Partial least squares structural equation modeling (PLS-SEM) using R : a workbook / Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, Soumya Ray.

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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...

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Bibliographic Details
Uniform Title:Classroom companion. Business.
Main Authors: Hair, Joseph F., Jr., 1944- (Author)
Hult, G. Tomas M. (Author)
Ringle, Christian M. (Author)
Sarstedt, Marko (Author)
Danks, Nicholas P. (Author)
Ray, Soumya (Author)
Language:English
Published: Cham, Switzerland : Springer, 2021.
Series:Classroom companion. Business.
Subjects:
Least squares.
Structural equation modeling.
Finance > Mathematical models.
R (Computer program language)
Local Note:
MSU Stanley C. and Selma D. Hollander Faculty Book Collection
Physical Description:xiv, 197 pages : illustrations (some color) ; 24 cm.
Format: Book

MARC

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505 0 |a 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. 
520 |a 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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