Structural Equation Modelling with Partial Least Squares Using Stata and R: Theory and Applications Using Stata and R

✍ Scribed by Mehmet Mehmetoglu, Sergio Venturini

• Publisher: Chapman and Hall/CRC
• Year: 2021
• Tongue: English
• Leaves: 385
• Edition: 1
• Category: Library

✦ 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.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Authors
List of Figures
List of Tables
List of Algorithms
Abbreviations
Greek Alphabet
I. Preliminaries and Basic Methods
1. Framing Structural Equation Modelling
1.1. What Is Structural Equation Modelling?
1.2. Two Approaches to Estimating SEM Models
1.2.1. Covariance-based SEM
1.2.2. Partial least squares SEM
1.2.3. Consistent partial least squares SEM
1.3. What Analyses Can PLS-SEM Do?
1.4. The Language of PLS-SEM
1.5. Summary
2. Multivariate Statistics Prerequisites
2.1. Bootstrapping
2.2. Principal Component Analysis
2.3. Segmentation Methods
2.3.1. Cluster analysis
2.3.1.1. Hierarchical clustering algorithms
2.3.1.2. Partitional clustering algorithms
2.3.2. Finite mixture models and model-based clustering
2.3.3. Latent class analysis
2.4. Path Analysis
2.5. Getting to Partial Least Squares Structural Equation Modelling
2.6. Summary
Appendix: R Commands
The bootstrap
Principal component analysis
Segmentation methods
Latent class analysis
Path analysis
Appendix: Technical Details
More insights on the bootstrap
The algebra of principal components analysis
Clustering stopping rules
Finite mixture models estimation and selection
Path analysis using matrices
3. PLS Structural Equation Modelling: Specification and Estimation
3.1. Introduction
3.2. Model Specification
3.2.1. Outer (measurement) model
3.2.2. Inner (structural) model
3.2.3. Application: Tourists satisfaction
3.3. Model Estimation
3.3.1. The PLS-SEM algorithm
3.3.2. Stage I: Iterative estimation of latent variable scores
3.3.3. Stage II: Estimation of measurement model parameters
3.3.4. Stage III: Estimation of structural model parameters
3.4. Bootstrap-based Inference
3.5. The plssem Stata Package
3.5.1. Syntax
3.5.2. Options
3.5.3. Stored results
3.5.4. Application: Tourists satisfaction (cont.)
3.6. Missing Data
3.6.1. Application: Tourists satisfaction (cont.)
3.7. Effect Decomposition
3.8. Sample Size Requirements
3.9. Consistent PLS-SEM
3.9.1. The plssemc command
3.10. Higher Order Constructs
3.11. Summary
Appendix: R Commands
The plspm package
The cSEM package
Appendix: Technical Details
A formal definition of PLS-SEM
More details on the consistent PLS-SEM approach
4. PLS Structural Equation Modelling: Assessment and Interpretation
4.1. Introduction
4.2. Assessing the Measurement Part
4.2.1. Reflective measurement models
4.2.1.1. Unidimensionality
4.2.1.2. Construct reliability
4.2.1.3. Construct validity
4.2.2. Higher order reflective measurement models
4.2.3. Formative measurement models
4.2.3.1. Content validity
4.2.3.2. Multicollinearity
4.2.3.3. Weights
4.3. Assessing the Structural Part
4.3.1. R-squared
4.3.2. Goodness-of-fit
4.3.3. Path coefficients
4.4. Assessing a PLS-SEM Model: A Full Example
4.4.1. Setting up the model using plssem
4.4.2. Estimation using plssem in Stata
4.4.3. Evaluation of the example study model
4.4.3.1. Measurement part
4.4.3.2. Structural part
4.5. Summary
Appendix: R Commands
Appendix: Technical Details
Tools for assessing the measurement part of a PLS-SEM model
Tools for assessing the structural part of a PLS-SEM model
II. Advanced Methods
5. Mediation Analysis With PLS-SEM
5.1. Introduction
5.2. Baron and Kenny's Approach to Mediation Analysis
5.2.1. Modifying the Baron-Kenny approach
5.2.2. Alternative to the Baron-Kenny approach
5.2.3. Effect size of the mediation
5.3. Examples in Stata
5.3.1. Example 1: A single observed mediator variable
5.3.2. Example 2: A single latent mediator variable
5.3.3. Example 3: Multiple latent mediator variables
5.4. Moderated Mediation
5.5. Summary
Appendix: R Commands
6. Moderating/Interaction Effects Using PLS-SEM
6.1. Introduction
6.2. Product-Indicator Approach
6.3. Two-Stage Approach
6.4. Multi-Sample Approach
6.4.1. Parametric test
6.4.2. Permutation test
6.5 Example Study: Interaction Effects
6.5.1. Application of the product-indicator approach
6.5.2. Application of the two-stage approach
6.5.2.1. Two-stage as an alternative to product-indicator
6.5.2.2. Two-stage with a categorical moderator
6.5.3. Application of the multi-sample approach
6.6. Measurement Model Invariance
6.7. Summary
Appendix: R Commands
Application of the product-indicator approach
Application of the two-stage approach
Application of the multi-sample approach
Measurement model invariance
7. Detecting Unobserved Heterogeneity in PLS-SEM
7.1. Introduction
7.2. Methods for the Identification and Estimation of Unobserved Heterogeneity in PLS-SEM
7.2.1. Response-based unit segmentation in PLS-SEM
7.2.2. Finite mixture PLS (FIMIX-PLS)
7.2.3. Other methods
7.2.3.1. Path modelling segmentation tree algorithm (Pathmox)
7.2.3.2. Partial least squares genetic algorithm segmentation (PLS-GAS)
7.3. Summary
Appendix: R Commands
Appendix: Technical Details
The math behind the REBUS-PLS algorithm
Permutation tests
III. Conclusions
8. How to Write Up a PLS-SEM Study
8.1. Publication Types and Structure
8.2. Example of PLS-SEM Publication
Summary
IV. Appendices
A. Basic Statistics Prerequisites
A.1. Covariance and Correlation
A.2. Linear Regression Analysis
A.2.1. The simple linear regression model
A.2.2. Goodness-of-fit
A.2.3. The multiple linear regression model
A.2.4. Inference for the linear regression model
A.2.4.1. Normal-based inference
A.2.5. Categorical predictors
A.2.6. Multicollinearity
A.2.7. Example
A.3. Summary
Appendix: R Commands
Covariance and correlation
Bibliography
Index