Correlation-preserving mean plausible values as a basis for prediction in the context of Bayesian structural equation modeling

TL;DR

This paper introduces correlation-preserving mean plausible values for Bayesian structural equation modeling, enabling predictions that maintain inter-factor correlations and align with estimated path coefficients.

Contribution

It proposes a novel method for computing mean plausible values that preserve correlations, improving prediction accuracy in BSEM.

Findings

Correlation-preserving plausible values maintain inter-factor correlations.

The method aligns predictions with estimated path coefficients.

An example and syntax for computation are provided.

Abstract

Mean plausible values can be computed when Bayesian structural equation modeling (BSEM) is performed. As mean plausible values do not preserve the inter-factor correlations, they yield path coefficients that are different from the estimated path coefficients of the model. As it might be of interest to perform exactly the same prediction on the level of plausible values that has been estimated by BSEM, correlation-preserving mean plausible values were proposed. An example for the computation of the correlation preserving mean plausible values is given and the corresponding syntax is given in the Appendix.