23.2 Partial Least Squared
The difference between PLS and Principal Components Regression is that Principal Components Regression focuses on variance while reducing dimensionality. PLS focuses on covariance while reducing dimensionality.
Partial Least Squared- Structural Equation Modeling vs. Covariance-based Structural Modeling
PLS-SEM vs. CB-SEM
CB-SEM | PLS-SEM | |
---|---|---|
Base Model | Common Factor Model | Composite Factor Model |
McIntosh, Edwards, and Antonakis (2014)
Reflections on Partial least Squares Path Modeling (PLS-PM)
There is still a debate to whether
PLS-PM is a SEM method
PLS-PM can reduce the impact of measurement error: yes (increase reliability)
PLS-PM can validate measurement models
PLS-PM provides valid inference on path coefficients
PLS-PM is better than SEM at handling small sample sizes
PLS-PM can be used for exploratory modeling
Model fit can be based on
global chi-square fit statistic
local chi-square fit statistic
explained variance