A primer on partial least squares structural equation modeling (PLS-SEM):
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| Main Authors: | , , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne
SAGE
[2017]
|
| Edition: | Second edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverzeichnis Seite 331-349 |
| Physical Description: | xx, 363 Seiten Illustrationen, Diagramme |
| ISBN: | 9781483377445 |
Staff View
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Record in the Search Index
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| adam_text | Brief Contents
Preface xi
About the Authors xix
Chapter 1: An Introduction to Structural
Equation Modeling 1
Chapter 2: Specifying the Path Model and
Examining Data 36
Chapter 3: Path Model Estimation 81
Chapter 4: Assessing PLS-SEM Results Part I:
Evaluation of Reflective Measurement Models 104
Chapter 5: Assessing PLS-SEM Results Part II:
Evaluation of the Formative Measurement Models 137
Chapter 6: Assessing PLS-SEM Results Part III:
Evaluation of the Structural Model 190
Chapter 7: Mediator and Moderator Analysis 227
Chapter 8: Outlook on Advanced Methods 275
Glossary 312
References 331
Author Index 346
Subject Index 350
Visit the companion site for this book at http://study.sagepub.com/
hairprimer2e.
Detailed Contents
Preface xi
About the Authors xix
Chapter 1: An Introduction to Structural Equation
Modeling 1
Chapter Preview 1
What Is Structural Equation Modeling? 2
Considerations in Using Structural Equation Modeling 4
Composite Variables 5
Measurement 5
Measurement Scales 7
Coding 9
Data Distributions 10
Structural Equation Modeling With Partial Least Squares
Path Modeling 11
Path Models With Latent Variables 11
Measurement Theory 13
Structural Theory 14
PLS-SEM, CB-SEM, and Regressions Based on Sum Scores 14
Data Characteristics 22
Model Characteristics 27
Organization of Remaining Chapters 29
Summary 31
Review Questions 33
Critical Thinking Questions 33
Key Terms 34
Suggested Readings 35
Chapter 2: Specifying the Path Model and
Examining Data 36
Chapter Preview 36
Stage 1: Specifying the Structural Model 37
Mediation 39
Moderation 41
Higher-Order and Hierarchical Component
Models 43
Stage 2: Specifying the Measurement Models 44
Reflective and Formative Measurement Models 46
Single-Item Measures and Sum Scores 51
Stage 3: Data Collection and Examination 56
Missing Data 56
Suspicious Response Patterns 58
Outliers 59
Data Distribution 60
Case Study Illustration—Specifying the PLS-SEM Model 62
Application of Stage 1; Structural A4odel
Specification 63
Application of Stage 2: Measurement Model
Specification 64
Application of Stage 3: Data Collection and
Examination 66
Path Model Creation Using the SmartPLS Software 68
Summary 76
Review Questions 78
Critical Thinking Questions 78
Key Terms 79
Suggested Readings 80
Chapter 3: Path Model Estimation 81
Chapter Preview 81
Stage 4: Model Estimation and the PLS-SEM Algorithm 82
How the Algorithm Works 82
Statistical Properties 86
Algorithmic Options and Parameter Settings to Run the
Algorithm 89
Results 91
Case Study Illustration—PLS Path Model Estimation
(Stage 4) 92
Model Estimation 93
Estimation Results 95
Summary 99
Review Questions 101
Critical Thinking Questions 102
Key Terms 102
Suggested Readings 102
Chapter 4: Assessing PLS-SEM Results Part I: Evaluation of
Reflective Measurement Models 104
Chapter Preview 104
Overview of Stage 5: Evaluation of Measurement
Models 105
Stage 5a: Assessing Results of Reflective Measurement
Models 111
Internal Consistency Reliability 111
Convergent Validity 112
Discriminant Validity 115
Case Study Illustration—Reflective Measurement
Models 122
Running the PLS-SEM Algorithm 122
Reflective Measurement Model Evaluation 124
Summary 133
Review Questions 134
Critical Thinking Questions 134
Key Terms 134
Suggested Readings 135
Chapter 5: Assessing PLS-SEM Results Part II: Evaluation of
the Formative Measurement Models 137
Chapter Preview 137
Stage 5b: Assessing Results of Formative Measurement
Models 138
Step 1: Assess Convergent Validity 140
Step 2: Assess Formative Measurement Models for
Collinearity Issues 141
Step 3: Assess the Significance and Relevance of the
Formative Indicators 144
Bootstrapping Procedure 149
Case Study Illustration—Evaluation of Formative
Measurement Models 159
Extending the Simple Path Model 159
Reflective Measurement Model Evaluation 169
Formative Measurement Model Evaluation 172
Summary 185
Review Questions 186
Critical Thinking Questions 187
Key Terms 187
Suggested Readings 188
Chapter 6: Assessing PLS-SEM Results Part III: Evaluation of
the Structural Model 190
Chapter Preview 190
Stage 6: Assessing PLS-SEM Structural Model Results 191
Step 1: Collinearity Assessment 192
Step 2: Structural Model Path Coefficients 195
Step 3: Coefficient of Determination (R2 Value) 198
Step 4: Effect Size f1 201
Step 5: Blindfolding and Predictive Relevance Q2 202
Step 6: Effect Size q2 207
Case Study Illustration—How Are PLS-SEM Structural Model
Results Reported? 209
Summary 221
Review Questions 223
Critical Thinking Questions 223
Key Terms 224
Suggested Readings 224
Chapter 7: Mediator and Moderator Analysis 227
Chapter Preview 227
Mediation 228
Introduction 228
Types of Mediation Effects 232
Testing Mediating Effects 235
Measurement Model Evaluation in Mediation
Analysis 235
Multiple Mediation 236
Case Study Illustration—Mediation 238
Moderation 243
Introduction 243
Types of Moderator Variables 244
Modeling Moderating Effects 246
Creating the Interaction Term 248
Results Interpretation 256
Moderated Mediation and Mediated Moderation 259
Case Study Illustration—Moderation 262
Summary 271
Review Questions 272
Critical Thinking Questions 272
Key Terms 273
Suggested Readings 273
Chapter 8: Outlook on Advanced Methods 275
Chapter Preview 275
Importance-Performance Map Analysis 276
Hierarchical Component Models 281
Confirmatory Tetrad Analysis 285
Dealing With Observed and Unobserved Heterogeneity 290
Multigroup Analysis 291
Uncovering Unobserved Heterogeneity 295
Measurement Model Invariance 298
Consistent Partial Least Squares 300
Summary 306
Review Questions 308
Critical Thinking Questions 309
Key Terms 309
Suggested Readings 310
Glossary 312
References 331
Author Index 346
Subject Index 350
Visit the companion site for this book at http://study.sagepub.com/
hairprimer2e.
