Structural equation modeling : applications using Mplus
Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using M plus Focusing on the conceptual and practical aspects of Structural Equation Modeling (SEM), this book demonstrates basic concepts and examples of various SEM models, along with updates on...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Hoboken
Wiley
2019
John Wiley & Sons, Incorporated Wiley-Blackwell |
| Edition | 2 |
| Series | Wiley Series in Probability and Statistics |
| Subjects | |
| Online Access | Get full text |
| ISBN | 1119422701 9781119422709 9781119422723 1119422728 |
| DOI | 10.1002/9781119422730 |
Cover
Table of Contents:
- Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction to structural equation modeling -- 1.1 Introduction -- 1.2 Model formulation -- 1.2.1 Measurement models -- 1.2.2 Structural models -- 1.2.3 Model formulation in equations -- 1.3 Model identification -- 1.4 Model estimation -- 1.4.1 Bayes estimator -- 1.5 Model fit evaluation -- 1.5.1 The model X2 statistic -- 1.5.2 Comparative fit index (CFI) -- 1.5.3 Tucker Lewis index (TLI) or non‐normed fit index (NNFI) -- 1.5.4 Root mean square error of approximation (RMSEA) -- 1.5.5 Root mean‐square residual (RMR), standardized RMR (SRMR), and weighted RMR (WRMR) -- 1.5.6 Information criteria indices -- 1.5.7 Model fit evaluation with Bayes estimator -- 1.5.8 Model comparison -- 1.6 Model modification -- 1.7 Computer programs for SEM -- Chapter 2 Confirmatory factor analysis -- 2.1 Introduction -- 2.2 Basics of CFA models -- 2.2.1 Latent variables/factors -- 2.2.2 Indicator variables -- 2.2.3 Item parceling -- 2.2.4 Factor loadings -- 2.2.5 Measurement errors -- 2.2.6 Item reliability -- 2.2.7 Scale reliability -- 2.3 CFA models with continuous indicators -- 2.3.1 Alternative methods for factor scaling -- 2.3.2 Model estimated item reliability -- 2.3.3 Model modification based on modification indices -- 2.3.4 Model estimated scale reliability -- 2.3.5 Item parceling -- 2.4 CFA models with non‐normal and censored continuous indicators -- 2.4.1 Testing non‐normality -- 2.4.2 CFA models with non‐normal indicators -- 2.4.3 CFA models with censored data -- 2.5 CFA models with categorical indicators -- 2.5.1 CFA models with binary indicators -- 2.5.2 CFA models with ordinal categorical indicators -- 2.6 The item response theory (IRT) model and the graded response model (GRM) -- 2.6.1 The item response theory (IRT) model -- 2.6.2 The graded response model (GRM)
- 5.1 Introduction -- 5.2 Multigroup CFA models -- 5.2.1 Multigroup first‐order CFA -- 5.2.2 Multigroup second‐order CFA -- 5.2.3 Multigroup CFA with categorical indicators -- 5.3 Multigroup SEM -- 5.3.1 Testing invariance of structural path coefficients across groups -- 5.3.2 Testing invariance of indirect effects across groups -- 5.4 Multigroup latent growth modeling (LGM) -- 5.4.1 Testing invariance of the growth function -- 5.4.2 Testing invariance of latent growth factor means -- Chapter 6 Mixture modeling -- 6.1 Introduction -- 6.2 Latent class analysis (LCA) modeling -- 6.2.1 Description of LCA models -- 6.2.2 Defining the latent classes -- 6.2.3 Predicting class membership -- 6.2.4 Unconditional LCA -- 6.2.5 Directly including covariates into LCA models -- 6.2.6 Approaches for auxiliary variables in LCA models -- 6.2.7 Implementing the PC, three‐step, Lanza's, and BCH methods -- 6.2.8 LCA with residual covariances -- 6.3 Extending LCA to longitudinal data analysis -- 6.3.1 Longitudinal latent class analysis (LLCA) -- 6.3.2 Latent transition analysis (LTA) models -- 6.4 Growth mixture modeling (GMM) -- 6.4.1 Unconditional growth mixture modeling (GMM) -- 6.4.2 GMM with covariates and a distal outcome -- 6.5 Factor mixture modeling (FMM) -- 6.5.1 LCFA models -- 6.A Including covariates in LTA model -- 6.B Manually implementing three-step mixture modeling -- Chapter 7 Sample size for structural equation modeling -- 7.1 Introduction -- 7.2 The rules of thumb for sample size in SEM -- 7.3 The Satorra‐Saris method for estimating sample size -- 7.3.1 Application of The Satorra‐Saris method to CFA models -- 7.3.2 Application of the Satorra‐Saris's method to latent growth models -- 7.4 Monte Carlo simulation for estimating sample sizes -- 7.4.1 Application of a Monte Carlo simulation to CFA models
- 2.7 Higher‐order CFA models -- 2.8 Bifactor models -- 2.9 Bayesian CFA models -- 2.10 Plausible values of latent variables -- 2.A BSI-18 instrument -- 2.B Item reliability -- 2.C Cronbach's alpha coefficient -- 2.D Calculating probabilities using probit regression coefficients -- Chapter 3 Structural equation models -- 3.1 Introduction -- 3.2 Multiple indicators, multiple causes (MIMIC) model -- 3.2.1 Interaction effects between covariates -- 3.2.2 Differential item functioning (DIF) -- 3.3 General structural equation models -- 3.3.1 Testing indirect effects -- 3.4 Correcting for measurement error in single indicator variables -- 3.5 Testing interactions involving latent variables -- 3.6 Moderated mediating effect models -- 3.6.1 Bootstrap confidence intervals -- 3.6.2 Estimating counterfactual‐based causal effects in Mplus -- 3.7 Using plausible values of latent variables in secondary analysis -- 3.8 Bayesian structural equation modeling (BSEM) -- 3.A Influence of measurement errors -- 3.B Fraction of missing information (FMI) -- Chapter 4 Latent growth modeling (LGM) for longitudinal data analysis -- 4.1 Introduction -- 4.2 Linear LGM -- 4.2.1 Unconditional linear LGM -- 4.2.2 LGM with time‐invariant covariates -- 4.2.3 LGM with time‐invariant and time‐varying covariates -- 4.3 Nonlinear LGM -- 4.3.1 LGM with polynomial time functions -- 4.3.2 Piecewise LGM -- 4.3.3 Free time scores -- 4.3.4 LGM with distal outcomes -- 4.4 Multiprocess LGM -- 4.5 Two‐part LGM -- 4.6 LGM with categorical outcomes -- 4.7 LGM with individually varying times of observation -- 4.8 Dynamic structural equation modeling (DSEM) -- 4.8.1 DSEM using observed centering for covariates -- 4.8.2 Residual DSEM (RDSEM) using observed centering for covariates -- 4.8.3 Residual DSEM (RDSEM) using latent variable centering for covariates -- Chapter 5 Multigroup modeling
- 7.4.2 Application of a Monte Carlo simulation to latent growth models -- 7.4.3 Application of a Monte Carlo simulation to latent growth models with covariates -- 7.4.4 Application of a Monte Carlo simulation to latent growth models with missing values -- 7.5 Estimate sample size for SEM based on model fit indexes -- 7.5.1 Application of the MacCallum-Browne-Sugawara's method -- 7.5.2 Application of Kim's method -- 7.6 Estimate sample sizes for latent class analysis (LCA) model -- References -- Index -- Wiley Series in Probability and Statistics -- EULA