Segmentation of PLS path models by iterative reweighted regressions

Rainer Schlittgen, Marko Sarstedt, Christian M. Ringle, Jan-Michael Becker

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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 variancebased 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.
Original languageEnglish
Title of host publicationProceedings of the 2nd International Symposium on Partial Least Squares Path Modeling
Subtitle of host publicationThe Conference for PLS Users
EditorsJörg Henseler, Christian Ringle, José Roldán, Gabriel Cepeda
Place of PublicationEnschede
PublisherUniversity of Twente
Number of pages12
ISBN (Print)9789036540568
Publication statusPublished - 2015
Externally publishedYes
Event2015 PLS User Conference: 2nd International Symposium on Partial Least Squares Path Modeling - The Conference for PLS Users - Seville, Spain
Duration: 16 Jun 201519 Jun 2015


Conference2015 PLS User Conference


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