Structural Equation Modelling with Partial Least Squares Using Stata and R

PLS-SEM is a popular statistical framework for estimating models with latent variables, observed variables, or a combination of these. This book provides a practical guide to using PLS-SEM, with explanations of the methodology, examples, and software packages for both Stata and R. It is primarily aimed at researchers and graduate students in statistics, social science, psychology, and other disciplines.

Format: Paperback / softback
Length: 347 pages
Publication date: 29 August 2022
Publisher: Taylor & Francis Ltd

Partial least squares structural equation modeling (PLS-SEM) is gaining widespread popularity across various social science fields and disciplines. Its appeal lies in its ability to estimate models encompassing latent variables, observed variables, or a combination of these. This popularity is expected to surge further with the development of more robust estimation techniques, such as consistent PLS-SEM. The traditional and modern estimation methods for PLS-SEM are now readily accessible through open-source and commercial software packages.

This comprehensive book presents PLS-SEM as a valuable practical statistical toolbox suitable for estimating diverse research models. By providing the necessary technical prerequisites and theoretical treatment of various PLS-SEM aspects, the authors facilitate its practical applications. What sets this book apart is its extensive explanation and practical usage of comprehensive Stata (plssem) and R (cSEM and plspm) packages for conducting PLS-SEM analysis. The book's primary objective is to assist readers in understanding the mechanics of PLS-SEM and applying it for publication purposes.

Key Features:
Intuitive and Technical Explanations: The book offers clear and concise explanations of PLS-SEM methods, making them accessible to researchers and graduate students with varying levels of statistical expertise.
Comprehensive Coverage of Stata and R Packages: The authors provide detailed explanations of Stata and R packages, enabling readers to utilize these powerful tools for conducting PLS-SEM analysis.
Numerous Example Applications: The book showcases numerous real-world example applications of PLS-SEM, demonstrating its practical utility across various social science disciplines.
Detailed Interpretation of Software Output: The book includes detailed interpretation of software output, helping readers understand the results and implications of PLS-SEM analysis.
Reporting of a PLS-SEM Study: The book provides a step-by-step guide for reporting a PLS-SEM study, including the necessary statistical procedures and reporting guidelines.
Github Repository for Supplementary Book Material: The authors offer a GitHub repository containing supplementary book material, including data sets, code, and additional resources, to support readers' learning and exploration.

Target Audience:
The book is primarily aimed at researchers and graduate students from statistics, social science, psychology, and other related disciplines. While technical details have been moved to appendices, the book provides sufficient background information to facilitate a comprehensive understanding of PLS-SEM.

In conclusion, Partial least squares structural equation modeling (PLS-SEM) is emerging as a prominent statistical framework in social science research. This book offers a comprehensive and practical guide to PLS-SEM, providing researchers and students with the necessary tools and knowledge to estimate models encompassing latent variables, observed variables, or a combination of these. With its extensive coverage of Stata and R packages, numerous example applications, and detailed interpretation of software output, this book is an invaluable resource for advancing the understanding and application of PLS-SEM in social science research.

Weight: 578g
Dimension: 156 x 233 x 24 (mm)
ISBN-13: 9780367701833