Synopses & Reviews
This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM.
Review
"Perfectly addresses the needs of social scientists like me without formal training in mathematical statistics....Succeeds in conveying the conceptual complexity of SEM without sacrificing the reader's understanding of the ramifications of the model's assumptions....Can be read by any graduate in psychology or even by keen undergraduates interested in exploring new vistas. Yet it will also constitute a surprisingly good read for experienced researchers in search of some refreshing insights in their favorite techniques....A real tour de force....Succeeds in reconciling comprehensiveness and comprehensibility."--
The Psychologist"Structural equation modeling is an important tool in research in the social sciences, and this updated edition keeps pace with the newest methodologies, including providing a SEM-text web page with free access to data and program files for the examples here."--SciTech Book News
"...the greatest strength of this book is Kline's ability to present materials in an engaging, accessible manner. In nearly all situations, Kline is able to describe even the more complex material in practical, (relatively) jargon-free terms....In this regard, this book is unparalleled, and I suspect that this strength alone will make this the book of choice for many who are eager to learn SEM but who do not possess extensive quantitative backgrounds....it provides better treatment of some of the more advanced topics....This book represents a well-written introduction to SEM that will be accessible to readers with minimal quantitative background....This book could be readily adapted to courses for students with a basic understanding of correlation and regression or as part of a course for more advanced students."-- APA PsycCRITIQUES
"...Perfectly addresses the need of social scientists like me without formal training in mathematical statistics....the book is rich in examples from different fields....It can be read by any graduate [student] in psychology or even by keen undergraduates interested in exploring new vistas....it will also constitute a surprisingly good read for experienced researchers in search of some refreshing insights in their favourite techniques....a real tour de force."--The Psychologist
"This wonderfully written book is an impressive introduction to structural equation models (SEM) containing a sharp mix of expert analysis and observations....Contains important resources for both theoretical and applied researchers interested in SEMs...appropriate as a text for graduate students and a reference for researchers, providing both audiences with valuable insight into the subject matter...A well-written book that will be useful for graduate students and researchers."--Journal of the American Statistical Association
Review
A good book has gotten much better. Since the first edition appeared in 1998, Kline's book has been the only one I assign in my SEM course. It easily surpassed its competitors in clarity and accessibility, without sacrificing accuracy and completeness. This new edition is a major upgrade, with substantial reworking of the original chapters to add depth and clarity, as well as new chapters on topics like latent growth curve modeling and hierarchical linear modeling./m-/Paul D. Allison, Department of Sociology, University of Pennsylvania
"An outstanding introduction to SEM, geared toward the beginner in this area. The relaxed, conversational writing style makes access to this complex material pain-free and easy. The book strikes a perfect balance between easy reading and the presentation of technical detail and technical terms. I recommend this book strongly, and I will use it the next time I teach SEM."-Alexander von Eye, Department of Psychology, Michigan State University
"An excellent choice....The book strikes a nice balance between the technical and practical aspects of SEM. A particular strength is that the examples include correlations and standard deviations, so that readers can execute the examples using various SEM software."-Fred E. Markowitz, Department of Sociology, Northern Illinois University
"An extremely important resource for both the theoretical and applied researcher interested in SEM. Dr. Kline's work contains information that is insightful, accurate, and practical. I recommend this book as both a text for graduate-level seminars and a technical resource for research."-Larry Price, College of Education, Texas State University-San Marcos
"The approach to SEM is wonderful. Difficult concepts and issues are explained in a way
that is accessible to those who are not trained as quantitative methodologists. The blend
of conceptual and procedural explanations is generally very good, and the discussions and explanations are technically accurate."-Xitao Fan, Curry School of Education, University of Virginia
Review
"I would strongly recommend this book for use as a primary text in any SEM course. It offers a clear, applied presentation of complicated SEM techniques for a wide array of audiences with various abilities. The text would be beneficial for students with a limited background in theoretical statistics, as well as those with a strong understanding of the theoretical underpinnings of SEM. I often refer to this text in my everyday work, due to the clarity with which the material is presented."--Greg Welch, PhD, Nebraska Center for Research on Children, Youth, Families, and Schools, University of Nebraska-Lincoln
"The skill and knowledge sets for evaluating and using SEM have become important components of social science education. Kline provides a text that is accessible for graduate students, practitioners, and researchers who are not intimately familiar with SEM techniques. In addition, he effortlessly summarizes current information that researchers who already use SEM should have. The reorganization of the material, new topic boxes, new Web page, and updated technical information enhance an already great resource. A major strength of the book is the individual chapter examples with explanation of the values provided from a variety of statistical analysis packages. I will continue to use this third edition as the primary text in my SEM course, and highly recommend it as both a text and a technical resource."--James B. Schreiber, PhD, Center for Advancing the Study of Teaching and Learning, Duquesne University
