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Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 3rd Revised edition [Mīkstie vāki]

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(Hamburg University of Technology, Germany), (Michigan State University, USA), (University of South Alabama, USA), (Ludwig-Maximilians-University, Munich, Germany)
  • Formāts: Paperback / softback, 384 pages, height x width: 228x152 mm, weight: 560 g
  • Izdošanas datums: 26-Aug-2021
  • Izdevniecība: SAGE Publications Inc
  • ISBN-10: 1544396406
  • ISBN-13: 9781544396408
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  • Formāts: Paperback / softback, 384 pages, height x width: 228x152 mm, weight: 560 g
  • Izdošanas datums: 26-Aug-2021
  • Izdevniecība: SAGE Publications Inc
  • ISBN-10: 1544396406
  • ISBN-13: 9781544396408
Citas grāmatas par šo tēmu:
The third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) guides readers through learning and mastering the techniques of this approach in clear language. Authors Joseph H. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt use their years of conducting and teaching research to communicate the fundamentals of PLS-SEM in straightforward language to explain the details of this method, with limited emphasis on equations and symbols. A running case study on corporate reputation follows the different steps in this technique so readers can better understand the research applications. Learning objectives, review and critical thinking questions, and key terms help readers cement their knowledge. This edition has been thoroughly updated, featuring the latest version of the popular software package SmartPLS 3. New topics have been added throughout the text, including a thoroughly revised and extended chapter on mediation, recent research on the foundations of PLS-SEM, detailed descriptions of research summarizing the advantages as well as limitations of PLS-SEM, and extended coverage of advanced concepts and methods, such as out-of-sample versus in-sample prediction metrics, higher-order constructs, multigroup analysis, necessary condition analysis, and endogeneity. 
Preface xi
About the Authors xviii
Chapter 1 An Introduction to Structural Equation Modeling 1(39)
Preview
1(1)
What Is Structural Equation Modeling?
2(4)
Considerations in Using Structural Equation Modeling
6(6)
Composite Variables
6(1)
Measurement
7(1)
Measurement Scales
8(2)
Coding
10(1)
Data Distributions
10(2)
Principles of Structural Equation Modeling
12(3)
Path Models With Latent Variables
12(1)
Testing Theoretical Relationships
13(5)
Measurement Theory
14(1)
Structural Theory
14(1)
PLS-SEM, CB-SEM, and Regressions Based on Sum Scores
15(3)
Considerations When Applying PLS-SEM
18(13)
Key Characteristics of the PLS-SEM Method
18(6)
Data Characteristics
24(6)
Minimum Sample Size Requirement
24(3)
Missing Value Treatment
27(1)
Nonnormal Data
28(1)
Scales of Measurement
28(1)
Secondary Data
28(2)
Model Characteristics
30(1)
Guidelines for Choosing Between PLS-SEM and CB-SEM
31(1)
Organization of Remaining
Chapters
32(2)
Summary
34(2)
Review Questions
36(1)
Critical Thinking Questions
36(1)
Key Terms
36(1)
Suggested Readings
37(3)
Chapter 2 Specifying the Path Model and Examining Data 40(45)
Preview
41(1)
Stage 1: Specifying the Structural Model
41(9)
Mediation
44(1)
Moderation
45(2)
Control Variables
47(3)
Stage 2: Specifying the Measurement Models
50(11)
Reflective and Formative Measurement Models
51(6)
Single-Item Measures and Sum Scores
57(2)
Higher-Order Constructs
59(2)
Stage 3: Data Collection and Examination
61(6)
Missing Data
62(2)
Suspicious Response Patterns
64(1)
Outliers
64(1)
Data Distribution
65(2)
Case Study Illustration-Specifying the PLS-SEM Model
67(12)
Application of Stage 1: Structural Model Specification
67(2)
Application of Stage 2: Measurement Model Specification
69(2)
Application of Stage 3: Data Collection and Examination
71(1)
Path Model Creation Using the SmartPLS Software
72(7)
Summary
79(2)
Review Questions
81(1)
Critical Thinking Questions
82(1)
Key Terms
82(1)
Suggested Readings
83(2)
Chapter 3 Path Model Estimation 85(24)
Preview
85(1)
Stage 4: Model Estimation and the PLS-SEM Algorithm
86(11)
How the Algorithm Works
86(6)
Statistical Properties
92(2)
Algorithmic Options and Parameter Settings to Run the Algorithm
94(2)
Results
96(1)
Case Study Illustration-PLS Path Model Estimation (Stage 4)
97(7)
Model Estimation
97(2)
Estimation Results
99(5)
Summary
104(1)
Review Questions
105(1)
Critical Thinking Questions
106(1)
Key Terms
106(1)
Suggested Readings
106(3)
Chapter 4 Assessing PLS-SEM Results-Part I: Evaluation of the Reflective Measurement Models 109(31)
Preview
109(1)
