LEADER 05563nam 2200745Ia 450 001 9910820045703321 005 20200520144314.0 010 $a1-283-55059-8 010 $a9786613863041 010 $a1-118-35629-2 010 $a1-118-35625-X 010 $a1-118-35631-4 035 $a(CKB)2560000000090074 035 $a(EBL)989223 035 $a(OCoLC)794922819 035 $a(SSID)ssj0000701932 035 $a(PQKBManifestationID)11383071 035 $a(PQKBTitleCode)TC0000701932 035 $a(PQKBWorkID)10676847 035 $a(PQKB)10606913 035 $a(MiAaPQ)EBC989223 035 $a(Au-PeEL)EBL989223 035 $a(CaPaEBR)ebr10587611 035 $a(CaONFJC)MIL386304 035 $a(MiAaPQ)EBC7147505 035 $a(Au-PeEL)EBL7147505 035 $a(EXLCZ)992560000000090074 100 $a20120605d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStructural equation modeling$b[electronic resource] $eapplications using Mplus /$fJichuan Wang, Xiaoqian Wang 205 $a1st ed. 210 $aChichester, West Sussex $cWiley$d2012 215 $a1 online resource (479 p.) 225 0 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a1-119-97829-7 320 $aIncludes bibliographical references and index. 327 $aStructural Equation Modeling: Applications Using Mplus; Contents; Preface; 1 Introduction; 1.1 Model formulation; 1.1.1 Measurement model; 1.1.2 Structural model; 1.1.3 Model formulation in equations; 1.2 Model identification; 1.3 Model estimation; 1.4 Model evaluation; 1.5 Model modification; 1.6 Computer programs for SEM; Appendix 1.A Expressing variances and covariances among observed variables as functions of model parameters; Appendix 1.B Maximum likelihood function for SEM; 2 Confirmatory factor analysis; 2.1 Basics of CFA model; 2.2 CFA model with continuous indicators 327 $a2.3 CFA model with non-normal and censored continuous indicators2.3.1 Testing non-normality; 2.3.2 CFA model with non-normal indicators; 2.3.3 CFA model with censored data; 2.4 CFA model with categorical indicators; 2.4.1 CFA model with binary indicators; 2.4.2 CFA model with ordered categorical indicators; 2.5 Higher order CFA model; Appendix 2.A BSI-18 instrument; Appendix 2.B Item reliability; Appendix 2.C Cronbach's alpha coefficient; Appendix 2.D Calculating probabilities using PROBIT regression coefficients; 3 Structural equations with latent variables; 3.1 MIMIC model 327 $a3.2 Structural equation model3.3 Correcting for measurement errors in single indicator variables; 3.4 Testing interactions involving latent variables; Appendix 3.A Influence of measurement errors; 4 Latent growth models for longitudinal data analysis; 4.1 Linear LGM; 4.2 Nonlinear LGM; 4.3 Multi-process LGM; 4.4 Two-part LGM; 4.5 LGM with categorical outcomes; 5 Multi-group modeling; 5.1 Multi-group CFA model; 5.1.1 Multi-group first-order CFA; 5.1.2 Multi-group second-order CFA; 5.2 Multi-group SEM model; 5.3 Multi-group LGM; 6 Mixture modeling; 6.1 LCA model; 6.1.1 Example of LCA 327 $a6.1.2 Example of LCA model with covariates6.2 LTA model; 6.2.1 Example of LTA; 6.3 Growth mixture model; 6.3.1 Example of GMM; 6.4 Factor mixture model; Appendix 6.A Including covariate in the LTA model; 7 Sample size for structural equation modeling; 7.1 The rules of thumb for sample size needed for SEM; 7.2 Satorra and Saris's method for sample size estimation; 7.2.1 Application of Satorra and Saris's method to CFA model; 7.2.2 Application of Satorra and Saris's method to LGM; 7.3 Monte Carlo simulation for sample size estimation; 7.3.1 Application of Monte Carlo simulation to CFA model 327 $a7.3.2 Application of Monte Carlo simulation to LGM7.3.3 Application of Monte Carlo simulation to LGM with covariate; 7.3.4 Application of Monte Carlo simulation to LGM with missing values; 7.4 Estimate sample size for SEM based on model fit indices; 7.4.1 Application of MacCallum, Browne and Sugawara's method; 7.4.2 Application of Kim's method; References; Index; Series 330 $a A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featu 410 0$aWiley Series in Probability and Statistics 606 $aMultivariate analysis$xData processing 606 $aSocial sciences$xStatistical methods$xData processing 606 $aStructural equation modeling$xData processing 615 0$aMultivariate analysis$xData processing. 615 0$aSocial sciences$xStatistical methods$xData processing. 615 0$aStructural equation modeling$xData processing. 676 $a519.5/3 686 $aSOC027000$2bisacsh 700 $aWang$b Jichuan$0960180 701 $aWang$b Xiaoqian$01675245 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910820045703321 996 $aStructural equation modeling$94040570 997 $aUNINA