Handbook of structural equation modeling
- edited by Rick H. Hoyle.
- New York : Guilford Press, c2012.
- Physical description
- xi, 740 p. : ill., maps ; 25 cm.
Math & Statistics Library
QA278.3 .H36 2012
- Unknown QA278.3 .H36 2012
- Hoyle, Rick H.
- Includes bibliographical references and indexes.
- Part 1. Background. R. Hoyle, Introduction and Overview. R. Matsueda, Key Advances in the History of Structural Equation Modeling. M. Ho, S. Stark, O. Chernyshenko, Graphical Representation of Structural Equation Models Using Path Diagrams. K. Bollen, R. Hoyle, Latent Variables in Structural Equation Modeling. J. Pearl, The Causal Foundations of Structural Equation Modeling. D. Bandalos, P. Gagne, Simulation Methods in Structural Equation Modeling. Part 2. Fundamentals. R. Kline, Assumptions in Structural Equation Modeling. R. Hoyle, Model Specification in Structural Equation Modeling. D. Kenny, S. Milan, Identification: A Nontechnical Discussion of a Technical Issue. P. Lei, Q. Wu, Estimation in Structural Equation Modeling. T. Lee, L. Cai, R. MacCallum, Power Analysis for Tests of Structural Equation Models. M. Edwards, R. Wirth, C. Houts, N. Xi, Categorical Data in the Structural Equation Modeling Framework. S. West, A. Taylor, W. Wu, Model Fit and Model Selection in Structural Equation Modeling. C. Chou, J. Huh, Model Modification in Structural Equation Modeling. L. Williams, Equivalent Models: Concepts, Problems, Alternatives. Part 3. Implementation. P. Malone, J. Lubansky, Preparing Data for Structural Equation Modeling: Doing Your Homework. J. Graham, D. Coffman, Structural Equation Modeling with Missing Data. G. Hancock, M. Liu, Bootstrapping Standard Errors and Data-Model Fit Statistics in Structural Equation Modeling. B. Byrne, Choosing Structural Equation Modeling Computer Software: Snapshots of LISREL, EQS, Amos, and Mplus. J. Fox, J. Byrnes, S. Boker, M. Neale, Structural Equation Modeling in R with the sem and OpenMx Packages. A. Boomsma, R. Hoyle, A. Panter, The Structural Equation Modeling Research Report. Part 4. Basic Applications. T. Brown, M. Moore, Confirmatory Factor Analysis. R. Millsap, M. Olivera-Aguilar, Investigating Measurement Invariance Using Confirmatory Factor Analysis. S. Green, M. Thompson, A Flexible Structural Equation Modeling Approach for Analyzing Means. J. Cheong, D. MacKinnon, Mediation/Indirect Effects in Structural Equation Modeling. H. Marsh, Z. Wen, B. Nagengast, K. Hau, Structural Equation Models of Latent Interaction. J. Biesanz, Autoregressive Longitudinal Models. T. Raykov, Scale Construction and Development Using Structural Equation Modeling. Part 5. Advanced Applications. J. Bovaird, N. Koziol, Measurement Models for Ordered-Categorical Indicators. S. Rabe-Hesketh, A. Skrondal, X. Zheng, Multilevel Structural Equation Modeling. M. Shiyko, N. Ram, K. Grimm, An Overview of Growth Mixture Modeling: A Simple Nonlinear Application in OpenMx. J. McArdle, Latent Curve Modeling of Longitudinal Growth Data. P. Wood, Dynamic Factor Models for Longitudinally Intensive Data: Description and Estimation via Parallel Factor Models of Cholesky Decomposition. D. Cole, Latent Trait-State Models. E. Ferrer, H. Song, Longitudinal Structural Models for Assessing Dynamics in Dyadic Interactions. S. Franic, C. Dolan, D. Borsboom, D. Boomsma, Structural Equation Modeling in Genetics. A. McIntosh, A. Protzner, Structural Equation Models of Imaging Data. D.Kaplan, S. Depaoli, Bayesian Structural Equation Modeling. M. Wall, Spatial Structural Equation Modeling. G. Marcoulides, M. Ing, Automated Structural Equation Modeling Strategies.
- (source: Nielsen Book Data)
- Publisher's Summary
- The first comprehensive structural equation modeling (SEM) handbook, this accessible volume presents both the mechanics of SEM and specific SEM strategies and applications. The editor, along with an international group of 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. 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.
(source: Nielsen Book Data)
- Publication date
- 9781606230770 (hbk. : alk. paper)
- 1606230778 (hbk. : alk. paper)