"In the third edition, Kline has improved the pedagogical value of his book relative to prior editions and to other SEM books. The many exercises help a reader understand how to apply important chapter concepts, making the book not only useful for an SEM course, but also an effective resource for self-study. The Web page featuring complete computer syntax and data for the examples is very helpful. Other new material further supports a readers understanding of SEM; for example, Chapter 2 provides more information on multiple regression/correlation, and the chapter on data preparation has been expanded to cover important topics such as positive-definite matrices, which are among the most common problems researchers experience when using SEM. This edition also provides more information on available SEM software, including an entire chapter on the use of computer software."--Craig Wells, PhD, School of Education, University of Massachusetts-Amherst "Chapters 2 and 3 review important concepts in multiple regression and data screening, both of which are critical to understand before learning SEM. From an instructor's perspective, I enjoyed reading these chapters very much. They are well written, logically organized, and easy to follow. Another strength of the book is the thorough and comprehensive reference list on various topics."--Duan Zhang, PhD, College of Education, University of Denver "I am excited about this book. The coverage is excellent and the writing style is friendly and direct, with a subtle humor that I find refreshing. I especially like the new topic boxes in the third edition, most of which discuss issues that I have had to address separately in lectures."--Jacob Marszalek, PhD, Division of Counseling and Educational Psychology, University of Missouri-Kansas City "If you didn't think a statistics text could be riveting, read this book! The first chapter covers basic statistical concepts in better detail and more clearly than other texts that are meant to be general introductions. Discussing extremely helpful articles that have examined SEM research, Kline identifies and provides empirical evidence of potential problems with using and reporting SEM. This helps readers understand what to do (and what not to do) from the very beginning. I love it that one of the goals is to help readers avoid common mistakes in SEM."--Debbie Hahs-Vaughn, PhD, Department of Educational Research, Technology, and Leadership, University of Central Florida "This is now the #1 book I will recommend to students and substantive researchers (who are not quantitative specialists) who want to learn SEM! Compared to most SEM books that I have seen, this one strikes a better balance between accessibility and breadth. In the third edition, Kline not only has updated the material, but has substantially improved it. He adds more depth to certain topics--such as estimation, in Chapter 7--and covers some intermediate-to-advanced topics not described in the previous edition, all at a level appropriate for beginners."--Noel A. Card, PhD, Division of Family Studies and Human Development, University of Arizona "A terrific introduction to SEM. Kline offers some of the basics and does so in a way that is quite approachable for students. I have recommended this text to a number of people who were just beginning to use SEM, and will continue to do so."--R. Lance Holbert, PhD, School of Communications, Ohio State University
"Of all the introductory SEM texts, this one is the most interesting to read. Anyone who has taken a course in basic algebra or introductory statistics will be able to understand the ideas and work through the exercises, and those who work their way through the book will have a good foundation in SEM and will be able to use it effectively."--David F. Gillespie, PhD, George Warren Brown School of Social Work, Washington University in St. Louis
Synopsis
This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.
Synopsis
This bestselling text provides a balance between the technical and practical aspects of structural equation modeling (SEM). Using clear and accessible language, Rex B. Kline covers core techniques, potential pitfalls, and applications across the behavioral and social sciences. Some more advanced topics are also covered, including estimation of interactive effects of latent variables and multilevel SEM. The companion Web page offers downloadable syntax, data, and output files for each detailed example for EQS, LISREL, and Mplus, allowing readers to view the results of the same analysis generated by three different computer tools.
New to This Edition *Thoroughly revised and restructured to follow the phases of most SEM analyses. *Syntax, data, and output files for all detailed research examples are now provided online. *Chapter on computer tools. *Exercises with answers, which support self-study. *Topic boxes on specialized issues, such as dealing with problems in the analysis; the assessment of construct measurement reliability; and more. *Updated coverage of a more rigorous approach to hypothesis and model testing; the evaluation of measurement invariance; and more.
*”Troublesome” examples have been added to provide a context for discussing how to handle various problems that can crop up in SEM analyses.
About the Author
Rex B. Kline, PhD, is an associate professor of Psychology at Concordia University in Montréal. Since earning a PhD in psychology, his areas of research and writing have included the psychometric evaluation of cognitive abilities, child clinical assessment, structural equation modeling, and usability engineering in computer science. Dr. Kline has published three books and more than 40 articles in research journals and is the coauthor of a teacher-informant rating scale for referred children.
