Electronic Resource

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

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

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Detail Information

Series Title

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Call Number

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Publisher

: Springer Cham., 2021

Collation

XIV, 197 hlm,: ill, lamp;

Language

English

ISBN/ISSN

9783030805197

Classification

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Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Marketing
Econometrics,
Statistics and Computing/Statistics Programs

Specific Detail Info

This book is open access, which means that you have free and unlimited access Offers concise guidelines on how to apply and interpret PLS-SEM results Includes an llustrative step-by-step application of PLS-SEM within the R software environment Draws on the highly user-friendly SEMinR package, co-developed by two of the co-authors Adopts a case study approach that focuses on the illustration of relevant analysis steps

Statement of Responsibility

Joseph F. Hair Jr. , G. Tomas M. Hult , Christian M. Ringle , Marko Sarstedt , Nicholas P. Danks , Soumya Ray

Other Information

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Source

https://link.springer.com/book/10.1007/978-3-030-80519-7

Validator

ida

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-3-030-80519-7

Journal Volume

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Journal Issue

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