Principles And Practice Of Structural Equation Modeling, Fourth Edition - Kline Rex B. | Libro Guilford Press 12/2015 -

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Principles and Practice of Structural Equation Modeling, Fourth Edition

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Lingua: Inglese
Pubblicazione: 12/2015
Edizione: Edizione nuova, 4° edizione

Note Editore

Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates recent developments such as Pearl's graphing theory and the structural causal model (SCM), measurement invariance, and more. Readers gain a comprehensive understanding of all phases of SEM, from data collection and screening to the interpretation and reporting of the results. Learning is enhanced by exercises with answers, rules to remember, and topic boxes. The companion website supplies data, syntax, and output for the book's examples--now including files for Amos, EQS, LISREL, Mplus, Stata, and R (lavaan).New to This Edition*Extensively revised to cover important new topics: Pearl's graphing theory and the SCM, causal inference frameworks, conditional process modeling, path models for longitudinal data, item response theory, and more.*Chapters on best practices in all stages of SEM, measurement invariance in confirmatory factor analysis, and significance testing issues and bootstrapping.*Expanded coverage of psychometrics.*Additional computer tools: online files for all detailed examples, previously provided in EQS, LISREL, and Mplus, are now also given in Amos, Stata, and R (lavaan).*Reorganized to cover the specification, identification, and analysis of observed variable models separately from latent variable models.User-Friendly Features*Exercises with answers, plus end-of-chapter annotated lists of further reading.*Real examples of troublesome data, demonstrating how to handle typical problems in analyses.*Topic boxes on specialized issues, such as causes of nonpositive definite correlations.*Boxed rules to remember.*Website promoting a learn-by-doing approach, including syntax and data files for six widely used SEM computer tools.


