New Estimation Procedures for PLS Path Modelling

TL;DR

This paper introduces new estimation procedures for PLS Path Modelling that improve the estimation of latent variables and model coefficients, especially in the presence of complex group structures and interactions.

Contribution

It proposes novel external and internal estimation schemes that enhance PLS Path Modelling's flexibility and effectiveness in handling variable group structures and interactions.

Findings

New external schemes better capture strong group structures.

Internal schemes improve handling of variable complementarity and interactions.

Application examples demonstrate practical benefits.

Abstract

Given R groups of numerical variables X1, ... XR, we assume that each group is the result of one underlying latent variable, and that all latent variables are bound together through a linear equation system. Moreover, we assume that some explanatory latent variables may interact pairwise in one or more equations. We basically consider PLS Path Modelling's algorithm to estimate both latent variables and the model's coefficients. New "external" estimation schemes are proposed that draw latent variables towards strong group structures in a more flexible way. New "internal" estimation schemes are proposed to enable PLSPM to make good use of variable group complementarity and to deal with interactions. Application examples are given.