Table of Contents
I. Concepts and Tools1. IntroductionThe Book's WebsitePedagogical ApproachGetting Ready to Learn about SEMCharacteristics of SEMWidespread Enthusiasm, but with a Cautionary TaleFamily History and a Reminder about ContextExtended Latent Variable FamiliesPlan of the BookSummary2. Fundamental ConceptsMultiple RegressionPartial Correlation and Part CorrelationOther Bivariate CorrelationsLogistic RegressionStatistical TestsTOPIC BOX 2.1. The "Big Five" Misinterpretations of Statistical SignificanceBootstrappingSummaryRecommended ReadingsExercises3. Data PreparationForms of Input DataPositive DefinitenessTOPIC BOX 3.1. Causes of Nonpositive Definiteness and SolutionsData ScreeningSelecting Good Measures and Reporting about ThemSummaryRecommended ReadingsExercises4. Computer ToolsEase of Use, Not Suspension of JudgmentHuman-Computer InteractionTOPIC BOX 4.1. Graphical Isn't Always BetterCore SEM Programs and Book Website ResourcesOther Computer ToolsSummaryRecommended ReadingsII. Core Techniques5. SpecificationSteps of SEMModel Diagram SymbolsSpecification ConceptsPath Analysis ModelsCFA ModelsStructural Regression ModelsExploratory SEMSummaryRecommended ReadingsExercises6. IdentificationGeneral RequirementsUnique EstimatesRule for Recursive Structural ModelsRules for Nonrecursive Structural ModelsRules for Standard CFA ModelsRules for Nonstandard CFA ModelsRules for SR ModelsA Healthy Perspective on IdentificationEmpirical UnderidentificationManaging Identification ProblemsSummaryRecommended ReadingsExercisesAPPENDIX 6.A. Evaluation of the Rank Condition7. EstimationMaximum Likelihood EstimationTOPIC BOX 7.1. Two-Stage Least Squares EstimationDetailed ExampleBrief Example with a Start Value ProblemFitting Models to Correlation MatricesAlternative EstimatorsA Healthy Perspective on EstimationSummaryRecommended ReadingsExercisesAPPENDIX 7.A. Start Value Suggestions for Structural ModelsAPPENDIX 7.B. Effect Decomposition in Nonrecursive Models and the Equilibrium Assumption8. Hypothesis TestingEyes on the PrizeState of Practice, State of MindA Healthy Perspective on Fit StatisticsTypes of Fit Statistics and "Golden Rules"Model Chi-SquareApproximate Fit IndexesVisual Summaries of FitRecommended Approach to Model Fit EvaluationDetailed ExampleTesting Hierarchical ModelsComparing Nonhierarchical ModelsPower AnalysisEquivalent and Near-Equivalent ModelsSummaryRecommended ReadingsExercises9. Measurement Models and Confirmatory Factor AnalysisNaming and Reification FallaciesEstimation of CFA ModelsDetailed ExampleRespecification of Measurement ModelsSpecial Topics and TestsTOPIC BOX 9.1. Reliability of Construct MeasurementItems as Indicators and Other Methods for Analyzing ItemsEstimated Factor ScoresEquivalent CFA ModelsHierarchical CFA ModelsModels for Multitrait–Multimethod DataMeasurement Invariance and Multiple-Sample CFASummaryRecommended ReadingsExercisesAPPENDIX 9.A. Start Value Suggestions for Measurement ModelsAPPENDIX 9.B. Constraint Interaction in Measurement Models10. Structural Regression ModelsAnalyzing SR ModelsEstimation of SR ModelsDetailed ExampleEquivalent SR ModelsSingle Indicators in Partially Latent SR ModelsCause Indicators and Formative MeasurementTOPIC BOX 10.1. Partial Least Squares Path ModelingInvariance Testing of SR ModelsReporting Results of SEM AnalysesSummaryRecommended ReadingsExercisesAPPENDIX 10.A. Constraint Interaction in SR ModelsIII. Advanced Techniques, Avoiding Mistakes11. Mean Structures and Latent Growth ModelsLogic of Mean StructuresIdentification of Mean StructuresEstimation of Mean StructuresLatent Growth ModelsStructured Means in Measurement ModelsMIMIC Models as an Alternative to Multiple-Sample AnalysisSummaryRecommended Readings12. Interaction Effects and Multilevel SEMInteraction Effects of Observed VariablesInteraction Effects in Path ModelsMediation and Moderation TogetherInteractive Effects of Latent VariablesEstimation with the Kenny-Judd MethodAlternative Estimation MethodsRationale of Multilevel AnalysisBasic Multilevel TechniquesConvergence of SEM and MLMMultilevel SEMSummaryRecommended Readings13. How to Fool Yourself with SEMTripping at the Starting Line: SpecificationImproper Care and Feeding: DataChecking Critical Judgment at the Door: Analysis and RespecificationThe Garden Path: InterpretationSummaryRecommended Readings*Suggested Answers to Exercises