Jichuan Wang, PhD, is Professor in the Department of Pediatrics, Epidemiology, and Biostatistics at the George Washington University (GWU) School of Medicine. He also serves as Senior Biostatistician in the National Children's Medical Center (CNMC) in Washington, DC. Xiaoqian Wang, PhD, is a Principle Consultant at Mobley Group Pacific Ltd., P.R. China.

• Preface ix1 Introduction to structural equation modeling 11.1 Introduction 11.2 Model formulation 31.3 Model identification 111.4 Model estimation 141.5 Model fit evaluation 191.6 Model modification 271.7 Computer programs for SEM 28Appendix 1.A Expressing variances and covariances among observed variables as functions of model parameters 30Appendix 1.B Maximum likelihood function for SEM 322 Confirmatory factor analysis 332.1 Introduction 332.2 Basics of CFA models 342.3 CFA models with continuous indicators 452.4 CFA models with non-normal and censored continuous indicators 612.5 CFA models with categorical indicators 702.6 The item response theory (IRT) model and the graded response model (GRM) 772.7 Higher-order CFA models 912.8 Bifactor models 962.9 Bayesian CFA models 1022.10 Plausible values of latent variables 110Appendix 2.A BSI-18 instrument 113Appendix 2.B Item reliability 114Appendix 2.C Cronbach’s alpha coefficient 116Appendix 2.D Calculating probabilities using probit regression coefficients 1173 Structural equation models 1193.1 Introduction 1193.2 Multiple indicators, multiple causes (MIMIC) model 1203.3 General structural equation models 1373.4 Correcting for measurement error in single indicator variables 1443.5 Testing interactions involving latent variables 1503.6 Moderated mediating effect models 1533.7 Using plausible values of latent variables in secondary analysis 1643.8 Bayesian structural equation modeling (BSEM) 167Appendix 3.A Influence of measurement errors 173Appendix 3.B Fraction of missing information (FMI) 1754 Latent growth modeling (LGM) for longitudinal data analysis 1774.1 Introduction 1774.2 Linear LGM 1784.3 Nonlinear LGM 1924.4 Multiprocess LGM 2164.5 Two-part LGM 2214.6 LGM with categorical outcomes 2294.7 LGM with individually varying times of observation 2384.8 Dynamic structural equation modeling (DSEM) 2415 Multigroup modeling 2535.1 Introduction 2535.2 Multigroup CFA models 2545.3 Multigroup SEM 3165.4 Multigroup latent growth modeling (LGM) 3276 Mixture modeling 3396.1 Introduction 3396.2 Latent class analysis (LCA) modeling 3406.3 Extending LCA to longitudinal data analysis 3736.4 Growth mixture modeling (GMM) 3926.5 Factor mixture modeling (FMM) 411Appendix 6.A Including covariates in LTA model 418Appendix 6.B Manually implementing three-step mixture modeling 4347 Sample size for structural equation modeling 4437.1 Introduction 4437.2 The rules of thumb for sample size in SEM 4447.3 The Satorra-Saris method for estimating sample size 4457.4 Monte Carlo simulation for estimating sample sizes 4587.5 Estimate sample size for SEM based on model fit indexes 4737.6 Estimate sample sizes for latent class analysis (LCA) model 479References 483Index 507