Common structural variation reduction in PLS-SEM: Replacement analytic composites and the one fourth rule

The article below explains how one can accomplish a common structural variation reduction, via replacement analytic composites and the one fourth rule, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2021). Common structural variation reduction in PLS-SEM: Replacement analytic composites and the one fourth rule. Data Analysis Perspectives Journal, 2(5), 1-6.

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Abstract:

Path coefficients may be distorted, in the context of structural equation modeling via partial least squares (PLS-SEM), due to excess common structural variation shared in a model. This may be caused by methodological issues; e.g., the use of highly correlated but conceptually distinct latent variables, or common method bias. We discuss a common structural variation reduction procedure using WarpPLS, a leading PLS-SEM software tool. This procedure relies on the creation of analytic composites as replacements for latent variables, where the weights are one fourth of the original path coefficients among the latent variables and their predictors in the structural model, and with signs that are the opposites of the signs of the original path coefficients.

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