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

Bibliographic Details
Main Author: Hair Jr., Joseph F.
Other Authors: Hult, G. Tomas M., Ringle, Christian M., Sarstedt, Marko., Danks, Nicholas P., Ray, Soumya.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2021.
Edition:1st ed.
Series:Classroom Companion: Business Series
Subjects:
Electronic books.
Online Access:Click to View
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.

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