A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)SAGE Publications, 6. jul. 2021 - 384 sider 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. |
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About the Authors | |
Chapter 3 Path Model Estimation | |
Evaluation of the Reflective | |
Evaluation of the Formative | 1 |
Orthogonalizing Approach | 2 |
Chapter 1 An Introduction to Structural Equation Modeling | 3 |
Chapter 2 Specifying the Path Model and Examining Data | 4 |
Moderated Mediation and Mediated Moderation | 211 |
Case Study IllustrationModeration | 213 |
Summary | 217 |
Review Questions 7 Critical Thinking Questions 8 Key Terms | 246 |
Suggested Readings | 247 |
Chapter 8 Outlook on Advanced Methods 1 Chapter Preview 2 ImportancePerformance Map Analysis 3 Necessary Condition Analysis 4 HigherOrde... | 249 |
Confirmatory Tetrad Analysis | 253 |
Examining Endogeneity | 255 |
Chapter 3 Path Model Estimation | 5 |
Evaluation of the Reflective | 6 |
Evaluation of the Formative Measurement Models | 7 |
Evaluation of the Structural Model | 8 |
Chapter 7 Mediator and Moderator Analysis | 9 |
Chapter 8 Outlook on Advanced Methods | 10 |
Evaluation of the Structural | 158 |
Model Evaluation | 162 |
Results Interpretation | 166 |
Chapter 7 Mediator and Moderator Analysis | 197 |
Treating Observed and Unobserved Heterogeneity 1 Multigroup Analysis | 256 |
Uncovering Unobserved Heterogeneity | 258 |
Measurement Model Invariance | 259 |
Consistent PLSSEM 10 Summary | 260 |
Review Questions 12 Critical Thinking Questions | 262 |
Key Terms | 263 |
Suggested Readings | 291 |
Glossary | 293 |
References | 320 |
2021 | |
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apply approach Becker bootstrap confidence intervals bootstrap samples CB-SEM Chapter Cheah collinearity COMP composite compute concept consider construct scores control variables convergent validity corporate reputation correlations criterion CUSA CUSL customer satisfaction data set Diamantopoulos discriminant validity endogenous construct endogenous latent variable evaluation example exogenous formative indicators formative measurement models Gudergan guidelines Hair Henseler higher-order constructs HTMT indicator variables indirect effect interaction term Journal latent variable scores least squares structural lower-order components Marketing measurement theory method missing values model estimation Modeling Window moderator variable multigroup analysis Nitzl option outer loadings outer weights parameter partial least squares path coefficients PLS path model PLS-SEM algorithm PLS-SEM results PLSpredict predictive power procedure R² values reflective measurement models regression Rigdon Ringle Sarstedt scale Shmueli shown in Exhibit significance level single-item measures SmartPLS SmartPLS software specific squares structural equation statistical statistical power structural equation modeling target construct tetrads theoretical theory variance