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.