Segmentation of PLS path models by iterative reweighted regressions

Uncovering unobserved heterogeneity is a requirement to obtain valid results when using structural equation modeling (SEM). Conventional segmentation methods usually fail in an SEM context because they account for the indicator data, but not for the latent variables and their relationships in the structural model. This research introduces a new segmentation approach to variance-based SEM using partial least squares path modeling (PLS). The iterative reweighted regressions segmentation method for PLS (PLS-IRRS) effectively identifies and treats unobserved heterogeneity in data sets. Compared to existing alternatives, PLS-IRRS is multiple times faster while delivering results of the same quality. Researchers should therefore routinely use PLS-IRRS to address the critical issue of unobserved heterogeneity in PLS.

Subjects

fsQCA

Fuzzy set qualitative comparative analysis

Genetic algorithms

Partial least squares

PLS

PLS-IRRS

Reweighted regressions

Segmentation

DDC Class

000: Allgemeines, Wissenschaft

Options

Segmentation of PLS path models by iterative reweighted regressions