Handbook of Structural Equation Modeling 1st Edition by Rick Hoyle ISBN 1462516793 9781462516797
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ISBN 10: 1462516793
ISBN 13: 9781462516797
Author: Rick Hoyle
a comprehensive guide to the theory, methods, and applications of structural equation modeling (SEM), a statistical technique widely used in the social sciences, psychology, education, and other disciplines. This book offers an in-depth exploration of SEM, covering essential topics such as model specification, identification, estimation, and evaluation. It includes contributions from leading experts, providing both foundational knowledge and advanced strategies for using SEM to analyze complex data. The book is designed for researchers, students, and practitioners, offering practical guidance and illustrative examples to enhance understanding and application of SEM in various research contexts. With its clear explanations and detailed coverage, this handbook serves as an indispensable resource for anyone seeking to deepen their expertise in structural equation modeling.
Handbook of Structural Equation Modeling 1st Edition Table of contents:
Chapter 1: Introduction to Structural Equation Modeling
- Overview of Structural Equation Modeling (SEM)
- History and Evolution of SEM
- Applications of SEM in Social Sciences, Psychology, Education, and Other Fields
- Key Concepts: Latent Variables, Measurement Models, and Structural Models
- The Role of SEM in Theory Testing and Model Building
Chapter 2: Foundations of SEM: Concepts and Terminology
- Key Terms and Concepts in SEM
- Path Diagrams and Their Interpretation
- Measurement vs. Structural Models
- Assumptions of SEM: Linearity, Normality, and Independence
- Types of Models: Confirmatory Factor Analysis (CFA), Path Analysis, and Full SEM
Chapter 3: Model Specification and Identification
- Defining Models and Variables
- Model Specification: Building a SEM Model
- The Importance of Identifying Models
- Over-identified, Under-identified, and Just-identified Models
- Guidelines for Correct Model Specification
Chapter 4: Estimation Methods in SEM
- Overview of Estimation Techniques: Maximum Likelihood, Generalized Least Squares, and Others
- Comparison of Estimation Methods: Advantages and Limitations
- Choosing the Right Estimation Method for Your Data
- Estimation in the Presence of Missing Data
- Practical Examples of Model Estimation
Chapter 5: Model Fit and Evaluation
- Assessing the Fit of a SEM Model
- Goodness-of-Fit Indices: Chi-Square, RMSEA, CFI, TLI, and Others
- Model Modification: Adding or Removing Paths to Improve Fit
- Interpreting Fit Indices and Model Modification Decisions
- Evaluating Model Fit in Large and Small Samples
Chapter 6: Confirmatory Factor Analysis (CFA)
- Introduction to CFA and its Role in SEM
- Estimating Factor Loadings and Measurement Errors
- Testing the Validity and Reliability of Latent Constructs
- CFA Model Modification and Diagnostic Tools
- Applications of CFA in Scale Development and Validation
Chapter 7: Path Analysis and Structural Models
- Understanding Path Analysis: Direct and Indirect Effects
- Structural Models: Causal Inference and Theoretical Frameworks
- Estimating and Interpreting Path Coefficients
- Testing for Mediation and Moderation in SEM
- Practical Applications of Structural Models
Chapter 8: Multigroup SEM
- SEM in Multiple Groups and Comparative Analysis
- Estimating Group Differences: Measurement Invariance and Structural Invariance
- Testing Hypotheses Across Groups (e.g., Gender, Age, Cultural Differences)
- Application Examples in Social Science and Health Research
- Challenges and Solutions in Multigroup SEM
Chapter 9: Longitudinal SEM and Growth Curve Modeling
- Overview of Longitudinal SEM: Data from Repeated Measurements
- Growth Curve Modeling: Analyzing Changes Over Time
- Estimating Individual Trajectories and Group Differences
- Applications of Longitudinal SEM in Developmental and Clinical Research
- Practical Examples and Case Studies
Chapter 10: Bayesian SEM
- Introduction to Bayesian Methods in SEM
- Differences Between Frequentist and Bayesian Approaches
- Estimation with Bayesian SEM: Priors, Likelihoods, and Posteriors
- Benefits and Challenges of Bayesian SEM
- Applications and Practical Examples of Bayesian SEM
Chapter 11: SEM with Non-Normal Data
- Challenges of Non-Normality in SEM
- Methods for Handling Non-Normal Data: Bootstrapping, Robust Estimation, and More
- Dealing with Outliers and Non-Linear Relationships
- Testing Model Robustness in Non-Normal Data
- Practical Considerations for SEM with Non-Normal Distributions
Chapter 12: Advanced Topics in SEM
- Multilevel SEM and Hierarchical Data
- SEM with Latent Growth Models and Dynamic Systems
- Non-Linear SEM: Handling Complex Relationships
- SEM with Categorical Data and Latent Class Analysis
- Future Directions in SEM Research and Applications
Chapter 13: Practical Considerations in SEM
- Common Pitfalls in SEM and How to Avoid Them
- Software for SEM: Comparison of Tools (e.g., AMOS, Mplus, LISREL, R)
- Best Practices for Data Preparation and Cleaning
- Interpreting Results: Making Sense of SEM Output
- Ethical Considerations in SEM Research
Chapter 14: Applications of SEM in Social Sciences and Other Fields
- SEM in Psychology: Testing Theories of Personality, Cognition, and Behavior
- SEM in Education: Models of Learning and Achievement
- SEM in Health and Medical Research: Applications in Epidemiology and Clinical Studies
- SEM in Marketing and Business Research
- Case Studies: Real-World Applications of SEM
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