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) using R : a workbook / Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, Soumya Ray.

9783030805197 (electronic bk.)
3030805190 (electronic bk.)
9783030805180 (print)

Cham, Switzerland : Springer, 2021.

English

1 online resource (xiv, 197 pages) : illustrations (some color).

10.1007/978-3-030-80519-7 doi

QA275

511/.42

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

Includes index.

Open access.

text file
PDF

Online resource; title from PDF title page (SpringerLink, viewed November 11, 2021).

Classroom companion. Business.

Print version: 9783030805180
Print version: 9783030805203

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