1st Edition
Structural Equation Modelling with Partial Least Squares Using Stata and R
Partial least squares structural equation modelling (PLS-SEM) is becoming a popular statistical framework in many fields and disciplines of the social sciences. The main reason for this popularity is that PLS-SEM can be used to estimate models including latent variables, observed variables, or a combination of these. The popularity of PLS-SEM is predicted to increase even more as a result of the development of new and more robust estimation approaches, such as consistent PLS-SEM. The traditional and modern estimation methods for PLS-SEM are now readily facilitated by both open-source and commercial software packages.
This book presents PLS-SEM as a useful practical statistical toolbox that can be used for estimating many different types of research models. In so doing, the authors provide the necessary technical prerequisites and theoretical treatment of various aspects of PLS-SEM prior to practical applications. What makes the book unique is the fact that it thoroughly explains and extensively uses comprehensive Stata (plssem) and R (cSEM and plspm) packages for carrying out PLS-SEM analysis. The book aims to help the reader understand the mechanics behind PLS-SEM as well as performing it for publication purposes.
Features:
- Intuitive and technical explanations of PLS-SEM methods
- Complete explanations of Stata and R packages
- Lots of example applications of the methodology
- Detailed interpretation of software output
- Reporting of a PLS-SEM study
- Github repository for supplementary book material
The book is primarily aimed at researchers and graduate students from statistics, social science, psychology, and other disciplines. Technical details have been moved from the main body of the text into appendices, but it would be useful if the reader has a solid background in linear regression analysis.
Part I Preliminaries and Basic Methods
1. Framing Structural Equation Modelling
2. Multivariate Statistics Prerequisites
3. PLS Structural Equation Modelling: Specification and Estimation
4. PLS Structural Equation Modelling: Assessment and Interpretation
Part II Advanced Methods
5. Mediation AnalysisWith PLS-SEM
6. Moderating/Interaction Effects Using PLS-SEM
7. Detecting Unobserved Heterogeneity in PLS-SEM
Part III Conclusions
8. How to Write Up a PLS-SEM Study
Part IV Appendices
A. Basic Statistics Prerequisites
Biography
Mehmet Mehmetoglu is a professor of research methods in the Department of Psychology at the Norwegian University of Science and Technology (NTNU). His research interests include consumer psychology, evolutionary psychology and statistical methods. Mehmetoglu has co/publications in about 30 different refereed international journals such as Journal of Statistical Software, Personality and Individual Differences, and Evolutionary Psychological Science.
Sergio Venturini is an Associate Professor of Statistics in the Management Department at the Università degli Studi di Torino (Italy). His research interests include Bayesian data analysis methods, meta-analysis and statistical computing. He coauthored many publications that have been published in different refereed international journals such as Annals of Applied Statistics, Bayesian Analysis and Journal of Statistical Software.
"...This is certainly a welcome addition to the capability of Stata, and moreover, this book also includes sections on the use of packages in R software for PLS structural equation modeling...this book takes a balanced approach to presenting the statistical theory of PLS structural equationmodeling and its practical applications. The mathematical level is not high but is higher thanmost books on PLS structural equation modeling...this book will be useful for users of other software with an interest in getting a grasp of statistical theory behind PLS structural equation modeling. It is comprehensive and also accessible. Anyone who is seriously thinking of using PLS structural equation modeling for their research should carefully read through this book before embarking on their first analysis."
- Yu-Kang Tu, Biometrics, July 2021
