Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R : : A Workbook.

• Superior document: Classroom Companion: Business Series
• Author: Hair Jr., Joseph F.
• TeilnehmendeR: Hult, G. Tomas M.; Ringle, Christian M.; Sarstedt, Marko.; Danks, Nicholas P.; Ray, Soumya.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; ©2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Series: Classroom Companion: Business Series
• Physical Description: 1 online resource (208 pages)

Table of Contents

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R -- Preface -- References -- Contents -- About the Authors -- 1: An Introduction to Structural Equation Modeling -- 1.1 What Is Structural Equation Modeling? -- 1.2 Principles of Structural Equation Modeling -- 1.2.1 Path Models with Latent Variables -- 1.2.2 Testing Theoretical Relationships -- 1.2.3 Measurement Theory -- 1.2.4 Structural Theory -- 1.3 PLS-SEM and CB-SEM -- 1.4 Considerations When Applying PLS-SEM -- 1.4.1 Key Characteristics of the PLS-SEM Method -- 1.4.2 Data Characteristics -- 1.4.2.1 Minimum Sample Size Requirements -- 1.4.2.2 Missing Value Treatment -- 1.4.2.3 Non-normal Data -- 1.4.2.4 Scales of Measurement -- 1.4.2.5 Secondary Data -- 1.4.3 Model Characteristics -- 1.5 Guidelines for Choosing Between PLS-SEM and CB-SEM -- References -- Suggested Readings -- 2: Overview of R and RStudio -- 2.1 Introduction -- 2.2 Explaining Our Syntax -- 2.3 Computational Statistics Using Programming -- 2.4 Introducing R and RStudio -- 2.4.1 Installing R and RStudio -- 2.4.2 Layout of RStudio -- 2.5 Organizing Your Projects -- 2.6 Packages -- 2.7 Writing R Scripts -- 2.8 How to Find Help in RStudio -- References -- Suggested Readings -- 3: The SEMinR Package -- 3.1 The Corporate Reputation Model -- 3.2 Loading and Cleaning the Data -- 3.3 Specifying the Measurement Models -- 3.4 Specifying the Structural Model -- 3.5 Estimating the Model -- 3.6 Summarizing the Model -- 3.7 Bootstrapping the Model -- 3.8 Plotting, Printing, and Exporting Results to Articles -- References -- Suggested Reading -- 4: Evaluation of Reflective Measurement Models -- 4.1 Introduction -- 4.2 Indicator Reliability -- 4.3 Internal Consistency Reliability -- 4.4 Convergent Validity -- 4.5 Discriminant Validity.

4.6 Case Study Illustration: Reflective Measurement Models -- References -- Suggested Reading -- 5: Evaluation of Formative Measurement Models -- 5.1 Convergent Validity -- 5.2 Indicator Collinearity -- 5.3 Statistical Significance and Relevance of the Indicator Weights -- Excurse -- 5.4 Case Study Illustration: Formative Measurement Models -- 5.4.1 Model Setup and Estimation -- Excurse -- 5.4.2 Reflective Measurement Model Evaluation -- 5.4.3 Formative Measurement Model Evaluation -- References -- Suggested Reading -- 6: Evaluation of the Structural Model -- 6.1 Assess Collinearity Issues of the Structural Model -- 6.2 Assess the Significance and Relevance of the Structural Model Relationships -- 6.3 Assess the Model's Explanatory Power -- 6.4 Assess the Model's Predictive Power -- 6.5 Model Comparisons -- 6.6 Case Study Illustration: Structural Model Evaluation -- Excurse -- Excurse -- References -- Suggested Reading -- 7: Mediation Analysis -- 7.1 Introduction -- 7.2 Systematic Mediation Analysis -- 7.2.1 Evaluation of the Mediation Model -- 7.2.2 Characterization of Outcomes -- 7.2.3 Testing Mediating Effects -- 7.3 Multiple Mediation Models -- 7.4 Case Study Illustration: Mediation Analysis -- References -- Suggested Reading -- 8: Moderation Analysis -- 8.1 Introduction -- 8.2 Types of Moderator Variables -- 8.3 Modeling Moderating Effects -- 8.4 Creating the Interaction Term -- 8.5 Model Evaluation -- 8.6 Result Interpretation -- 8.7 Case Study Illustration: Moderation Analysis -- References -- Suggested Reading -- Appendix A: The PLS-SEM Algorithm -- Appendix B: Assessing the Reflectively Measured Constructs in the Corporate Reputation Model -- Glossary -- Index.