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Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook by Christian M. Ringle, Joseph F. Hair Jr., G. Tomas M. Hult, Marko Sarstedt, Soumya Ray, Nicholas P. Danks (Hardcover, 2021)

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Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM. .

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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.