Repositorio de producción científica de la Universidad de Sevilla

Segmentation of PLS-Path Models by Iterative Reweighted Regressions

Opened Access Segmentation of PLS-Path Models by Iterative Reweighted Regressions

Citas

buscar en

Estadísticas
Icon
Exportar a
Autor: Schlittgen, Rainier
Sarstedt, Marko
Ringle, Christian M.
Becker, Jan-Michael
Fecha: 2015
ISBN/ISSN: 9789036540568
Tipo de documento: Ponencia
Resumen: 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
Tamaño: 736.5Kb
Formato: PDF

URI: http://hdl.handle.net/11441/55851

DOI: 10.3990/2.344

Mostrar el registro completo del ítem


Esta obra está bajo una Licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional

Este registro aparece en las siguientes colecciones