Using indicator correlation fit indices in PLS-SEM: Selecting the algorithm with the best fit

The article below explains how one can use indicator correlation fit indices for selecting the analysis algorithm with the best fit in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2020). Using indicator correlation fit indices in PLS-SEM: Selecting the algorithm with the best fit. Data Analysis Perspectives Journal, 1(4), 1-4.

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Abstract:

Upon completion of a PLS-SEM analysis, one can obtain the model-implied indicator correlation matrix and compare it with the actual indicator correlation matrix. The latter is obtained directly from the data being analyzed. Indicator correlation fit indices are quantifications of the differences among these two matrices. Our focus in this paper is on the use of indicator correlation fit indices in PLS-SEM for selecting the analysis algorithm with the best fit.