Handbook of Partial Least Squares : Concepts, Methods and Applications
This handbook provides a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to structural equation modeling applications, software and interpretation of results. The handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology
eBook,English, 2010
Springer Berlin Heidelberg, Berlin, Heidelberg, 2010
1 online resource (790 pages)
9783540328254, 3540328254
990473184
Handbook of Partial Least Squares; Editorial: Perspectives on Partial Least Squares; 1 Partial Least Squares: A Success Story; 2 The Handbook in a Nutshell; 2.1 Part I: Methods of Partial Least Squares; 2.1.1 PLS Path Modeling: Concepts, Model Estimation, and Assessment; 2.1.2 PLS Path Modeling: Extensions; 2.1.3 PLS Path Modeling with Classification Issues; 2.1.4 PLS Path Modeling for Customer Satisfaction Studies; 2.1.5 PLS Regression; 2.2 Part II: Applications to Marketing and Related Areas; 2.3 Part III: Tutorials; References; Part I Methods. PLS Path Modeling: Concepts, Model Estimation and Assessment1 Latent Variables and Indices: Herman Wold's Basic Design and Partial Least Squares; 1.1 Introduction; 1.2 A Second Order Factor Model, the ``Basic Design''; 1.3 Distributional Assumptions: Multinormality or ``Distribution Free''?; 1.4 On the PLS-Algorithms: Convergence Issues and Functional Properties of Fixed Points; 1.5 Correlations, Structural Parameters, Loadings; 1.6 Two Suggestions for Further Research; 1.6.1 Proper Indices; 1.6.2 Potentially Useful Constraints; 1.7 Conclusion; References. 2 PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement2.1 Introduction; 2.2 PLS Path Modeling: Basic Algorithm and Quality Indexes; 2.2.1 The Algorithm; 2.2.2 The Quality Indexes; 2.3 Prediction-Based Model Assessment; 2.3.1 Hypothesis Testing on One Path Coefficient; 2.3.2 Hypothesis Testing on the Whole Set of Path Coefficients; 2.3.3 Application to Simulated Data; 2.3.3.1 Simulation Scheme; 2.3.3.2 Results; 2.4 Heterogeneity in PLS Path Modeling; 2.4.1 The REBUS-PLS Algorithm; 2.4.2 Application to Real Data. 2.5 Conclusion and PerspectivesReferences; PLS Path Modeling: Extensions; 3 Bootstrap Cross-Validation Indices for PLS Path Model Assessment; 3.1 Introduction; 3.2 General Procedure; 3.3 Study 1; 3.4 Study 2; 3.5 Discussion and Conclusion; References; 4 A Bridge Between PLS Path Modeling and Multi-Block Data Analysis; 4.1 A PLS Path Modeling Approach to Confirmatory Factor Analysis; 4.1.1 External Estimation; 4.1.2 Internal Estimation; 4.1.3 Computation of the Vector of Weights wj Using Mode A or Mode B Options; 4.1.4 Some Considerations on the Criteria; 4.2 The Hierarchical PLS Path Model. 4.2.1 Use of Mode A with the Path-Weighting Scheme4.2.2 Use of Mode B with Centroid and Factorial Schemes; 4.3 Multi-block Analysis Methods and PLS Path Modeling; 4.4 Application to Sensory Data; 4.4.1 Data Description; 4.4.2 Principal Component Analysis of Each Block; 4.4.3 PLS Confirmatory Factor Analysis; 4.4.3.1 Study of Dimension 1; 4.4.3.2 Study of Dimension 2; 4.4.4 Use of the MAXDIFF/MAXBET Algorithms; 4.4.5 Use of Hierarchical PLS Path Model; 4.5 Conclusion; References
5 Use of ULS-SEM and PLS-SEM to Measure a Group Effect in a Regression Model Relating Two Blocks of Binary Variables
English