Title 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, Germany, and The University of Newcastle, Australia

Edition Second edition Published Thousand Oaks, California : Sage, [2017] ©2017

Copies

Location	Call no.	Vol.	Availability
WATERFT	511.42 Hai/Pop 2017		AVAILABLE

Description xx, 363 pages : illustrations ; 23 cm regular print Contents Contents note continued: Case Study Illustration---Evaluation of Formative Measurement Models -- Extending the Simple Path Model -- Reflective Measurement Model Evaluation -- Formative Measurement Model Evaluation -- Summary -- Review Questions -- Critical Thinking Questions -- Key Terms -- Suggested Readings -- ch. 6 Assessing PLS-SEM Results Part III: Evaluation of the Structural Model -- Chapter Preview -- Stage 6 Assessing PLS-SEM Structural Model Results -- Step 1 Collinearity Assessment -- Step 2 Structural Model Path Coefficients -- Step 3 Coefficient of Determination (R2 Value) -- Step 4 Effect Size f2 -- Step 5 Blindfolding and Predictive Relevance Q2 -- Step 6 Effect Size q2 -- Case Study Illustration---How Are PLS-SEM Structural Model Results Reported? -- Summary -- Review Questions -- Critical Thinking Questions -- Key Terms -- Suggested Readings -- ch. 7 Mediator and Moderator Analysis -- Chapter Preview -- Mediation -- Introduction -- Types of Mediation Effects -- Contents note continued: Single-Item Measures and Sum Scores -- Stage 3 Data Collection and Examination -- Missing Data -- Suspicious Response Patterns -- Outliers -- Data Distribution -- Case Study Illustration---Specifying the PLS-SEM Model -- Application of Stage 1 Structural Model Specification -- Application of Stage 2 Measurement Model Specification -- Application of Stage 3 Data Collection and Examination -- Path Model Creation Using the SmartPLS Software -- Summary -- Review Questions -- Critical Thinking Questions -- Key Terms -- Suggested Readings -- ch. 3 Path Model Estimation -- Chapter Preview -- Stage 4 Model Estimation and the PLS-SEM Algorithm -- How the Algorithm Works -- Statistical Properties -- Algorithmic Options and Parameter Settings to Run the Algorithm -- Results -- Case Study Illustration---PLS Path Model Estimation (Stage 4) -- Model Estimation -- Estimation Results -- Summary -- Review Questions -- Critical Thinking Questions -- Key Terms -- Contents note continued: Testing Mediating Effects -- Measurement Model Evaluation in Mediation Analysis -- Multiple Mediation -- Case Study Illustration---Mediation -- Moderation -- Introduction -- Types of Moderator Variables -- Modeling Moderating Effects -- Creating the Interaction Term -- Results Interpretation -- Moderated Mediation and Mediated Moderation -- Case Study Illustration---Moderation -- Summary -- Review Questions -- Critical Thinking Questions -- Key Terms -- Suggested Readings -- ch. 8 Outlook on Advanced Methods -- Chapter Preview -- Importance-Performance Map Analysis -- Hierarchical Component Models -- Confirmatory Tetrad Analysis -- Dealing With Observed and Unobserved Heterogeneity -- Multigroup Analysis -- Uncovering Unobserved Heterogeneity -- Measurement Model Invariance -- Consistent Partial Least Squares -- Summary -- Review Questions -- Critical Thinking Questions -- Key Terms -- Suggested Readings Contents note continued: Suggested Readings -- ch. 4 Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models -- Chapter Preview -- Overview of Stage 5: Evaluation of Measurement Models -- Stage 5a Assessing Results of Reflective Measurement Models -- Internal Consistency Reliability -- Convergent Validity -- Discriminant Validity -- Case Study Illustration---Reflective Measurement Models -- Running the PLS-SEM Algorithm -- Reflective Measurement Model Evaluation -- Summary -- Review Questions -- Critical Thinking Questions -- Key Terms -- Suggested Readings -- ch. 5 Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models -- Chapter Preview -- Stage 5b Assessing Results of Formative Measurement Models -- Step 1 Assess Convergent Validity -- Step 2 Assess Formative Measurement Models for Collinearity Issues -- Step 3 Assess the Significance and Relevance of the Formative Indicators -- Bootstrapping Procedure -- Machine generated contents note: ch. 1 An Introduction to Structural Equation Modeling -- Chapter Preview -- What Is Structural Equation Modeling? -- Considerations in Using Structural Equation Modeling -- Composite Variables -- Measurement -- Measurement Scales -- Coding -- Data Distributions -- Structural Equation Modeling With Partial Least Squares Path Modeling -- Path Models With Latent Variables -- Measurement Theory -- Structural Theory -- PLS-SEM, CB-SEM, and Regressions Based on Sum Scores -- Data Characteristics -- Model Characteristics -- Organization of Remaining Chapters -- Summary -- Review Questions -- Critical Thinking Questions -- Key Terms -- Suggested Readings -- ch. 2 Specifying the Path Model and Examining Data -- Chapter Preview -- Stage 1 Specifying the Structural Model -- Mediation -- Moderation -- Higher-Order and Hierarchical Component Models -- Stage 2 Specifying the Measurement Models -- Reflective and Formative Measurement Models -- Summary A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) is a practical guide that provides concise instructions on how to use partial least squares structural equation modeling (PLS-SEM), an evolving statistical technique, to conduct research and obtain solutions. Featuring the latest research, new examples using the SmartPLS software, and expanded discussions throughout, the second edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways Notes Previous edition: 2014 Bibliography Includes bibliographical references and indexes Subject Least squares. Structural equation modeling. Author Hult, G. Tomas M., author Ringle, Christian M., author Sarstedt, Marko, author LC no. 2016005380 ISBN 148337744X 9781483377445

  Permalink