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

Hair, Joseph F.; Hult, G. Thomas M.; Ringle, Christian M.; Sarstedt, Marko ; Danks, Nicholas P. and Ray, Soumya (2021): Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. A Workbook. Wiesbaden: Springer Gabler.

Abstract

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.

• Item Type: Monograph
• Keywords: Partial least squares structural equation modeling; PLS-SEM; data requirements; measurement specification; open access guide; R; how-tos; SEMinR
• Faculties: Munich School of Management > Institute for Marketing
• Subjects: 300 Social sciences > 330 Economics
• ISBN: 978-3-030-80518-0 (print) ; 978-3-030-80519-7 (online)
• Place of Publication: Wiesbaden
• Language: English