[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-45438-en":3,"doc-seo-45438-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},45438,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Hair Book 2017 PLS-SEM Second Edition Primer","The primer provides a structured introduction to Partial Least Squares Structural Equation Modeling (PLS-SEM), covering the full workflow from specifying structural and measurement models to estimating paths and evaluating results. It explains mediation and moderation, compares PLS-SEM with CB-SEM and regression approaches based on sum scores, and details model assessment using reflective and formative measurement criteria, reliability, validity, and structural diagnostics such as R², f², and predictive relevance (Q²).","A Primer on  \nPartial Least Squares Structural Equation Modeling (PLS-SEM)  \nSecond Edition  \nJoseph F. Hair, Jr. Kennesaw State University  \nG. Tomas M. Hult Michigan State University  \nChristian M. Ringle  \nHamburg University of Technology, Germany, and The University of Newcastle, Australia  \nMarko Sarstedt  \nOtto-von-Guericke University, Magdeburg, Germany, and The University of Newcastle, Australia  \nCopyright © 2017 by SAGE Publications, Inc. Printed in the United States of America  \nLibrary of Congress Cataloging-in-Publication Data Names: Hair, Joseph F.  \nTitle: A primer on partial least squares structural equation modeling (PLS-SEM) / Joseph F. Hair, Jr., Kennesaw State University, USA [and three others] .  \nDescription: Second edition. | Los Angeles : Sage,[2017] | Includes bibliographical references and index.  \nIdentifiers: LCCN 2016005380 | ISBN 9781483377445 (pbk.)  \nSubjects: LCSH: Least squares. | Structural equation modeling.  \nClassification: LCC QA275 .P88 2017 | DDC 511/ .42—dc23 LC record available at [http://lccn.loc.gov/2016005380](http://lccn.loc.gov/2016005380)  \nBrief Contents  \nPreface xi  \nChapter 1: An Introduction to Structural  \nEquation Modeling 1  \nChapter 2: Specifying the Path Model and  \nExamining Data 36  \nChapter 3: Path Model Estimation 81  \nChapter 4: Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models 104  \nChapter 5: Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models 137  \nChapter 6: Assessing PLS-SEM Results Part III: Evaluation of the Structural Model 190  \nChapter 7: Mediator and Moderator Analysis 227  \nChapter 8: Outlook on Advanced Methods 275  \nGlossary 312  \nReferences 331  \nAuthor Index 346  \nSubject Index 350  \ncompanion site [http://study.sagepub.com/ hairprimer2e](http://study.sagepub.com/ hairprimer2e).  \nContents  \nPreface xi  \nChapter 1: An Introduction to Structural Equation Modeling 1  \nChapter Preview 1  \nWhat Is Structural Equation Modeling? 2  \nConsiderations in Using Structural Equation Modeling 4 Composite Variables 5  \nMeasurement 5  \nMeasurement Scales 7  \nCoding 9  \nData Distributions 10  \nStructural Equation Modeling With Partial Least Squares Path Modeling 11  \nPath Models With Latent Variables 11  \nMeasurement Theory 13  \nStructural Theory 14  \nPLS-SEM, CB-SEM, and Regressions Based on Sum Scores 14 Data Characteristics 22  \nModel Characteristics 27  \nOrganization of Remaining Chapters 29  \nSummary 31  \nReview Questions 33  \nCritical Thinking Questions 33  \nKey Terms 34  \nSuggested Readings 35  \nChapter 2: Specifying the Path Model and  \nExamining Data 36  \nChapter Preview 36  \nStage 1: Specifying the Structural Model 37  \nMediation 39  \nModeration 41  \nHigher-Order and Hierarchical Component Models 43  \nStage 2: Specifying the Measurement Models 44  \nReflective and Formative Measurement Models 46 Single-Item Measures and Sum Scores 51  \nStage 3: Data Collection and Examination 56  \nMissing Data 56  \nSuspicious Response Patterns 58  \nOutliers 59  \nData Distribution 60  \nCase Study Illustration—Specifying the PLS-SEM Model 62 Application of Stage 1: Structural Model  \nSpecification 63  \nApplication of Stage 2: Measurement Model Specification 64  \nApplication of Stage 3: Data Collection and Examination 66  \nPath Model Creation Using the SmartPLS Software 68 Summary 76  \nReview Questions 78  \nCritical Thinking Questions 78  \nKey Terms 79  \nSuggested Readings 80  \nChapter 3: Path Model Estimation 81  \nChapter Preview 81  \nStage 4: Model Estimation and the PLS-SEM Algorithm 82 How the Algorithm Works 82  \nStatistical Properties 86  \nAlgorithmic Options and Parameter Settings to Run the Algorithm 89  \nResults 91  \nCase Study Illustration—PLS Path Model Estimation (Stage 4) 92  \nModel Estimation 93  \nEstimation Results 95  \nSummary 99  \nReview Questions 101  \nCritical Thinking Questions 102  \nKey Terms 102  \nSuggested Readings 102  \nChapter 4: Assessing PLS-SEM Results Part I: Evaluation of Reflective Measur","cbCaifeK1tdkLym2","https://ap.wps.com/l/cbCaifeK1tdkLym2","pdf",4951377,3,1,375,"English","en",105,"# Preface\n# Chapter 1: An Introduction to Structural Equation Modeling\n## What Is Structural Equation Modeling?\n## Structural Equation Modeling With Partial Least Squares Path Modeling\n# Chapter 2: Specifying the Path Model and Examining Data\n## Stage 1: Specifying the Structural Model\n## Stage 2: Specifying the Measurement Models\n## Stage 3: Data Collection and Examination\n# Chapter 3: Path Model Estimation\n## Stage 4: Model Estimation and the PLS-SEM Algorithm\n# Chapter 4: Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models\n## Stage 5a: Assessing Results of Reflective Measurement Models\n# Chapter 5: Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models\n## Stage 5b: Assessing Results of Formative Measurement Models\n# Chapter 6: Assessing PLS-SEM Results Part III: Evaluation of the Structural Model\n## Stage 6: Assessing PLS-SEM Structural Model Results\n# Chapter 7: Mediator and Moderator Analysis\n## Mediation","[{\"question\":\"What stages does the PLS-SEM workflow in this primer cover?\",\"answer\":\"It organizes the process into stages: specifying the structural model, specifying measurement models, collecting and examining data, estimating the model with the PLS-SEM algorithm, and then assessing results for reflective, formative, and structural models.\"},{\"question\":\"How are reflective and formative measurement models evaluated?\",\"answer\":\"Reflective measurement models are evaluated using internal consistency reliability and convergent and discriminant validity. Formative measurement models are assessed via convergent validity, collinearity checks, and the significance and relevance of formative indicators using procedures such as bootstrapping.\"},{\"question\":\"What metrics are used to evaluate the structural model results?\",\"answer\":\"Structural assessment includes collinearity assessment, path coefficients, R² (coefficient of determination), effect size f², blindfolding and predictive relevance Q², and effect size q².\"}]",1783459502,945,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"hair-book-2017-pls-sem-second-edition-primer","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/hair-book-2017-pls-sem-second-edition-primer/45438/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-20","2026-07-07",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What stages does the PLS-SEM workflow in this primer cover?","Question",{"text":75,"@type":76},"It organizes the process into stages: specifying the structural model, specifying measurement models, collecting and examining data, estimating the model with the PLS-SEM algorithm, and then assessing results for reflective, formative, and structural models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are reflective and formative measurement models evaluated?",{"text":80,"@type":76},"Reflective measurement models are evaluated using internal consistency reliability and convergent and discriminant validity. Formative measurement models are assessed via convergent validity, collinearity checks, and the significance and relevance of formative indicators using procedures such as bootstrapping.",{"name":82,"@type":73,"acceptedAnswer":83},"What metrics are used to evaluate the structural model results?",{"text":84,"@type":76},"Structural assessment includes collinearity assessment, path coefficients, R² (coefficient of determination), effect size f², blindfolding and predictive relevance Q², and effect size q².","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]