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

• Main Author: Hair Jr., Joseph F. (auth)
• Other Authors: Hult, G. Tomas M. (auth), Ringle, Christian M. (auth), Sarstedt, Marko (auth), Danks, Nicholas P. (auth), Ray, Soumya (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: Springer Nature 2021
• Series: Classroom Companion: Business
• Subjects: Sales & marketing; Mathematical & statistical software; Econometrics; Open Access; PLS-SEM) Using R; Workbook; Partial Least Squares Structural Equation Modeling; R Software Environment
• Online Access: OAPEN Library: download the publication; OAPEN Library: description of the publication

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.