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.

Special Features: Included are a Web page offering 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.

I. Fundamental Concepts
1. Introduction

1.1. Plan of the Book

1.2. Notation

1.3. Computer Programs for SEM

1.4. Statistical Journeys

1.5. Family Values

1.6. Extend Latent Variable Families

1.7. Family History

1.8. Internet Resources

1.9. Summary

2. Basic Statistical Concepts: I. Correlation and Regression

2.1. Standardized and Unstandardized Variables

2.2. Bivariate Correlation and Regression

2.3. Partial Correlation

2.4. Multiple Correlation and Regression

2.5. Statistical Tests

2.6. Bootstrapping

2.7. Summary

2.8. Recommended Readings

3. Basic Statistical Concepts: II. Data Preparation and Screening

3.1. Data Preparation

3.2. Data Screening

3.3. Score Reliability and Validity

3.4. Summary

3.5. Recommended Readings

4. Core SEM Techniques and Software

4.1. Steps of SEM

4.2. Path Analysis: A Structural Model of Illness Factors

4.3. Confirmatory Factor Analysis: A Measurement Model of Arousal

4.4. A Structural Regression Model of Family Risk and Child Adjustment

4.5. Extensions

4.6. SEM Computer Programs

4.7. Summary

4.8. Recommended Readings

II. Core SEM Techniques

5. Introduction to Path Analysis

5.1. Correlation and Causation

5.2. Specification of Path Models

5.3. Types of Path Models

5.4. Principles of Identification

5.5. Sample Size

5.6. Overview of Estimation Options

5.7. Maximum Likelihood Estimation

5.8. Other Issues

5.9. Summary

5.10. Recommended Readings

Appendix 5.a. Recommendations for Start Values

Appendix 5.b. Effect Size Interpretation of Standardized Path Coefficients

6. Details of Path Analysis

6.1. Detailed Analysis of a Recursive Model of Illness Factors

6.2. Assessing Model Fit

6.3. Testing Hierarchical Models

6.4. Comparing Nonhierarchical Models

6.5. Equivalent Models

6.6. Power Analysis

6.7. Other Estimation Options

6.8. Summary

6.9. Recommended Readings

Appendix 6.a. Statistical Tests for Indirect Effects in Recursive Path Models

Appendix 6.b. Amos Basic Syntax

Appendix 6.c. Estimation of Recursive Path Models with Multiple Regression

7. Measurement Models and Confirmatory Factor Analysis

7.1. Specification of CFA Models

7.2. Identification of CFA Models

7.3. Naming and Reification Fallacies

7.4. Estimation of CFA Models

7.5. Testing CFA Models

7.6. Equivalent CFA Models

7.7. Analyzing Indicators with Non-Normal Distributions

7.8. Special Types of CFA Models

7.9. Other Issues

7.10. Summary

7.11. Recommended Readings

Appendix 7.a. Recommendations for Start Values

Appendix 7.b. CALIS Syntax

8. Models with Structural and Measurement Components

8.1. Characteristics of SR Models

8.2. Analysis of SR Models

8.3. Estimation of SR Models

8.4. A Detailed Example

8.5. Other Issues

8.6. Summary

8.7. Recommended Readings

Appendix 8.a. SEPATH Syntax

III. Advanced Techniques, Avoiding Mistakes

9. Nonrecursive Structural Models

9.1. Specification of Nonrecursive Models

9.2. Identification of Nonrecursive Models

9.3. Estimation of Nonrecursive Models

9.4. Examples

9.5. Summary

9.6. Recommended Readings

Appendix 9.a. EQS Syntax

10. Mean Structures and Latent Growth Models

10.1. Introduction to Mean Structures

10.2. Identification of Mean Structures

10.3. Estimation of Mean Structures

10.4. Structured Means in Measurement Models

10.5. Latent Growth Models

10.6. Extensions

10.7. Summary

10.8. Recommended Readings

Appendix 10.a. Mplus Syntax

11. Multiple-Sample SEM

11.1. Rationale of Multiple-Sample SEM

11.2. Multiple-Sample Path Analysis

11.3. Multiple-Sample CFA

11.4. Extensions

11.5. MIMIC Models as an Alternative to Multiple-Sample Analysis

11.6. Summary

11.7. Recommended Readings

Appendix 11.a. LISREL SIMPLIS Syntax

12. How to Fool Yourself with SEM

12.1. Tripping at the Starting Line: Specification

12.2. Improper Care and Feeding: Data

12.3. Checking Critical Judgment at the Door: Analysis and Respecification

12.4. The Garden Path: Interpretation

12.5. Summary

12.6. Recommended Readings

13. Other Horizons

13.1. Interaction and Curvilinear Effects

13.2. Multilevel Structural Equation Models

13.3. Summary

13.4. Recommended Readings