Overview of Stage 5: Evaluation of Measurement Models
110(6)
Stage 5a: Assessing Results of Reflective Measurement Models
116(11)
Step 1: Indicator Reliability
117(1)
Step 2: Internal Consistency Reliability
118(2)
Step 3: Convergent Validity
120(1)
Step 4: Discriminant Validity
120(7)
Case Study Illustration-Evaluation of the Reflective Measurement Models (Stage 5a)
127(9)
Running the PLS-SEM Algorithm
127(1)
Reflective Measurement Model Evaluation
128(8)
Summary
136(1)
Review Questions
137(1)
Critical Thinking Questions
137(1)
Key Terms
137(1)
Suggested Readings
138(2)
Chapter 5 Assessing PLS-SEM Results-Part II: Evaluation of the Formative Measurement Models 140(46)
Preview
140(1)
Stage 5b: Assessing Results of Formative Measurement Models
141(18)
Step 1: Assess Convergent Validity
143(2)
Step 2: Assess Formative Measurement Models for Collinearity Issues
145(3)
Step 3: Assess the Significance and Relevance of the Formative Indicators
148(4)
Bootstrapping Procedure
152(7)
Concept
152(4)
Bootstrap Confidence Intervals
156(3)
Case Study Illustration-Evaluation of the Formative Measurement Models (Stage 513)
159(23)
Extending the Simple Path Model
159(10)
Reflective Measurement Model Evaluation [ Recap)
169(2)
Formative Measurement Model Evaluation
171(11)
Summary
182(1)
Review Questions
183(1)
Critical Thinking Questions
183(1)
Key Terms
184(1)
Suggested Readings
184(2)
Chapter 6 Assessing PLS-SEM Results-Part III: Evaluation of the Structural Model 186(42)
Preview
186(1)
Stage 6: Structural Model Results Evaluation
187(22)
Step 1: Assess the Structural Model for Collinearity
191(1)
Step 2: Assess the Significance and Relevance of the Structural Model Relationships
192(2)
Step 3: Assess the Model's Explanatory Power
194(2)
Step 4: Assess the Model's Predictive Power
196(9)
Number of Folds
198(1)
Number of Repetitions
199(1)
Prediction Statistic
200(1)
Results Interpretation
201(3)
Treating Predictive Power Issues
204(1)
Step 5: Model Comparisons
205(4)
Case Study Illustration-Evaluation of the Structural Model [ Stage 6)
209(14)
Summary
223(2)
Review Questions
225(1)
Critical Thinking Questions
225(1)
Key Terms
225(1)
Suggested Readings
226(2)
Chapter 7 Mediator and Moderator Analysis 228(43)
Preview
228(1)
Mediation
229(14)
Introduction
229(4)
Measurement and Structural Model Evaluation in Mediation Analysis
233(1)
Types of Mediating Effects
233(3)
Testing Mediating Effects
236(2)
Multiple Mediation
238(2)
Case Study Illustration-Mediation
240(3)
Moderation
243(17)
Introduction
243(2)
Types of Moderator Variables
245(2)
Modeling Moderating Effects
247(2)
Creating the Interaction Term
249(37)
Product Indicator Approach
249(1)
Orthogonalizing Approach
250(1)
Two-Stage Approach
251(2)
Guidelines for Creating the Interaction Term
253(1)
Model Evaluation
253(1)
Results Interpretation
254(3)
Moderated Mediation and Mediated Moderation
257(3)
Case Study Illustration-Moderation
260(7)
Summary
267(1)
Review Questions
268(1)
Critical Thinking Questions
268(1)
Key Terms
269(1)
Suggested Readings
269(2)
Chapter 8 Outlook on Advanced Methods 271(34)
Preview
271(2)
Importance-Performance Map Analysis
273(3)
Necessary Condition Analysis
276(1)
Higher-Order Constructs
277(4)
Confirmatory Tetrad Analysis
281(4)
Examining Endogeneity
285(1)
Treating Observed and Unobserved Heterogeneity
286(8)
Multigroup Analysis
287(3)
Uncovering Unobserved Heterogeneity
290(4)
Measurement Model Invariance
294(2)
Consistent PLS-SEM
296(2)
Summary
298(2)
Review Questions
300(1)
Critical Thinking Questions
301(1)
Key Terms
301(1)
Suggested Readings
302(3)
Glossary 305(22)
References 327(25)
Index 352
Joseph F. Hair, Jr.is Professor of Marketing, PhD Director, and the Cleverdon Chair of Business in the Mitchell College of Business, University of South Alabama, USA. He previously held the Copeland Endowed Chair of Entrepreneurship and was Director, Entrepreneurship Institute, Ourso College of Business Administration, Louisiana State University. He has authored over 95 books, including Multivariate Data Analysis (8th edition, 2019) (cited 170,000+ times), MKTG (13th edition, 2019), Essentials of Business Research Methods, 5th edition, 2023), and Essentials of Marketing Research (6th edition, 2023). Dr. Hair is the most highly cited scholar in PLS-SEM and marketing, with 340,000+ citations (Google Scholar, 2023). He also has published numerous articles in scholarly journals and was recognized as the Academy of Marketing Science Marketing Educator of the year. A popular guest speaker, Professor Hair often presents seminars on research techniques, multivariate data analysis, and marketing issues for organizations in Europe, Australia, China, India, and South America.

