Structural Equation Modelling with Partial Least Squares Using Stata and R

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Structural Equation Modelling with Partial Least Squares Using Stata and R

Mehmetoglu, Mehmet/ Venturini, Sergio

ウェブストア価格 ¥13,334（本体¥12,122）
Chapman & Hall/CRC（2022/08発売）
外貨定価 US$ 63.99
ポイント 121pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 347 p.
言語 ENG
商品コード 9780367701833
DDC分類 511.42

Full Description

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