The consistent PLS-SEM (PLSc-SEM) algorithm performs a correction of reflective constructs' correlations to make results consistent with a factor-model.
The consistent PLS-SEM (PLSc-SEM) algorithm performs a correction of reflective constructs' correlations to make results consistent with a factor-model (Dijkstra 2010; Dijkstra 2014; Dijkstra and Henseler 2015; Dijkstra and Schermelleh-Engel 2014). In principle, the correction builds on Nunnally’s (1978) well-known 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-SEM 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-SEM performs on the results of the basic PLS-SEM algorithm. However, parameter settings for the underlying PLS-SEM algorithm are important and should be checked.
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. 23-46.
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): 146-153.
Dijkstra, T. K., and Henseler, J. (2015). Consistent Partial Least Squares Path Modeling, MIS Quarterly, 39(2): 297-316.
Dijkstra, T. K., and Schermelleh-Engel, K. (2014). Consistent Partial Least Squares for Nonlinear Structural Equation Models, Psychometrika, 79(4): 585-604.
Nunnally, J. C. (1978). Psychometric Theory, McGraw Hill: New York.