or low quality data

Claim in Y21	Conclusion
Indicator weights from PLS Mode B are equivalent to Bartlett factor scores, but only when assuming no cross-loadings or correlated errors across scales, which ML-SEM and Bayesian SEM can accommodate	In real-world analysis situations, we cannot assume cross-loadings or correlated errors are non-existent
	Therefore: PLS should not be used in situation where cross-loadings or correlated errors may exist, as it has no way to account for these features
PLS Mode B composites are most reliable among all weighted averages of observed indicators, for correctly specified models and where all cross-loadings and error correlations are completely channeled through the composite correlation(s)	Even if a model was correct, in real-world finite samples, advantages of differential item weighting are trivial as long as very bad items are first dropped from the data
	Therefore: Differentially weighted composites should be always compared against unit weighted ones using the CEI. Unless meaningful differences are found and can be explained, unit-weighted composites should be chosen for their simplicity
In a situation with two latent variables, PLS Mode B always yields a greater signal to noise ratio than ML-SEM for estimating the regression coefficient between the two latent variables	Y21 confuses effect size with statistical significance. While PLS may lead to higher statistical significance, and thus a greater likelihood of finding and effect, this comes at the expense of a higher chance of false positives. The claim by Y21 does not hold in more realistic models with more than one predictor variable, where the inconsistency of PLS can lead to incorrect conclusions about the existence of an effect
However: “PLS-SEM tends to have an inflated effect size even with normally distributed data …”	Therefore: Results from a PLS analysis are more likely to be false positives than those from other methods such as ML-SEM, ceteris paribus
“PLS-SEM may have inflated type I errors and R-square values even with normally distributed data”
“[PLS] needs a large enough sample size and good quality of data for reliable model/parameter inference (Marcoulides and Saunders, 2006). In particular, samples with heavy-tails or data contamination can strongly affect the goodness of the estimates by the LS method”	PLS should especially not be used with small samples or low quality data

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