Principles and Practice of Structural Equation Modeling, Fourth Edition

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 the 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 the 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.

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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Suggested Answers to Exercises
References
Author Index
Subject Index
About the Author