05563nam 2200745Ia 450 991079170780332120200520144314.01-283-55059-897866138630411-118-35629-21-118-35625-X1-118-35631-4(CKB)2560000000090074(EBL)989223(OCoLC)794922819(SSID)ssj0000701932(PQKBManifestationID)11383071(PQKBTitleCode)TC0000701932(PQKBWorkID)10676847(PQKB)10606913(MiAaPQ)EBC989223(Au-PeEL)EBL989223(CaPaEBR)ebr10587611(CaONFJC)MIL386304(MiAaPQ)EBC7147505(Au-PeEL)EBL7147505(EXLCZ)99256000000009007420120605d2012 uy 0engur|n|---|||||txtccrStructural equation modeling[electronic resource] applications using Mplus /Jichuan Wang, Xiaoqian Wang1st ed.Chichester, West Sussex Wiley20121 online resource (479 p.)Wiley series in probability and statisticsDescription based upon print version of record.1-119-97829-7 Includes bibliographical references and index.Structural 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 indicators2.3 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 model3.2 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 LCA6.1.2 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 model7.3.2 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 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 featuWiley Series in Probability and StatisticsMultivariate analysisData processingSocial sciencesStatistical methodsData processingStructural equation modelingData processingMultivariate analysisData processing.Social sciencesStatistical methodsData processing.Structural equation modelingData processing.519.5/3SOC027000bisacshWang Jichuan960180Wang Xiaoqian1556022MiAaPQMiAaPQMiAaPQBOOK9910791707803321Structural equation modeling3818408UNINA