| Description |
1 online resource (xxxvi, 472 pages) : illustrations (some color) |
| Contents |
Intro -- Foreword -- Preface -- Abbreviations -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Part I Basic Concepts and Extensions -- 1 Introduction to the Partial Least Squares Path Modeling: Basic Concepts and Recent Methodological Enhancements -- 1.1 Introduction -- 1.2 Overview of Three Primary SEM Methods -- 1.3 Recent Developments in the PLS-PM/PLS-SEM Method -- 1.4 Essential Emerging PLS-SEM Tools for Social Sciences Scholars -- 1.4.1 Mediation -- 1.4.2 Moderation -- 1.4.3 Moderated Mediation -- 1.4.4 Non-linear SEM Solutions |
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1.4.5 Out-of-Sample Prediction -- 1.5 Observations and Conclusions -- References -- 2 Quantile Composite-Based Path Modeling with R: A Hands-on Guide -- 2.1 Introduction -- 2.2 Quantile Composite-Based Path Modeling in a Nutshell -- 2.3 Data Description -- 2.4 Running QC-PM with R -- 2.4.1 Loading and Pre-processing of the Data -- 2.4.2 Model Specification, Estimation, and Results -- 2.4.3 Model Assessment and Validation -- 2.4.4 Post-processing: Graphs and Result Exporting -- 2.5 Concluding Remarks -- References |
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3 Use of Partial Least Squares Path Modeling Within and Across Business Disciplines -- 3.1 Introduction -- 3.2 Frequency of PLS-PM Use in Financial Times Journals -- 3.3 Rationale for PLS-PM Use in Financial Times Journals -- 3.3.1 Problematic Rationale: Small Sample Size -- 3.3.2 Problematic Rationale: Data Normality -- 3.3.3 Questionable Rationale: Model Complexity -- 3.3.4 Appropriate Rationale: Model Assessment -- 3.4 The Future of PLS-PM Use in Business Disciplines -- 3.5 Conclusions -- References |
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4 Statistical and Psychometric Properties of Three Weighting Schemes of the PLS-SEM Methodology -- 4.1 Introduction -- 4.2 Two Distinctive Features of PLS-SEM and the Environmental Variable -- 4.3 PLS-SEM Modes A and B -- 4.4 PLS-SEM Mode normal upper B Subscript normal upper ABA -- 4.5 Scale Invariance and Scale-Inverse Equivariance -- 4.5.1 Analytical Results -- 4.5.2 Numerical Results -- 4.5.3 Sample Results -- 4.6 Sensitivity of Weights to Misspecified Models -- 4.6.1 PLS-SEM Mode A -- 4.6.2 PLS-SEM Mode B -- 4.6.3 PLS-SEM Mode normal upper B Subscript normal upper ABA |
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4.7 Two Real Data Examples -- 4.8 Conclusion and Discussion -- References -- 5 Software Packages for Partial Least Squares Structural Equation Modeling: An Updated Review -- 5.1 Introduction -- 5.2 Software for PLS-SEM -- 5.2.1 ADANCO -- 5.2.2 SmartPLS -- 5.2.3 WarpPLS -- 5.2.4 XLSTAT-PLSPM -- 5.2.5 plssem -- 5.2.6 cSEM -- 5.2.7 SEMinR -- 5.2.8 Summary of Software Features -- 5.3 Conclusion -- References -- Part II Methodological Issues -- 6 Revisiting and Extending PLS for Ordinal Measurement and Prediction -- 6.1 Introduction -- 6.2 Ordinal (Consistent) Partial Least Squares Path Modeling -- 6.2.1 Calculating Polychoric/Polyserial Correlations |
| Summary |
Now in its second edition, this edited book presents recent progress and techniques in partial least squares path modeling (PLS-PM), and provides a comprehensive overview of the current state-of-the-art in PLS-PM research. Like the previous edition, the book is divided into three parts: the first part emphasizes the basic concepts and extensions of the PLS-PM method; the second part discusses the methodological issues that have been the focus of recent developments, and the last part deals with real-world applications of the PLS-PM method in various disciplines. This new edition broadens the scope of the first edition and consists of entirely new original contributions, again written by expert authors in the field, on a wide range of topics, including: how to perform quantile composite path modeling with R; the rationale and justification for using PLS-PM in top-tier journals; psychometric properties of three weighting schemes and why PLS-PM is a better fit to mode B; a comprehensive review of PLS software; how to perform out-of-sample predictions with ordinal consistent partial least squares; multicollinearity issues in PLS-PM using ridge regression; theorizing and testing specific indirect effects in PLS and considering their effect size; how to run hierarchical models and available approaches; and how to apply necessary condition analysis (NCA) in PLS-PM. This book will appeal to researchers interested in the latest advances in PLS-PM as well as masters and Ph.D. students in a variety of disciplines who use PLS-PM methods. With clear guidelines on selecting and using PLS-PM, especially those related to composite models, readers will be brought up to date on recent debates in the field |
| Bibliography |
Includes bibliographical references and indexes |
| Subject |
Least squares.
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MÃnimos cuadrados
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Least squares
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| Form |
Electronic book
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| Author |
Latan, Hengky
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Hair, Joseph F., Jr., 1944-
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Noonan, Richard B
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| ISBN |
9783031377723 |
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3031377729 |
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