| Bibliography, etc. note |
Table of content<br/><br/>I. Background<br/>1. Introduction and Overview, Rick H. Hoyle<br/><br/>2. Key Advances in the History of Structural Equation Modeling, Ross L. Matsueda<br/><br/>3. Graphical Representation of Structural Equation Models Using Path Diagrams, Moon-ho Ringo Ho, Stephen Stark, and Olexander Chernyshenko<br/><br/>4. Latent Variables in Structural Equation Modeling, Kenneth A. Bollen and Rick H. Hoyle<br/><br/>5. The Causal Foundations of Structural Equation Modeling, Judea Pearl<br/><br/>6. Simulation Methods in Structural Equation Modeling, Deborah L. Bandalos and Phillip Gagné<br/><br/>II. Fundamentals<br/><br/>7. Assumptions in Structural Equation Modeling, Rex B. Kline<br/><br/>8. Model Specification in Structural Equation Modeling, Rick H. Hoyle<br/><br/>9. Identification: A Nontechnical Discussion of a Technical Issue, David A. Kenny and Stephanie Milan<br/><br/>10. Estimation in Structural Equation Modeling, Pui-Wa Lei and Qiong Wu<br/><br/>11. Power Analysis for Tests of Structural Equation Models, Taehun Lee, Li Cai, and Robert C. MacCallum<br/><br/>12. Categorical Data in the Structural Equation Modeling Framework, Michael C. Edwards, R. J. Wirth, Carrie R. Houts, and Nuo Xi<br/><br/>13. Model Fit and Model Selection in Structural Equation Modeling, Stephen G. West, Aaron B. Taylor, and Wei Wu<br/><br/>14. Model Modification in Structural Equation Modeling, Chih-Ping Chou and Jimi Huh<br/><br/>15. Equivalent Models: Concepts, Problems, Alternatives, Larry J. Williams<br/><br/>III. Implementation<br/><br/>16. Preparing Data for Structural Equation Modeling: Doing Your Homework, Patrick S. Malone and Jill B. Lubansky<br/><br/>17. Structural Equation Modeling with Missing Data, John W. Graham and Donna L. Coffman<br/><br/>18. Bootstrapping Standard Errors and Data–Model Fit Statistics in Structural Equation Modeling, Gregory R. Hancock and Min Liu<br/><br/>19. Choosing Structural Equation Modeling Computer Software: Snapshots of LISREL, EQS, Amos, and Mplus, Barbara M. Byrne<br/><br/>20. Structural Equation Modeling in R with the sem and OpenMx Packages, John Fox, Jarrett E. Byrnes, Steven Boker, and Michael C. Neale<br/><br/>21. The Structural Equation Modeling Research Report, Anne Boomsma, Rick H. Hoyle, and A. T. Panter<br/><br/>IV. Basic Applications<br/><br/>22. Confirmatory Factor Analysis, Timothy A. Brown and Michael T. Moore<br/><br/>23. Investigating Measurement Invariance Using Confirmatory Factor Analysis, Roger E. Millsap and Margarita Olivera-Aguilar<br/><br/>24. A Flexible Structural Equation Modeling Approach for Analyzing Means, Samuel B. Green and Marilyn S. Thompson<br/><br/>25. Mediation/Indirect Effects in Structural Equation Modeling, JeeWon Cheong and David P. MacKinnon<br/><br/>26. Structural Equation Models of Latent Interaction, Herbert W. Marsh, Zhonglin Wen, Benjamin Nagengast, and Kit-Tai Hau<br/><br/>27. Autoregressive Longitudinal Models, Jeremy C. Biesanz<br/><br/>28. Scale Construction and Development Using Structural Equation Modeling, Tenko Raykov<br/><br/>V. Advanced Applications<br/><br/>29. Measurement Models for Ordered-Categorical Indicators, James A. Bovaird and Natalie A. Koziol<br/><br/>30. Multilevel Structural Equation Modeling, Sophia Rabe-Hesketh, Anders Skrondal, and Xiaohui Zheng<br/><br/>31. An Overview of Growth Mixture Modeling: A Simple Nonlinear Application in OpenMx, Mariya P. Shiyko, Nilam Ram, and Kevin J. Grimm<br/><br/>32. Latent Curve Modeling of Longitudinal Growth Data, John J. McArdle<br/><br/>33. Dynamic Factor Models for Longitudinally Intensive Data: Description and Estimation via Parallel Factor Models of Cholesky Decomposition, Phillip Wood<br/><br/>34. Latent Trait–State Models, David A. Cole<br/><br/>35. Longitudinal Structural Models for Assessing Dynamics in Dyadic Interactions, Emilio Ferrer and Hairong Song<br/><br/>36. Structural Equation Modeling in Genetics, Sanja Franic, Conor V. Dolan, Denny Borsboom, and Dorret I. Boomsma<br/><br/>37. Structural Equation Models of Imaging Data, Anthony R. McIntosh and Andrea B. Protzner<br/><br/>38. Bayesian Structural Equation Modeling, David Kaplan and Sarah Depaoli<br/><br/>39. Spatial Structural Equation Modeling, Melanie M. Wall<br/><br/>40. Automated Structural Equation Modeling Strategies, George A. Marcoulides and Marsha Ing |
| Summary, etc. |
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. |