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

✍ Scribed by Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, Soumya Ray

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 208
• Edition: 1
• Category: Library

✦ Synopsis

Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification.

This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.

✦ Table of Contents

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