Principles and Practice of Structural Equation Modeling

- 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 R² 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

- Effect Sizes for Indirect Effects

- Cross-Lag Panel Designs for Mediation

- Conditional Process Analysis

- Causal Mediation Analysis Based on Nonparametric Models and Counterfactuals

- Reporting Standards for Mediation Studies

- Summary

- Learn More

- Exercises

21. Latent Growth Curve Models

- Basic Latent Growth Models

- Data Set for Analyzing Basic Growth Models with No Covariates

- Example Analyses of Basic Growth Models

- Example for a Growth Predictor Model with Time-Invariant Covariates

- Practical Suggestions for Latent Growth Modeling

- Extensions of Latent Growth Models

- Summary

- Learn More

- Exercises

- Appendix 21.a. Unequal Measurement Intervals and Options for Defining the Intercept

22. Measurement Invariance

- Levels of Invariance

- Analysis Decisions

- Partial Measurement Invariance

- Detailed Example for a Two-Factor Model of Divergent Thinking

- Practical Suggestions for Measurement Invariance Testing

- Measurement Invariance Testing in Categorical CFA

- Other Statistical Approaches to Estimating Measurement Invariance

- Summary

- Exercises

23. Best Practices in SEM

- Resources

- Bottom Lines and Statistical Beauty

- Mightily Distinguish Your Work (Be a Hero)

- Family Relations

- Specification

- Identification

- Measures

- Sample and Data

- Estimation

- Respecification

- Tabulation

- Interpretation

- Summary

- Learn More

Suggested Answers to Exercises

References

Author Index

Subject Index

About the Author