Handbook of Partial Least Squares

1. Front Matter

   Pages I-XIV

   PDF

2. Editorial: Perspectives on Partial Least Squares

   • Vincenzo Esposito Vinzi, Wynne W. Chin, Jörg Henseler, Huiwen Wang

   Pages 1-20

3. Methods

   1. Front Matter

      Pages 22-22

      PDF

4. METHODS

   1. Latent Variables and Indices: Herman Wold’s Basic Design and Partial Least Squares

      • Theo K. Dijkstra

      Pages 23-46

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

      • Vincenzo Esposito Vinzi, Laura Trinchera, Silvano Amato

      Pages 47-82

   3. Bootstrap Cross-Validation Indices for PLS Path Model Assessment

      • Wynne W. Chin

      Pages 83-97

   4. A Bridge Between PLS Path Modeling and Multi-Block Data Analysis

      • Michel Tenenhaus, Mohamed Hanafi

      Pages 99-123

   5. Use of ULS-SEM and PLS-SEM to Measure a Group Effect in a Regression Model Relating Two Blocks of Binary Variables

      • Michel Tenenhaus, Emmanuelle Mauger, Christiane Guinot

      Pages 125-140

   6. A New Multiblock PLS Based Method to Estimate Causal Models: Application to the Post-Consumption Behavior in Tourism

      • Francisco Arteaga, Martina G. Gallarza, Irene Gil

      Pages 141-169

   7. An Introduction to a Permutation Based Procedure for Multi-Group PLS Analysis: Results of Tests of Differences on Simulated Data and a Cross Cultural Analysis of the Sourcing of Information System Services Between Germany and the USA

      • Wynne W. Chin, Jens Dibbern

      Pages 171-193

   8. Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples

      • Christian M. Ringle, Sven Wende, Alexander Will

      Pages 195-218

   9. Prediction Oriented Classification in PLS Path Modeling

      • Silvia Squillacciotti

      Pages 219-233

   10. Conjoint Use of Variables Clustering and PLS Structural Equations Modeling

       • Valentina Stan, Gilbert Saporta

       Pages 235-246

   11. Design of PLS-Based Satisfaction Studies

       • Kai Kristensen, Jacob Eskildsen

       Pages 247-277

   12. A Case Study of a Customer Satisfaction Problem: Bootstrap and Imputation Techniques

       • Clara Cordeiro, Alexandra Machás, Maria Manuela Neves

       Pages 279-287

   13. Comparison of Likelihood and PLS Estimators for Structural Equation Modeling: A Simulation with Customer Satisfaction Data

       • Manuel J. Vilares, Maria H. Almeida, Pedro S. Coelho

       Pages 289-305

   14. Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares

       • John Hulland, Michael J. Ryan, Robert K. Rayner

       Pages 307-325

   15. PLS in Data Mining and Data Integration

       • Svante Wold, Lennart Eriksson, Nouna Kettaneh

       Pages 327-357

   16. Three-Block Data Modeling by Endo- and Exo-LPLS Regression

       • Solve Sæbø, Magni Martens, Harald Martens

       Pages 359-379

   17. Regression Modelling Analysis on Compositional Data

       • Huiwen Wang, Jie Meng, Michel Tenenhaus

       Pages 381-406

Partial Least Squares is a family of regression based methods designed for the an- ysis of high dimensional data in a low-structure environment. Its origin lies in the sixties, seventies and eighties of the previous century, when Herman O. A. Wold vigorously pursued the creation and construction of models and methods for the social sciences, where “soft models and soft data” were the rule rather than the exception, and where approaches strongly oriented at prediction would be of great value. Theauthorwasfortunatetowitnessthedevelopment rsthandforafewyears. Herman Wold suggested (in 1977) to write a PhD-thesis on LISREL versus PLS in the context of latent variable models, more speci cally of “the basic design”. I was invited to his research team at the Wharton School, Philadelphia, in the fall of 1977. Herman Wold also honoured me by serving on my PhD-committee as a distinguished and decisive member. The thesis was nished in 1981. While I moved into another direction (speci cation, estimation and statistical inference in the c- text of model uncertainty) PLS sprouted very fruitfully in many directions, not only as regards theoretical extensions and innovations (multilevel, nonlinear extensions et cetera) but also as regards applications, notably in chemometrics, marketing, and political sciences. The PLS regression oriented methodology became part of main stream statistical analysis, as can be gathered from references and discussions in important books and journals. See e. g. Hastie et al. (2001), or Stone and Brooks (1990),Frank and Friedman (1993),Tenenhauset al. (2005),there are manyothers.

• Component-based Structural Equation Models
• Estimator
• Latent Variables
• Marketing Research
• PLS
• Partial Least Squares
• Regression
• clustering
• data analysis
• structural equation modeling

From the reviews:

“I found the book to be a good resource for those without prior knowledge of PLS looking forward to an introduction as well as a comprehensive reference for every researcher and every practitioner interested in the most recent advances in PLS methodology. All of them should have this title as an essential part of their library. … Great Stuff! … will be the definitive reference work in the field for a good time to come. … it is wonderfully readable and referable.” (Current Engineering Practice, August, 2011)

“The book is divided into three parts, dealing, respectively, with contemporary methodological developments, applications in marketing and related areas, and tutorials on particular aspects of PLS analysis. It provides a comprehensive overview of recent advances in the area, and describes cutting-edge methodological developments showing how PLS can be applied in tackling a wide variety of problem types. … for people who do have some prior experience with PLS, this book is a goldmine of novel methodological and practical applications.” (David J. Hand, International Statistical Review, Vol. 80 (3), 2012)

• Dept. Inf. Systems & Decision Sciences, ESSEC Business School of Paris, Cergy Pontoise CX, France

  Vincenzo Esposito Vinzi

• Bauer College of Business, University of Houston, Houston, U.S.A.

  Wynne W. Chin

• Nijmegen School of Management, Radboud University, Nijmegen, Netherlands

  Jörg Henseler

• School of Economics & Management, BeiHang University, Beijing, China, People's Republic

  Huiwen Wang

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