Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R A Workbook - Hair Jr., Joseph F.; Hult, G. Tomas M.; Ringle, Christian M.; Sarstedt, Marko; Danks, Nicholas P.; Ray, Soumya; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2021)
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$1 (Bs 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$1 (Bs 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 (3z(Bhow-tos(3y (Bof using SEMinR to obtain solutions and document their results. Rules of thumbin every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.s
An Introduction to Structural Equation Modeling -- Introduction to R and RStudio -- Introduction to SEMinR -- Evaluation of Reflective Measurement Models -- Evaluation of Formative Measurement Models -- Evaluation of the Structural Model -- Mediation Analysis -- Moderation Analysis.
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