Description
The first comprehensive structural equation modeling (SEM) handbook, this accessible volume offers broad and deep coverage of both the mechanics of SEM and specific SEM strategies and applications. The editor, contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM, such as intensive longitudinal assessments, dyadic data, brain imaging, and genotyping. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results.
Chapter
Chapter 3: Graphical Representation of Structural Equation Models Using Path Diagrams
Chapter 4: Latent Variables in Structural Equation Modeling
Chapter 5: The Causal Foundations of Structural Equation Modeling
Chapter 6: Simulation Methods in Structural Equation Modeling
Chapter 7: Assumptions in Structural Equation Modeling
Chapter 8: Model Specification in Structural Equation Modeling
Chapter 9: Identification: A Nontechnical Discussion of a Technical Issue
Chapter 10: Estimation in Structural Equation Modeling
Chapter 11: Power Analysis for Tests of Structural Equation Models
Chapter 12: Categorical Data in the Structural Equation Modeling Framework
Chapter 13: Model Fit and Model Selectionin Structural Equation Modeling
Chapter 14: Model Modification in Structural Equation Modeling
Chapter 15: Equivalent Models: Concepts, Problems, Alternatives
Chapter 16: Preparing Data for Structural Equation Modeling: Doing Your Homework
Chapter 17: Structural Equation Modeling with Missing Data
Chapter 18: Bootstrapping Standard Errors and Data–Model Fit Statistics in Structural Equation Modeling
Chapter 19: Choosing Structural Equation Modeling Computer Software: Snapshots of LISREL, EQS, Amos, and Mplus
Chapter 20: Structural Equation Modeling in R with the sem and OpenMx Packages
Chapter 21: The Structural Equation Modeling Research Report
Part IV: Basic Applications
Chapter 22: Confirmatory Factor Analysis
Chapter 23: Investigating Measurement Invariance Using Confirmatory Factor Analysis
Chapter 24: A Flexible Structural Equation Modeling Approach for Analyzing Means
Chapter 25: Mediation/Indirect Effects in Structural Equation Modeling
Chapter 26: Structural Equation Models of Latent Interaction
Chapter 27: Autoregressive Longitudinal Models
Chapter 28: Scale Construction and Development Using Structural Equation Modeling
Part V: Advanced Applications
Chapter 29: Measurement Models for Ordered-Categorical Indicators
Chapter 30: Multilevel Structural Equation Modeling
Chapter 31: An Overview of Growth Mixture Modeling: A Simple Nonlinear Application in OpenMx
Chapter 32: Latent Curve Modeling of Longitudinal Growth Data
Chapter 33: Dynamic Factor Modelsfor Longitudinally Intensive Data: Description and Estimation via Parallel Factor Models of Cholesky Decomposition
Chapter 34: Latent Trait–State Models
Chapter 35: Longitudinal Structural Models for Assessing Dynamics in Dyadic Interactions
Chapter 36: Structural Equation Modeling in Genetics
Chapter 37: Structural Equation Models of Imaging Data
Chapter 38: Bayesian Structural Equation Modeling
Chapter 39: Spatial Structural Equation Modeling
Chapter 40: Automated Structural Equation Modeling Strategies
Handbook of Structural Equation Modeling
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