Structural Equation Modelling with Partial Least Squares Using Stata and R

Mehmet Mehmetoglu

ISBN 10: 0367701839 / ISBN 13: 9780367701833

Published by Chapman And Hall/CRC Aug 2022, 2022

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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 methodsComplete explanations of Stata and R packagesLots of example applications of the methodologyDetailed interpretation of software outputReporting of a PLS-SEM studyGithub repository for supplementary book materialThe 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. 384 pp. Englisch.

Seller Inventory # 9780367701833

Bibliographic Details

• Title: Structural Equation Modelling with Partial Least Squares Using Stata and R
• Publisher: Chapman And Hall/CRC Aug 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 0367701839
• ISBN 13: 9780367701833
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.

About the Author

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.