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.
