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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

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.

- 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.

This title is part of the Methodology in the Social Sciences Series, edited by Todd D. Little, PhD.

“Perfectly addresses the needs of social scientists like me without formal training in mathematical statistics....Can be read by any graduate in psychology or even by keen undergraduates interested in exploring new vistas. Yet it will also constitute a surprisingly good read for experienced researchers in search of some refreshing insights in their favorite techniques....A real tour de force....Succeeds in reconciling comprehensiveness and comprehensibility.”

“The greatest strength of this book is Kline's ability to present materials in an engaging, accessible manner. In nearly all situations, Kline is able to describe even the more complex material in practical, jargon-free terms....In this regard, this book is unparalleled, and I suspect that this strength alone will make this the book of choice for many who are eager to learn SEM but who do not possess extensive quantitative backgrounds...This book could be readily adapted to courses for students with a basic understanding of correlation and regression or as part of a course for more advanced students.”

“This wonderfully written book is an impressive introduction to structural equation models (SEM) containing a sharp mix of expert analysis and observations....Contains important resources for both theoretical and applied researchers interested in SEMs...Appropriate as a text for graduate students and a reference for researchers, providing both audiences with valuable insight into the subject matter..”

“Kline is a master at explaining complex concepts in a very accessible manner. It is refreshing to see a new edition of an important book that truly is new, not simply redesigned. The fourth edition successfully incorporates recent developments in SEM and contemporary forms of causal reasoning and analysis, such as the SCM. Unlike most SEM texts, this book is notable for making a sophisticated, often-difficult statistical technique understandable to non-statisticians without watering down the material. Kline makes excellent use of relevant statistical theory without overwhelming the reader with algebraic matrices, proofs, formulas, and statistical notations. I recommend this book without reservation to researchers, instructors, and students in the social and behavioral sciences. It is far more than an introduction to SEM—in my opinion, it is a potential catalyst for reconsidering the statistical methods that researchers apply to better understand human action and interaction.”

“Too often, new editions of statistics books do not have substantive changes, but that is not the case here—Kline has made significant improvements to an already excellent book. Staying current is particularly necessary in SEM, where the theory has been developing rapidly in the last 10 years, yielding, for example, better estimation methods for categorical data and Bayesian methods. Helpful features include the topic boxes, which allow detailed discussion of particular topics without interfering with the overall flow of the text. I also like the exercises at the end of each chapter, which highlight the important parts of the chapter and provide crucial learning opportunities. Kline’s use of the companion website to distribute real examples is excellent. After reading about the models and analyses, it is helpful—actually vital—to be able to practice running the models in various software packages.”

“The best place to start for anyone who wants to learn the basics of SEM. The text emphasizes applied SEM content without relying on statistical formulas and the writing is clear and well organized, which is very helpful for students. I appreciate having exercises with answers that students can complete and check on their own. The examples are very helpful, and reflect the fact that real data are often troublesome. The website is easy to use and more extensive than for many other books.”

“The incorporation of Pearl’s approach to causal inference is a major improvement in the fourth edition. This is the most useful introductory SEM book out there. I have recommended this book to colleagues for both personal and class use, and will continue to do so.”

“This book is unique in that it treats structural equation models for what they are—carriers of causal assumptions and tools for causal inference. Gone are the inhibitions and trepidation that characterize most SEM texts in their treatments of causal inference. Overall, the book elevates SEM education to a new level of modernity and promises to usher in a renaissance for a field that pioneered causal analysis in the behavioral sciences.”

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

Previous editions published by Guilford:

Third Edition, © 2011

ISBN: 9781606238769

Second Edition, © 2005

ISBN: 9781572306905

First Edition, © 1998

ISBN: 9781572303379

New to this edition:

- Extensively revised to cover important new topics: Pearl's graphing theory and 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.