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Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments

Vincenzo Esposito Vinzi (), Christian Ringle, Silvia Squillacciotti () and Laura Trinchera
Additional contact information
Silvia Squillacciotti: Electricité de France, Research & Development, Postal: 1 Avenue du Général de Gaulle, 92141 CLAMART, FRANCE, http://www.edf.fr
Laura Trinchera: University of Naples, Federico II, Department of Mathematics and Statistics, Postal: Via Cintia 27 , Complesso Monte Sant'Angelo, 80126 NAPLES, ITALY, http://www.unina.it

No DR 07019, ESSEC Working Papers from ESSEC Research Center, ESSEC Business School

Abstract: Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies.

Keywords: Partial Least Squares; Path Modeling; Unobserved Heterogeneity (search for similar items in EconPapers)
JEL-codes: C39 C49 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2007-07
New Economics Papers: this item is included in nep-cmp
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