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

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