Principles and practice of structural equation modeling

Revised edition of the author's Principles and practice of structural equation modeling, 2011

Includes bibliographical references (pages 489-509) and index

Machine generated contents note: I. Concepts and Tools -- 1. Coming of Age -- Preparing to Learn SEM -- Definition of SEM -- Importance of Theory -- A Priori, but Not Exclusively Confirmatory -- Probabilistic Causation -- Observed Variables and Latent Variables -- Data Analyzed in SEM -- SEM Requires Large Samples -- Less Emphasis on Significance Testing -- SEM and Other Statistical Techniques -- SEM and Other Causal Inference Frameworks -- Myths about SEM -- Widespread Enthusiasm, but with a Cautionary Tale -- Family History -- Summary -- Learn More -- 2. Regression Fundamentals -- Bivariate Regression -- Multiple Regression -- Left-Out Variables Error -- Suppression -- Predictor Selection and Entry -- Partial and Part Correlation -- Observed versus Estimated Correlations -- Logistic Regression and Probit Regression -- Summary -- Learn More -- Exercises -- 3. Significance Testing and Bootstrapping -- Standard Errors -- Critical Ratios -- Power and Types of Null Hypotheses -- Significance Testing Controversy -- Confidence Intervals and Noncentral Test Distributions -- Bootstrapping -- Summary -- Learn More -- Exercises -- 4. Data Preparation and Psychometrics Review -- Forms of Input Data -- Positive Definiteness -- Extreme Collinearity -- Outliers -- Normality -- Transformations -- Relative Variances -- Missing Data -- Selecting Good Measures and Reporting about Them -- Score Reliability -- Score Validity -- Item Response Theory and Item Characteristic Curves -- Summary -- Learn More -- Exercises -- 5. Computer Tools -- Ease of Use, Not Suspension of Judgment -- Human-Computer Interaction -- Tips for SEM Programming -- SEM Computer Tools -- Other Computer Resources for SEM -- Computer Tools for the SCM -- Summary -- Learn More -- II. Specification and Identification -- 6. Specification of Observed Variable (Path) Models -- Steps of SEM -- Model Diagram Symbols -- Causal Inference -- Specification Concepts -- Path Analysis Models -- Recursive and Nonrecursive Models -- Path Models for Longitudinal Data -- Summary -- Learn More -- Exercises -- Appendix 6.A. LISREL Notation for Path Models -- 7. Identification of Observed Variable (Path) Models -- General Requirements -- Unique Estimates -- Rule for Recursive Models -- Identification of Nonrecursive Models -- Models with Feedback Loops and All Possible Disturbance Correlations -- Graphical Rules for Other Types of Nonrecursive Models -- Respecification of Nonrecursive Models that are Not Identified -- A Healthy Perspective on Identification -- Empirical Underidentification -- Managing Identification Problems -- Path Analysis Research Example -- Summary -- Learn More -- Exercises -- Appendix 7.A. Evaluation of the Rank Condition -- 8. Graph Theory and the Structural Causal Model -- Introduction to Graph Theory -- Elementary Directed Graphs and Conditional Independences -- Implications for Regression Analysis -- d-Separation -- Basis Set -- Causal Directed Graphs -- Testable Implications -- Graphical Identification Criteria -- Instrumental Variables -- Causal Mediation -- Summary -- Learn More -- Exercises -- Appendix 8.A. Locating Conditional Independences in Directed Cyclic Graphs -- Appendix 8.B. Counterfactual Definitions of Direct and Indirect Effects -- 9. Specification and Identification of Confirmatory Factor Analysis Models -- Latent Variables in CFA -- Factor Analysis -- Characteristics of EFA Models -- Characteristics of CFA Models -- Other CFA Specification Issues -- Identification of CFA Models -- Rules for Standard CFA Models -- Rules for Nonstandard CFA Models -- Empirical Underidentification in CFA -- CFA Research Example -- Appendix 9.A. LISREL Notation for CFA Models -- 10. Specification and Identification of Structural Regression Models -- Causal Inference with Latent Variables -- Types of SR Models -- Single Indicators -- Identification of SR Models -- Exploratory SEM -- SR Model Research Examples -- Summary -- Learn More -- Exercises -- Appendix 10.A. LISREL Notation for SR Models -- III. Analysis -- 11. Estimation and Local Fit Testing -- Types of Estimators -- Causal Effects in Path Analysis -- Single-Equation Methods -- Simultaneous Methods -- Maximum Likelihood Estimation -- Detailed Example -- Fitting Models to Correlation Matrices -- Alternative Estimators -- A Healthy Perspective on Estimation -- Summary -- Lean More -- Exercises -- Appendix 11.A. Start Value Suggestions for Structural Models -- 12. Global Fit Testing -- State of Practice, State of Mind -- A Healthy Perspective on Global Fit Statistics -- Model Test Statistics -- Approximate Fit Indexes -- Recommended Approach to Fit Evaluation -- Model Chi-Square -- RMSEA -- CFI -- SRMR -- Tips for Inspecting Residuals -- Global Fit Statistics for the Detailed Example -- Testing Hierarchical Models -- Comparing Nonhierarchical Models -- Power Analysis -- Equivalent and Near-Equivalent Models -- Summary -- Learn More -- Exercises -- Appendix 12.A. Model Chi-Squares Printed by LISREL -- 13. Analysis of Confirmatory Factor Analysis Models -- Fallacies about Factor or Indicator Labels -- Estimation of CFA Models -- Detailed Example -- Respecification of CFA Models -- Special Topics and Tests -- Equivalent CFA Models -- Special CFA Models -- Analyzing Likert-Scale Items as Indicators -- Item Response Theory as an Alternative to CFA -- Summary -- Learn More -- Exercises -- Appendix 13.A. Start Value Suggestions for Measurement Models -- Appendix 13.B. Constraint Interaction in CFA Models -- 14. Analysis of Structural Regression Models -- Two-Step Modeling -- Four-Step Modeling -- Interpretation of Parameter Estimates and Problems -- Detailed Example -- Equivalent Structural Regression Models -- Single Indicators in a Nonrecursive Model -- Analyzing Formative Measurement Models in SEM -- Summary -- Learn More -- Exercises -- Appendix 14.A. Constraint Interaction in SR Models -- Appendix 14.B. Effect Decomposition in Nonrecursive Models and the Equilibrium Assumption -- Appendix 14.C. Corrected Proportions of Explained Variance for Nonrecursive Models -- IV. Advanced Techniques and Best Practices -- 15. Mean Structures and Latent Growth Models -- Logic of Mean Structures -- Identification of Mean Structures -- Estimation of Mean Structures -- Latent Growth Models -- Detailed Example -- Comparison with a Polynomial Growth Model -- Extensions of Latent Growth Models -- Summary -- Learn More -- Exercises -- 16. Multiple-Samples Analysis and Measurement Invariance -- Rationale of Multiple-Samples SEM -- Measurement Invariance -- Testing Strategy and Related Issues -- Example with Continuous Indicators -- Example with Ordinal Indicators -- Structural Invariance -- Alternative Statistical Techniques -- Summary -- Learn More -- Exercises -- Appendix 16.A. Welch-James Test -- 17. Interaction Effects and Multilevel Structural Equation Modeling -- Interactive Effects of Observed Variables -- Interactive Effects in Path Analysis -- Conditional Process Modeling -- Causal Mediation Analysis -- Interactive Effects of Latent Variables -- Multilevel Modeling and SEM -- Summary -- Exercises -- Learn More -- 18. Best Practices in Structural Equation Modeling -- Resources -- Specification -- Identification -- Measures -- Sample and Data -- Estimation -- Respecification -- Tabulation -- Interpretation -- Avoid Confirmation Bias -- Bottom Lines and Statistical Beauty -- Summary -- Learn More -- Suggested Answers to Exercises -- References -- Author Index -- Subject Index -- About the Author.

"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 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 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. Pedagogical 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"--