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E-grāmata: Structural Equation Modelling with Partial Least Squares Using Stata and R

(Norwegian University of Science & Technology, Trondheim, Norway), (Department of Decision Sciences, Bocconi University, Milan)
  • Formāts: 382 pages
  • Izdošanas datums: 08-Mar-2021
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-13: 9781482227826
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Structural Equation Modelling with Partial Least Squares Using Stata and RPalielināt
  • Formāts: 382 pages
  • Izdošanas datums: 08-Mar-2021
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-13: 9781482227826
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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.

Recenzijas

"...This is certainly a welcome addition to the capability of Stata, and moreover, this book also includes sections on the use of packages in R software for PLS structural equation modeling...this book takes a balanced approach to presenting the statistical theory of PLS structural equationmodeling and its practical applications. The mathematical level is not high but is higher thanmost books on PLS structural equation modeling...this book will be useful for users of other software with an interest in getting a grasp of statistical theory behind PLS structural equation modeling. It is comprehensive and also accessible. Anyone who is seriously thinking of using PLS structural equation modeling for their research should carefully read through this book before embarking on their first analysis." - Yu-Kang Tu, Biometrics, July 2021 "The book is a very good guide for researchers in the field of structural equation modelling with partial least squares, for both beginners and professionals using Stata and R."

