Principles and Practice of Structural Equation Modeling, Fifth Edition

Autorentext

Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montréal, Québec, Canada. 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 chapters, journal articles, and books in these areas.

Klappentext

Significantly revised, the fifth edition of the most complete, accessible text now covers all three approaches to structural equation modeling (SEM)--covariance-based SEM, nonparametric SEM (Pearl's structural causal model), and composite SEM (partial least squares path modeling). With increased emphasis on freely available software tools such as R lavaan, the text provides an understanding of all phases of SEM--what to know, best practices, and pitfalls to avoid. It includes learning exercises and a new self-test on significance testing, regression, and psychometrics. The companion website supplies helpful primers on these topics as well as data, syntax, and output for the book's examples.

Inhalt

Introduction - What’s New - Book Website - Pedagogical Approach - Principles > Software - Symbols and Notation - Enjoy the Ride - Plan of the Book I. Concepts, Standards, and Tools 1. Promise and Problems - Preparing to Learn SEM - Definition of SEM - Basic Data Analyzed in SEM - Family Matters - Pedagogy and SEM Families - Sample Size Requirements - Big Numbers, Low Quality - Limits of This Book - Summary - Learn More 2. Background Concepts and Self-Test - Uneven Background Preparation - Potential Obstacles to Learning about SEM - Significance Testing - Measurement and Psychometrics - Regression Analysis - Summary - Self-Test - Scoring Criteria 3. Steps and Reporting - Basic Steps - Optional Steps - Reporting Standards - Reporting Example - Summary - Learn More 4. Data Preparation - Forms of Input Data - Positive Definiteness - Missing Data - Classical (Obsolete) Methods for Incomplete Data - Modern Methods for Incomplete Data - Other Data Screening Issues - Summary - Learn More - Exercises - Appendix 4.a. Steps of Multiple Imputation 5. Computer Tools - Ease of Use, Not Suspension of Judgment - Human–Computer Interaction - Tips for SEM Programming - Ease of Use, Not Suspension of Judgment - Commercial versus Free Computer Tools - R Packages for SEM - Free SEM Software with Graphical User Interfaces - Commercial SEM Computer Tools - SEM Resources for Other Computing Environments - Summary II. Specification, Estimation, and Testing 6. Nonparametric Causal Models - Graph Vocabulary and Symbolism - Contracted Chains and Confounding - Covariate Selection - Instrumental Variables - Conditional Independencies and Other Types of Bias - Principles for Covariate Selection - d-Separation and Basis Sets - Graphical Identification Criteria - Detailed Example - Summary - Learn More - Exercises 7. Parametric Causal Models - Model Diagram Symbolism - Diagrams for Contracted Chains and Assumptions - Confounding in Parametric Models - Models with Correlated Causes or Indirect Effects - Recursive, Nonrecursive, and Partially Recursive Models - Detailed Example - Summary - Learn More - Exercises - Appendix 7.a. Advanced Topics in Parametric Models 8. Local Estimation and Piecewise SEM - Rationale of Local Estimation - Piecewise SEM - Detailed Example - Summary - Learn More - Exercises 9. Global Estimation and Mean Structures - Simultaneous Methods and Error Propagation - Maximum Likelihood Estimation - Default ML - Analyzing Nonnormal Data - Robust ML - FIML for Incomplete Data versus Multiple Imputation - Alternative Estimators for Continuous Outcomes - Fitting Models to Correlation Matrices - Healthy Perspective on Estimators and Global Estimation - Detailed Example - Introduction to Mean Structures - Précis of Global Estimation - Summary - Learn More - Exercises - Appendix 9.a. Types of Information Matrices and Computer Options - Appendix 9.b. Casewise ML Methods for Data Missing Not at Random 10. Model Testing and Indexing - Model Testing - Model Chi-Square - Scaled Chi-Squares and Robust Standard Errors for Nonnormal Distributions - Model Fit Indexing - RMSEA - CFI - SRMR - Thresholds for Approximate Fit Indexes - Recommended Approach to Fit Evaluation - Global Fit Statistics for the Detailed Example - Power and Preci