Introduction - What''s New - Book Website - Pedagogical Approach - Principles > Software - Symbols and Notation - Enjoy the Ride - Plan of the Book I. Concepts, Standards, and Tools 1. Promise and Problems - Preparing to Learn SEM - Definition of SEM - Basic Data Analyzed in SEM - Family Matters - Pedagogy and SEM Families - Sample Size Requirements - Big Numbers, Low Quality - Limits of This Book - Summary - Learn More 2. Background Concepts and Self-Test - Uneven Background Preparation - Potential Obstacles to Learning about SEM - Significance Testing - Measurement and Psychometrics - Regression Analysis - Summary - Self-Test - Scoring Criteria 3. Steps and Reporting - Basic Steps - Optional Steps - Reporting Standards - Reporting Example - Summary - Learn More 4. Data Preparation - Forms of Input Data - Positive Definiteness - Missing Data - Classical (Obsolete) Methods for Incomplete Data - Modern Methods for Incomplete Data - Other Data Screening Issues - Summary - Learn More - Exercises - Appendix 4.a. Steps of Multiple Imputation 5.
Computer Tools - Ease of Use, Not Suspension of Judgment - Human-Computer Interaction - Tips for SEM Programming - Ease of Use, Not Suspension of Judgment - Commercial versus Free Computer Tools - R Packages for SEM - Free SEM Software with Graphical User Interfaces - Commercial SEM Computer Tools - SEM Resources for Other Computing Environments - Summary II. Specification, Estimation, and Testing 6. Nonparametric Causal Models - Graph Vocabulary and Symbolism - Contracted Chains and Confounding - Covariate Selection - Instrumental Variables - Conditional Independencies and Other Types of Bias - Principles for Covariate Selection - d-Separation and Basis Sets - Graphical Identification Criteria - Detailed Example - Summary - Learn More - Exercises 7. Parametric Causal Models - Model Diagram Symbolism - Diagrams for Contracted Chains and Assumptions - Confounding in Parametric Models - Models with Correlated Causes or Indirect Effects - Recursive, Nonrecursive, and Partially Recursive Models - Detailed Example - Summary - Learn More - Exercises - Appendix 7.a. Advanced Topics in Parametric Models 8. Local Estimation and Piecewise SEM - Rationale of Local Estimation - Piecewise SEM - Detailed Example - Summary - Learn More - Exercises 9. Global Estimation and Mean Structures - Simultaneous Methods and Error Propagation - Maximum Likelihood Estimation - Default ML - Analyzing Nonnormal Data - Robust ML - FIML for Incomplete Data versus Multiple Imputation - Alternative Estimators for Continuous Outcomes - Fitting Models to Correlation Matrices - Healthy Perspective on Estimators and Global Estimation - Detailed Example - Introduction to Mean Structures - Précis of Global Estimation - Summary - Learn More - Exercises - Appendix 9.
a. Types of Information Matrices and Computer Options - Appendix 9.b. Casewise ML Methods for Data Missing Not at Random 10. Model Testing and Indexing - Model Testing - Model Chi-Square - Scaled Chi-Squares and Robust Standard Errors for Nonnormal Distributions - Model Fit Indexing - RMSEA - CFI - SRMR - Thresholds for Approximate Fit Indexes - Recommended Approach to Fit Evaluation - Global Fit Statistics for the Detailed Example - Power and Precision - Summary - Learn More - Exercises - Appendix 10.a. Significance Testing Based on the RMSEA 11. Comparing Models - Nested Models - Building and Trimming - Empirical versus Theoretical Respecification - Chi-Square Difference Test - Modification Indexes and Related Statistics - Intelligent Automated Search Strategies - Model Building for the Detailed Example - Comparing Nonnested Models - Equivalent Models - Coping with Equivalent or Nearly Equivalent Models - Summary - Learn More - Exercises - Appendix 11.
a. Other Types of Model Relations and Tests 12. Comparing Groups - Issues in Multiple-Group SEM - Detailed Example for a Path Model of Achievement and Delinquency - Tests for Conditional Indirect Effects over Groups - Summary - Learn More - Exercises III. Multiple-Indicator Approximation of Concepts 13. Multiple-Indicator Measurement - Concepts, Indicators, and Proxies - Reflective Measurement and Effect Indicators - Causal-Formative Measurement and Causal Indicators - Composite Measurement and Composite Indicators - Mixed-Model Measurement - Considerations in Selecting a Measurement Model - Cautions on Formative Measurement - Summary 14. Confirmatory Factor Analysis - EFA versus CFA - Suggestions for Selecting Indicators - Basic CFA Models - Other Methods for Scaling Factors - Detailed Example for a Basic CFA Model of Cognitive Abilities - Respecification of CFA Models - Estimation Problems - Equivalent CFA Models - Special Tests with Equality Constraints - Models for Multitrait-Multimethod Data - Second-Order and Bifactor Models with General Factors - Summary - Learn More - Exercises - Appendix 14.a. Identification Rules for Correlated Errors or Multiple Loadings 15.
Structural Regression Models - Full SR Models - Two-Step Modeling - Other Modeling Strategies - Detailed Example of Two-Step Modeling in a High-Risk Sample - Partial SR Models with Single Indicators - Example for a Partial SR Model - Summary - Learn More - Exercises 16. Composite Models - Modern Composite Analysis in SEM - Disambiguation of Terms - Special Computer Tools - Motivating Example - Alternative Composite Model - Partial Least Squares Path Modeling Algorithm - PLS PM Analysis of the Composite Model - Henseler-Ogasawara Specification and ML Analysis - Summary - Learn More - Exercises IV. Advanced Techniques 17. Analyses in Small Samples - Suggestions for Analyzing Common Factor Models - Analysis of a Common Factor Model in a Small Sample - Controlling Measurement Error in Manifest Variable Path Models - Adjusted Test Statistics for Small Samples - Bayesian Methods and Regularized SEM - Summary - Learn More - Exercises 18. Categorical Confirmatory Factor Analysis - Basic Estimation Options for Categorical Data - Overview of Continuous/Categorical Variable Methodology - Latent Response Variables and Thresholds - Polychoric Correlations - Measurement Model and Diagram - Methods to Scale Latent Response Variables - Estimators, Adjusted Test Statistics, and Robust Standard Errors - Models with Continuous and Ordinal Indicators - Detailed Example for Items about Self-Rated Depression - Other Estimation Options for Categorical CFA - Item Response Theory and CFA - Summary - Learn More - Exercises 19. Nonrecursive Models with Causal Loops - Causal Loops - Assumptions of Causal Loops - Identification Requirements - Respecification of Nonrecursive Models That Are Not Identified - Order Condition and Rank Condition - Detailed Example for a Nonrecursive Partial SR Model - Blocked-Error R ² for Nonrecursive Models - Summary - Learn More - Exercises - Appendix 19.a. Evaluation of the Rank Condition 20.
Enhanced Mediation Analysis - Mediation Analysis in Cross-Sectional Designs