G. Tomas M. Hult is Professor and Byington Endowed Chair at Michigan State University (USA), and holds a visiting Chaired Professorship at Leeds University Business School (United Kingdom) and a visiting professorship at Uppsala University (Sweden). Professor Hult is a member of the Expert Networks of the World Economic Forum and United Nations/UNCTADs World Investment Forum, and is also part of the Expert Team at the American Customer Satisfaction Index (ACSI). Dr. Hult was recognized in 2016 as the Academy of Marketing Science / CUTCO-Vector Distinguished Marketing Educator; he is an elected Fellow of the Academy of International Business; and he ranks in the top-10 scholars in marketing per the prestigious world ranking of scientists. At Michigan State University, Dr. Hult was recognized with the Beal Outstanding Faculty Award in 2019 (MSUs highest award for outstanding total service to the University), and he has also been recognized with the John Dunning AIB Service Award for outstanding service to AIB as the longest serving Executive Director in AIBs history (2004-2019) (the most prestigious service award given by the Academy of International Business). Professor Hult regularly teaches doctoral seminars on multivariate statistics, structural equation modeling, and hierarchical linear modeling worldwide. He is a dual citizen of Sweden and the United States. More information about Professor Hult can be found at http://www.tomashult.com.

Christian M. Ringle is Professor of Management at the Hamburg University of Technology (Germany). His research addresses management of organizations, human resource management, methods development for business analytics and their application to business research. His contributions in these fields have been published in journals such as International Journal of Research in Marketing, Information Systems Research, Journal of the Academy of Marketing Science, MIS Quarterly, Organizational Research Methods, and The International Journal of Human Resource Management. Since 2018, he has been named member of Clarivate Analytics Highly Cited Researchers List. In 2014, Ringle co-founded SmartPLS (http://www.smartpls.com), a software tool with a graphical user interface for the application of the partial least squares structural equation modeling (PLS-SEM) method. Besides supporting consultancies and international corporations, he regularly teaches doctoral seminars on business analytics and multivariate statistics, the PLS-SEM method, and the use of SmartPLS worldwide. More information about Professor Dr. Christian M. Ringle can be found at https://www.tuhh.de/hrmo/team/prof-dr-c-m-ringle.html.

Marko Sarstedt is Professor of Marketing at the Ludwig-Maximilians-University Munich (Germany) and an adjunct research professor at Babe?-Bolyai-University Cluj-Napoca (Romania). His main research interest is the advancement of research methods to further the understanding of consumer behavior. His research has been published in Nature Human Behaviour, Journal of Marketing Research, Journal of the Academy of Marketing Science, Multivariate Behavioral Research, Organizational Research Methods, MIS Quarterly, British Journal of Mathematical and Statistical Psychology, and Psychometrika, among others. His research ranks among the most frequently cited in the social sciences with more than 100,000 citations according to Google Scholar. Marko has won numerous best paper and citation awards, including five Emerald Citations of Excellence awards and two AMS William R. Darden Awards. Marko has been repeatedly named member of Clarivate Analytics Highly Cited Researchers List. In March 2022, he was awarded an honorary doctorate from Babe?-Bolyai-University Cluj-Napoca for his research achievements and contributions to international exchange.