LEADER 04857nam 2200649 a 450 001 9910143581603321 005 20180612233815.0 010 $a1-280-83855-8 010 $a9786610838554 010 $a0-470-02473-9 010 $a0-470-02424-0 035 $a(CKB)1000000000357190 035 $a(EBL)290975 035 $a(OCoLC)476048264 035 $a(SSID)ssj0000252575 035 $a(PQKBManifestationID)11219496 035 $a(PQKBTitleCode)TC0000252575 035 $a(PQKBWorkID)10180531 035 $a(PQKB)10532555 035 $a(MiAaPQ)EBC290975 035 $a(EXLCZ)991000000000357190 100 $a20060911d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStructural equation modeling$b[electronic resource] $ea Bayesian approach /$fSik-Yum Lee 210 $aChichester, England ;$aHoboken, NJ $cWiley$dc2007 215 $a1 online resource (459 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-02423-2 320 $aIncludes bibliographical references and index. 327 $aStructural 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 327 $a3.3 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 327 $a6.3 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 327 $a8.3 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 327 $aAppendix 9.1: Conditional Distributions: Two-level Nonlinear SEM 330 $a***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent varia 410 0$aWiley series in probability and statistics. 606 $aStructural equation modeling 606 $aBayesian statistical decision theory 608 $aElectronic books. 615 0$aStructural equation modeling. 615 0$aBayesian statistical decision theory. 676 $a519.53 676 $a519.535 686 $a31.73$2bcl 700 $aLee$b Sik-Yum$0308566 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143581603321 996 $aStructural equation modeling$92275936 997 $aUNINA