PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement

  • Vincenzo Esposito Vinzi
  • Laura Trinchera
  • Silvano Amato
Part of the Springer Handbooks of Computational Statistics book series (SHCS)


In this chapter the authors first present the basic algorithm of PLS Path Modeling by discussing some recently proposed estimation options. Namely, they introduce the development of new estimation modes and schemes for multidimensional (formative) constructs, i.e. the use of PLS Regression for formative indicators, and the use of path analysis on latent variable scores to estimate path coefficients Furthermore, they focus on the quality indexes classically used to assess the performance of the model in terms of explained variances. They also present some recent developments in PLS Path Modeling framework for model assessment and improvement, including a non-parametric GoF-based procedure for assessing the statistical significance of path coefficients. Finally, they discuss the REBUS-PLS algorithm that enables to improve the prediction performance of the model by capturing unobserved heterogeneity. The chapter ends with a brief sketch of open issues in the area that, in the Authors’ opinion, currently represent major research challenges.


Latent Variable Local Model Unobserved Heterogeneity Manifest Variable Outer Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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The participation of V. Esposito Vinzi to this research was supported by the Research Center of the ESSEC Business School of Paris. The participation of L. Trinchera to this research was supported by the MIUR (Italian Ministry of Education, University and Research) grant “Multivariate statistical models for the ex-ante and the ex-post analysis of regulatory impact”, coordinated by C. Lauro (2006).


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Vincenzo Esposito Vinzi
    • 1
  • Laura Trinchera
    • 2
  • Silvano Amato
    • 2
  1. 1.Department of Information Systems and Decision SciencesESSEC Business School of ParisCergy-Pontoise, CedexFrance
  2. 2.Dipartimento di Matematica e StatisticaUniversità degli Studi di Napoli “Federico II”NapoliItaly

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