# Principles and Practice of Structural Equation Modeling, Fourth Edition

, by Kline, Rex B.**Note:**Supplemental materials are not guaranteed with Rental or Used book purchases.

- ISBN: 9781462523344 | 146252334X
- Cover: Paperback
- Copyright: 11/4/2015

Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montreal, Quebec, Canada. Since earning a doctorate in clinical psychology, he has conducted research on the psychometric evaluation of cognitive abilities, child clinical assessment, 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.

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

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