Structural Equation Modeling : A Bayesian Approach


Sik-Yum. Lee
Bok Engelsk 2007 · Electronic books.
Annen tittel
Medvirkende
Lee, Sik-Yum. (Author)
Utgitt
Hoboken : : Wiley, , 2007.
Omfang
1 online resource (459 p.)
Opplysninger
Description based upon print version of record.. - Structural Equation Modeling; Contents; About the Author; Preface; 1 Introduction; 1.1 Standard Structural Equation Models; 1.2 Covariance Structure Analysis; 1.3 Why a New Book?; 1.4 Objectives of the Book; 1.5 Data Sets and Notations; Appendix 1.1; References; 2 Some Basic Structural Equation Models; 2.1 Introduction; 2.2 Exploratory Factor Analysis; 2.3 Confirmatory and Higher-order Factor Analysis Models; 2.4 The LISREL Model; 2.5 The Bentler-Weeks Model; 2.6 Discussion; References; 3 Covariance Structure Analysis; 3.1 Introduction; 3.2 Definitions, Notations and Preliminary Results. - 3.3 GLS Analysis of Covariance Structure3.4 ML Analysis of Covariance Structure; 3.5 Asymptotically Distribution-free Methods; 3.6 Some Iterative Procedures; Appendix 3.1: Matrix Calculus; Appendix 3.2: Some Basic Results in Probability Theory; Appendix 3.3: Proofs of Some Results; References; 4 Bayesian Estimation of Structural Equation Models; 4.1 Introduction; 4.2 Basic Principles and Concepts of Bayesian Analysis of SEMs; 4.3 Bayesian Estimation of the CFA Model; 4.4 Bayesian Estimation of Standard SEMs; 4.5 Bayesian Estimation via WinBUGS; Appendix 4.1: The Metropolis-Hastings Algorithm. - 6.3 Bayesian Estimation and Goodness-of-fit6.4 Bayesian Model Comparison; 6.5 Application 1: Bayesian Selection of the Number of Factors in EFA; 6.6 Application 2: Bayesian Analysis of Quality of Life Data; References; 7 Structural Equation Models with Dichotomous Variables; 7.1 Introduction; 7.2 Bayesian Analysis; 7.3 Analysis of a Multivariate Probit Confirmatory Factor Analysis Model; 7.4 Discussion; Appendix 7.1: Questions Associated with the Manifest Variables; References; 8 Nonlinear Structural Equation Models; 8.1 Introduction; 8.2 Bayesian Analysis of a Nonlinear SEM. - 8.3 Bayesian Estimation of Nonlinear SEMs with Mixed Continuous and Ordered Categorical Variables8.4 Bayesian Estimation of SEMs with Nonlinear Covariates and Latent Variables; 8.5 Bayesian Model Comparison; References; 9 Two-level Nonlinear Structural Equation Models; 9.1 Introduction; 9.2 A Two-level Nonlinear SEM with Mixed Type Variables; 9.3 Bayesian Estimation; 9.4 Goodness-of-fit and Model Comparison; 9.5 An Application: Filipina CSWs Study; 9.6 Two-level Nonlinear SEMs with Cross-level Effects; 9.7 Analysis of Two-level Nonlinear SEMs using WinBUGS. - Appendix 4.2: EPSR ValueAppendix 4.3: Derivations of Conditional Distributions; References; 5 Model Comparison and Model Checking; 5.1 Introduction; 5.2 Bayes Factor; 5.3 Path Sampling; 5.4 An Application: Bayesian Analysis of SEMs with Fixed Covariates; 5.5 Other Methods; 5.6 Discussion; Appendix 5.1: Another Proof of Equation (5.10); Appendix 5.2: Conditional Distributions for Simulating (Ө, Ω|Y, t); Appendix 5.3: PP p-values for Model Assessment; References; 6 Structural Equation Models with Continuous and Ordered Categorical Variables; 6.1 Introduction; 6.2 The Basic Model. - Appendix 9.1: Conditional Distributions: Two-level Nonlinear SEM. - ***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent varia
Emner
Sjanger
Dewey
ISBN
9780470024232

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