Consistent PLS
Abstract
The consistent PLS (PLSc) algorithm performs a correction of reflective constructs' correlations to make results consistent with a factormodel.
Description
The consistent PLS (PLSc) algorithm performs a correction of reflective constructs' correlations to make results consistent with a factormodel (Dijkstra 2010; Dijkstra 2014; Dijkstra and Henseler 2015; Dijkstra and SchermellehEngel 2014). In principle, the correction builds on Nunnally’s (1978) wellknown correction for attenuation formula.
PLSc Settings in SmartPLS
Initial Calculations: One can decide whether the initial PLS path model should be taken as it is or if all LVs should be connected to generate latent variable scores when running the PLS algorithm. Dijkstra and Henseler (2012) advise to use connections between all LVs for the estimation of the latent variables scores to get more stable results.
Until now, no additional parameter specifications are required for the correction that PLSc performs on the results of the basic PLS algorithm. However, parameter settings for the underlying PLS algorithm are important and should be checked.
Links
 PLSpredict
 Confirmatory Composite Analysis (CCA)
 Predictionoriented Model Comparison
 Consistent Bootstrapping
 PLS and Bootstrapping Problems
 Confirmatory Composite Analysis (CCA)
References

Dijkstra, T. K. (2010). Latent Variables and Indices: Herman Wold’s Basic Design and Partial Least Squares, in Handbook of Partial Least Squares: Concepts, Methods and Applications (Springer Handbooks of Computational Statistics Series, vol. II), V. Esposito Vinzi, W. W. Chin, J. Henseler and H. Wang (eds.), Springer: Heidelberg, Dordrecht, London, New York, pp. 2346.

Dijkstra, T. K. (2014). PLS' Janus Face – Response to Professor Rigdon's ‘Rethinking Partial Least Squares Modeling: In Praise of Simple Methods’, Long Range Planning, 47(3): 146153.

Dijkstra, T. K., and Henseler, J. (2015). Consistent Partial Least Squares Path Modeling, MIS Quarterly, 39(2): 297316.

Dijkstra, T. K., and SchermellehEngel, K. (2014). Consistent Partial Least Squares for Nonlinear Structural Equation Models, Psychometrika, 79(4): 585604.
 Nunnally, J. C. (1978). Psychometric Theory, McGraw Hill: New York.