[6][7] In doing so, it maximizes the amount of variance explained (though what this means from a statistical point of view is unclear and PLS-PM users do not agree on how this goal might be achieved).
[8] A further related development is factor-based PLS-PM (PLSF), a variation of which employs PLSc-PM as a basis for the estimation of the factors in common factor models; this method significantly increases the number of common factor model parameters that can be estimated, effectively bridging the gap between classic PLS-PM and covariance‐based structural equation modeling.
The measurement models represent the relationships between the observed data and the latent variables.
[12] A recent study suggests that this claim is generally unjustified, and proposes two methods for minimum sample size estimation in PLS-PM.
However, PLS-PM is still considered preferable (over covariance‐based structural equation modeling) when it is unknown whether the data's nature is common factor- or composite-based.