I. Concepts and Tools1. Coming of AgePreparing to Learn SEMDefinition of SEMImportance of TheoryA Priori, but Not Exclusively ConfirmatoryProbabilistic CausationObserved Variables and Latent VariablesData Analyzed in SEMSEM Requires Large SamplesLess Emphasis on Significance TestingSEM and Other Statistical TechniquesSEM and Other Causal Inference FrameworksMyths about SEMWidespread Enthusiasm, but with a Cautionary TaleFamily HistorySummaryLearn More2. Regression FundamentalsBivariate RegressionMultiple RegressionLeft-Out Variables ErrorSuppressionPredictor Selection and EntryPartial and Part CorrelationObserved versus Estimated CorrelationsLogistic Regression and Probit RegressionSummaryLearn MoreExercises3. Significance Testing and BootstrappingStandard ErrorsCritical RatiosPower and Types of Null HypothesesSignificance Testing ControversyConfidence Intervals and Noncentral Test DistributionsBootstrappingSummaryLearn MoreExercises4. Data Preparation and Psychometrics ReviewForms of Input DataPositive DefinitenessExtreme CollinearityOutliersNormalityTransformationsRelative VariancesMissing DataSelecting Good Measures and Reporting about ThemScore ReliabilityScore ValidityItem Response Theory and Item Characteristic CurvesSummaryLearn MoreExercises5. Computer ToolsEase of Use, Not Suspension of JudgmentHuman–Computer InteractionTips for SEM ProgrammingSEM Computer ToolsOther Computer Resources for SEMComputer Tools for the SCMSummaryLearn MoreII. Specification and Identification6. Specification of Observed Variable (Path) ModelsSteps of SEMModel Diagram SymbolsCausal InferenceSpecification ConceptsPath Analysis ModelsRecursive and Nonrecursive ModelsPath Models for Longitudinal DataSummaryLearn MoreExercisesAppendix 6.A. LISREL Notation for Path Models7. Identification of Observed Variable (Path) ModelsGeneral RequirementsUnique EstimatesRule for Recursive ModelsIdentification of Nonrecursive ModelsModels with Feedback Loops and All Possible Disturbance CorrelationsGraphical Rules for Other Types of Nonrecursive ModelsRespecification of Nonrecursive Models that are Not IdentifiedA Healthy Perspective on IdentificationEmpirical UnderidentificationManaging Identification ProblemsPath Analysis Research ExampleSummaryLearn MoreExercisesAppendix 7.A. Evaluation of the Rank Condition8. Graph Theory and the Structural Causal ModelIntroduction to Graph TheoryElementary Directed Graphs and Conditional IndependencesImplications for Regression Analysisd-SeparationBasis SetCausal Directed GraphsTestable ImplicationsGraphical Identification CriteriaInstrumental VariablesCausal MediationSummaryLearn MoreExercisesAppendix 8.A. Locating Conditional Independences in Directed Cyclic GraphsAppendix 8.B. Counterfactual Definitions of Direct and Indirect Effects9. Specification and Identification of Confirmatory Factor Analysis ModelsLatent Variables in CFAFactor AnalysisCharacteristics of EFA ModelsCharacteristics of CFA ModelsOther CFA Specification IssuesIdentification of CFA ModelsRules for Standard CFA ModelsRules for Nonstandard CFA ModelsEmpirical Underidentification in CFACFA Research ExampleAppendix 9.A. LISREL Notation for CFA Models10. Specification and Identification of Structural Regression ModelsCausal Inference with Latent VariablesTypes of SR ModelsSingle IndicatorsIdentification of SR ModelsExploratory SEMSR Model Research ExamplesSummaryLearn MoreExercisesAppendix 10.A. LISREL Notation for SR ModelsIII. Analysis11. Estimation and Local Fit TestingTypes of EstimatorsCausal Effects in Path AnalysisSingle-Equation MethodsSimultaneous MethodsMaximum Likelihood EstimationDetailed ExampleFitting Models to Correlation MatricesAlternative EstimatorsA Healthy Perspective on EstimationSummaryLean MoreExercisesAppendix 11.A. Start Value Suggestions for Structural Models12. Global Fit TestingState of Practice, State of MindA Healthy Perspective on Global Fit StatisticsModel Test StatisticsApproximate Fit IndexesRecommended Approach to Fit EvaluationModel Chi-SquareRMSEACFISRMRTips for Inspecting ResidualsGlobal Fit Statistics for the Detailed ExampleTesting Hierarchical ModelsComparing Nonhierarchical ModelsPower AnalysisEquivalent and Near-Equivalent ModelsSummaryLearn MoreExercisesAppendix 12.A. Model Chi-Squares Printed by LISREL13. Analysis of Confirmatory Factor Analysis ModelsFallacies about Factor or Indicator LabelsEstimation of CFA ModelsDetailed ExampleRespecification of CFA ModelsSpecial Topics and TestsEquivalent CFA ModelsSpecial CFA ModelsAnalyzing Likert-Scale Items as IndicatorsItem Response Theory as an Alternative to CFASummaryLearn MoreExercisesAppendix 13.A. Start Value Suggestions for Measurement ModelsAppendix 13.B. Constraint Interaction in CFA Models14. Analysis of Structural Regression ModelsTwo-Step ModelingFour-Step ModelingInterpretation of Parameter Estimates and ProblemsDetailed ExampleEquivalent Structural Regression ModelsSingle Indicators in a Nonrecursive ModelAnalyzing Formative Measurement Models in SEMSummaryLearn MoreExercisesAppendix 14.A. Constraint Interaction in SR ModelsAppendix 14.B. Effect Decomposition in Nonrecursive Models and the Equilibrium AssumptionAppendix 14.C. Corrected Proportions of Explained Variance for Nonrecursive ModelsIV. Advanced Techniques and Best Practices15. Mean Structures and Latent Growth ModelsLogic of Mean StructuresIdentification of Mean StructuresEstimation of Mean StructuresLatent Growth ModelsDetailed ExampleComparison with a Polynomial Growth ModelExtensions of Latent Growth ModelsSummaryLearn MoreExercises16. Multiple-Samples Analysis and Measurement InvarianceRationale of Multiple-Samples SEMMeasurement InvarianceTesting Strategy and Related IssuesExample with Continuous IndicatorsExample with Ordinal IndicatorsStructural InvarianceAlternative Statistical TechniquesSummaryLearn MoreExercisesAppendix 16.A. Welch–James Test17. Interaction Effects and Multilevel Structural Equation ModelingInteractive Effects of Observed VariablesInteractive Effects in Path AnalysisConditional Process ModelingCausal Mediation AnalysisInteractive Effects of Latent VariablesMultilevel Modeling and SEMSummaryExercisesLearn More18. Best Practices in Structural Equation ModelingResourcesSpecificationIdentificationMeasuresSample and DataEstimationRespecificationTabulationInterpretationAvoid Confirmation BiasBottom Lines and Statistical BeautySummaryLearn MoreSuggested Answers to ExercisesReferencesAuthor IndexSubject IndexAbout the Author


Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montral. Since earning a doctorate in clinical psychology, he has conducted research on the psychometric evaluation of cognitive abilities, behavioral and scholastic assessment of children, structural equation modeling, training of researchers, statistics reform in the behavioral sciences, and usability engineering in computer science. Dr. Kline has published a number of books, chapters, and journal articles in these areas. His website is

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Condizione: Nuovo
Collana: Methodology in the Social Sciences
Dimensioni: 10 x 7 in Ø 2.13 lb
Formato: Brossura
Illustration Notes:illustrations
Pagine Arabe: 534

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