- Taras Lukashiv, International Society for Clinical Biostatistics, 72, 2021

Preface xiii
Authors xix
List of Figures
xxi
List of Tables
xxix
List of Algorithms
xxxi
Abbreviations xxxiii
Greek Alphabet xxxvii
I Preliminaries and Basic Methods
1(186)
1 Framing Structural Equation Modelling
3(12)
1.1 What Is Structural Equation Modelling?
3(3)
1.2 Two Approaches to Estimating SEM Models
6(4)
1.2.1 Covariance-based SEM
6(2)
1.2.2 Partial least squares SEM
8(1)
1.2.3 Consistent partial least squares SEM
9(1)
1.3 What Analyses Can PLS-SEM Do?
10(1)
1.4 The Language of PLS-SEM
11(2)
1.5 Summary
13(2)
2 Multivariate Statistics Prerequisites
15(74)
2.1 Bootstrapping
15(4)
2.2 Principal Component Analysis
19(9)
2.3 Segmentation Methods
28(21)
2.3.1 Cluster analysis
28(2)
2.3.1.1 Hierarchical clustering algorithms
30(9)
2.3.1.2 Partitional clustering algorithms
39(3)
2.3.2 Finite mixture models and model-based clustering
42(6)
2.3.3 Latent class analysis
48(1)
2.4 Path Analysis
49(7)
2.5 Getting to Partial Least Squares Structural Equation Modelling
56(3)
2.6 Summary
59(1)
Appendix: R Commands
59(21)
The bootstrap
60(2)
Principal component analysis
62(3)
Segmentation methods
65(9)
Latent class analysis
74(1)
Path analysis
74(6)
Appendix: Technical Details
80(9)
More insights on the bootstrap
80(2)
The algebra of principal components analysis
82(2)
Clustering stopping rules
84(2)
Finite mixture models estimation and selection
86(1)
Path analysis using matrices
87(2)
3 PLS Structural Equation Modelling: Specification and Estimation
89(66)
3.1 Introduction
89(3)
3.2 Model Specification
92(9)
3.2.1 Outer (measurement) model
93(3)
3.2.2 Inner (structural) model
96(1)
3.2.3 Application: Tourists satisfaction
97(4)
3.3 Model Estimation
101(7)
3.3.1 The PLS-SEM algorithm
102(1)
3.3.2 Stage I: Iterative estimation of latent variable scores
103(4)
3.3.3 Stage II: Estimation of measurement model parameters
107(1)
3.3.4 Stage III: Estimation of structural model parameters
107(1)
3.4 Bootstrap-based Inference
108(2)
3.5 The plssemStata Package
110(8)
3.5.1 Syntax
111(1)
3.5.2 Options
112(1)
3.5.3 Stored results
113(1)
3.5.4 Application: Tourists satisfaction (cont.)
113(5)
3.6 Missing Data
118(5)
3.6.1 Application: Tourists satisfaction (cont.)
121(2)
3.7 Effect Decomposition
123(4)
3.8 Sample Size Requirements
127(2)
3.9 Consistent PLS-SEM
129(5)
3.9.1 The pis seme command
130(4)
3.10 Higher Order Constructs
134(5)
3.11 Summary
139(1)
Appendix: R Commands
140(11)
The plspm package
141(4)
The cSEM package
145(6)
Appendix: Technical Details
151(4)
A formal definition of PLS-SEM
151(2)
More details on the consistent PLS-SEM approach
153(2)
4 PLS Structural Equation Modelling: Assessment and Interpretation
155(32)
4.1 Introduction
155(1)
4.2 Assessing the Measurement Part
156(7)
4.2.1 Reflective measurement models
156(1)
4.2.1.1 Unidimensionality
156(1)
4.2.1.2 Construct reliability
157(1)
4.2.1.3 Construct validity
157(2)
4.2.2 Higher order reflective measurement models
159(1)
4.2.3 Formative measurement models
160(1)
4.2.3.1 Content validity
161(1)
4.2.3.2 Multicollinearity
161(2)
4.2.3.3 Weights
163(1)
4.3 Assessing the Structural Part
163(4)
4.3.1 R-squared
164(1)
4.3.2 Goodness-of-fit
165(1)
4.3.3 Path coefficients
165(2)
4.4 Assessing a PLS-SEM Model: A Full Example
167(11)
4.4.1 Setting up the model using plssem
167(3)
4.4.2 Estimation using plssem in Stata
170(2)
4.4.3 Evaluation of the example study model
172(1)
4.4.3.1 Measurement part
172(4)
4.4.3.2 Structural part
176(2)
4.5 Summary
178(1)
Appendix: R Commands
178(5)
Appendix: Technical Details
183(4)
Tools for assessing the measurement part of a PLS-SEM model
183(2)
Tools for assessing the structural part of a PLS-SEM model
185(2)
II Advanced Methods
187(90)
5 Mediation Analysis With PLS-SEM
189(26)
5.1 Introduction
189(1)
5.2 Baron and Kenny's Approach to Mediation Analysis
189(6)
5.2.1 Modifying the Baron-Kenny approach
191(1)
5.2.2 Alternative to the Baron-Kenny approach
192(3)
5.2.3 Effect size of the mediation
195(1)
5.3 Examples in Stata
195(12)
5.3.1 Example I: A single observed mediator variable
196(2)
5.3.2 Example 2: A single latent mediator variable
198(5)
5.3.3 Example 3: Multiple latent mediator variables
203(4)
5.4 Moderated Mediation
207(1)
5.5 Summary
207(1)
Appendix: R Commands
208(7)
6 Moderating/Interaction Effects Using PLS-SEM
215(34)
6.1 Introduction
215(2)
6.2 Product-Indicator Approach
217(3)
6.3 Two-Stage Approach
220(3)
6.4 Multi-Sample Approach
223(3)
6.4.1 Parametric test
224(1)
6.4.2 Permutation test
225(1)
6.5 Example Study: Interaction Effects
226(9)
6.5.1 Application of the product-indicator approach
226(3)
6.5.2 Application of the two-stage approach
229(1)
6.5.2.1 Two-stage as an alternative to product-indicator
229(1)
6.5.2.2 Two-stage with a categorical moderator
230(4)
6.5.3 Application of the multi-sample approach
234(1)
6.6 Measurement Model Invariance
235(2)
6.7 Summary
237(1)
Appendix: R Commands
238(11)
Application of the product-indicator approach
238(1)
Application of the two-stage approach
239(4)
Application of the multi-sample approach
243(4)
Measurement model invariance
247(2)
7 Detecting Unobserved Heterogeneity in PLS-SEM
249(28)
7.1 Introduction
249(2)
7.2 Methods for the Identification and Estimation of Unobserved Heterogeneity in PLS-SEM
251(17)
7.2.1 Response-based unit segmentation in PLS-SEM
251(10)
7.2.2 Finite mixture PLS (FIMIX-PLS)
261(5)
7.2.3 Other methods
266(1)
7.2.3.1 Path modelling segmentation tree algorithm (Path-mox)
266(1)
7.2.3.2 Partial least squares genetic algorithm segmentation (PLS-GAS)
267(1)
7.3 Summary
268(1)
Appendix: R Commands
268(3)
Appendix: Technical Details
271(6)
The math behind the REBUS-PLS algorithm
271(3)
Permutation tests
274(3)
III Conclusions
277(10)
8 How to Write Up a PLS-SEM Study
279(8)
8.1 Publication Types and Structure
279(1)
8.2 Example of PLS-SEM Publication
280(5)
8.3 Summary
285(2)
IV Appendices
287(2)
A Basic Statistics Prerequisites
289(32)
A.1 Covariance and Correlation
289(7)
A.2 Linear Regression Analysis
296(24)
A.2.1 The simple linear regression model
296(3)
A.2.2 Goodness-of-fit
299(1)
A.2.3 The multiple linear regression model
300(2)
A.2.4 Inference for the linear regression model
302(1)
A.2.4.1 Normal-based inference
303(2)
A.2.5 Categorical predictors
305(4)
A.2.6 Multicollinearity
309(2)
A.2.7 Example
311(9)
A.3 Summary
320(1)
Appendix: R Commands
321(1)
Covariance and correlation 321(4)
Bibliography 325(16)
Index 341
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 Universitą 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.
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