Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

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.


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Brief Table of Contents

Chapter 1: An Introduction to Structural Equation Modeling

Chapter 2: Overview of R and RStudio

Chapter 3: The SEMinR Package

Chapter 4: Evaluation of Reflective Measurement Models

Chapter 5: Evaluation of Formative Measurement Models

Chapter 6: Evaluation of the Structural Model

Chapter 7: Mediation Analysis

Chapter 8: Moderation Analysis

Supplementary Information

     Appendix A: The PLS-SEM Algorithm

     Appendix B: Assessing the Reflectively Measured Constructs in Corporate Reputation Model