Structural equation modelling with partial least squares using stata and R
著者
書誌事項
Structural equation modelling with partial least squares using stata and R
（A Chapman & Hall book）
CRC Press, 2021
- : hbk
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注記
Includes bibliographical references (p. 325-339) and index
内容説明・目次
内容説明
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.
目次
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
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