Segmentation of PLS-Path Models by Iterative Reweighted Regressions

Abstract

Uncovering unobserved heterogeneity is a requirement to obtain valid results when using the structural equation modeling (SEM) method with empirical data. Conventional segmentation methods usually fail in SEM since they account for the observations but not the latent variables and their relationships in the structural model. This research introduces a new segmentation approach to variance-based SEM. The iterative reweighted regressions segmentation method for PLS (PLS-IRRS) effectively identifies segments in data sets. In comparison with existing alternatives, PLS-IRRS is multiple times faster while delivering the same quality of results. We believe that PLS-IRRS has the potential to become one of the primary choices to address the critical issue of unobserved heterogeneity in PLS-SEM