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| indexdate | 2024-07-10T07:29:28Z |
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| isbn | 9781483377445 |
| language | English |
| lccn | 016005380 |
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| physical | xx, 363 Seiten Illustrationen, Diagramme |
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| spelling | Hair, Joseph F. 1944- Verfasser (DE-588)135615828 aut A primer on partial least squares structural equation modeling (PLS-SEM) Joseph F. Hair, Jr. (Kennesaw State University) ; G. Tomas M. Hult (Michigan State University) ; Christian M. Ringle (Hamburg University of Technology, Germany and the University of Newcastle, Australia) ; Marko Sarstedt (Otto-von Guericke University, Magdeburg, German, and the University of Newcastle, Australia) Second edition Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne SAGE [2017] © 2017 xx, 363 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Literaturverzeichnis Seite 331-349 Least squares Structural equation modeling Methode der kleinsten Quadrate (DE-588)4038974-1 gnd rswk-swf Methode der partiellen kleinsten Quadrate (DE-588)4591652-4 gnd rswk-swf Strukturgleichungsmodell (DE-588)4252999-2 gnd rswk-swf Methode der kleinsten Quadrate (DE-588)4038974-1 s Strukturgleichungsmodell (DE-588)4252999-2 s DE-604 Methode der partiellen kleinsten Quadrate (DE-588)4591652-4 s 1\p DE-604 Hult, G. Tomas M. 1967- Verfasser (DE-588)128685832 aut Ringle, Christian M. 1974- Verfasser (DE-588)122291557 aut Sarstedt, Marko 1979- Verfasser (DE-588)136486576 aut Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028998037&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hair, Joseph F. 1944- Hult, G. Tomas M. 1967- Ringle, Christian M. 1974- Sarstedt, Marko 1979- A primer on partial least squares structural equation modeling (PLS-SEM) Least squares Structural equation modeling Methode der kleinsten Quadrate (DE-588)4038974-1 gnd Methode der partiellen kleinsten Quadrate (DE-588)4591652-4 gnd Strukturgleichungsmodell (DE-588)4252999-2 gnd |
| subject_GND | (DE-588)4038974-1 (DE-588)4591652-4 (DE-588)4252999-2 |
| title | A primer on partial least squares structural equation modeling (PLS-SEM) |
| title_auth | A primer on partial least squares structural equation modeling (PLS-SEM) |
| title_exact_search | A primer on partial least squares structural equation modeling (PLS-SEM) |
| title_full | A primer on partial least squares structural equation modeling (PLS-SEM) Joseph F. Hair, Jr. (Kennesaw State University) ; G. Tomas M. Hult (Michigan State University) ; Christian M. Ringle (Hamburg University of Technology, Germany and the University of Newcastle, Australia) ; Marko Sarstedt (Otto-von Guericke University, Magdeburg, German, and the University of Newcastle, Australia) |
| title_fullStr | A primer on partial least squares structural equation modeling (PLS-SEM) Joseph F. Hair, Jr. (Kennesaw State University) ; G. Tomas M. Hult (Michigan State University) ; Christian M. Ringle (Hamburg University of Technology, Germany and the University of Newcastle, Australia) ; Marko Sarstedt (Otto-von Guericke University, Magdeburg, German, and the University of Newcastle, Australia) |
| title_full_unstemmed | A primer on partial least squares structural equation modeling (PLS-SEM) Joseph F. Hair, Jr. (Kennesaw State University) ; G. Tomas M. Hult (Michigan State University) ; Christian M. Ringle (Hamburg University of Technology, Germany and the University of Newcastle, Australia) ; Marko Sarstedt (Otto-von Guericke University, Magdeburg, German, and the University of Newcastle, Australia) |
| title_short | A primer on partial least squares structural equation modeling (PLS-SEM) |
| title_sort | a primer on partial least squares structural equation modeling pls sem |
| topic | Least squares Structural equation modeling Methode der kleinsten Quadrate (DE-588)4038974-1 gnd Methode der partiellen kleinsten Quadrate (DE-588)4591652-4 gnd Strukturgleichungsmodell (DE-588)4252999-2 gnd |
| topic_facet | Least squares Structural equation modeling Methode der kleinsten Quadrate Methode der partiellen kleinsten Quadrate Strukturgleichungsmodell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028998037&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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