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Principles and Practice of Structural Equation Modeling, Fourth Edition


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Table of Contents

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
Learn More
2. Regression Fundamentals
Bivariate Regression
Multiple Regression
Left-Out Variables Error
Predictor Selection and Entry
Partial and Part Correlation
Observed versus Estimated Correlations
Logistic Regression and Probit Regression
Learn More
3. Significance Testing and Bootstrapping
Standard Errors
Critical Ratios
Power and Types of Null Hypotheses
Significance Testing Controversy
Confidence Intervals and Noncentral Test Distributions
Learn More
4. Data Preparation and Psychometrics Review
Forms of Input Data
Positive Definiteness
Extreme Collinearity
Relative Variances
Missing Data
Selecting Good Measures and Reporting about Them
Score Reliability
Score Validity
Item Response Theory and Item Characteristic Curves
Learn More
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
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
Learn More
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
Learn More
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
Basis Set
Causal Directed Graphs
Testable Implications
Graphical Identification Criteria
Instrumental Variables
Causal Mediation
Learn More
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
Learn More
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
Lean More
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
Tips for Inspecting Residuals
Global Fit Statistics for the Detailed Example
Testing Hierarchical Models
Comparing Nonhierarchical Models
Power Analysis
Equivalent and Near-Equivalent Models
Learn More
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
Learn More
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
Learn More
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
Learn More
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
Learn More
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
Learn More
18. Best Practices in Structural Equation Modeling
Sample and Data
Avoid Confirmation Bias
Bottom Lines and Statistical Beauty
Learn More
Suggested Answers to Exercises
Author Index
Subject Index
About the Author

About the Author

Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montreal. Since earning a doctorate in clinical psychology, he has conducted research on the psychometric evaluation of cognitive abilities, behavioral and scholastic assessment of children, 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. His website is http://tinyurl.com/rexkline.


"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."--Chris L. S. Coryn, PhD, Director, Interdisciplinary PhD in Evaluation, Western Michigan University

"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."--Craig S. Wells, PhD, Department of Educational Policy, Research, and Administration, University of Massachusetts Amherst

"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."--Donna Harrington, PhD, University of Maryland School of Social Work

"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."--Richard K. Wagner, PhD, Distinguished Professor of Psychology, Florida State University; Associate Director, Florida Center for Reading Research

"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."--Judea Pearl, PhD, Department of Computer Science, University of California, Los Angeles

"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 Psychologist (on the second edition)

"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."--PsycCRITIQUES (on the second edition)

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