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E-raamat: Structural Equation Modeling - Applications Using Mplus 2e: Applications Using Mplus 2nd Edition [Wiley Online]

(Mobley Group Pacific Ltd., P.R. China), (Children's National Medical Center, The George Washington University, USA)
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Structural Equation Modeling - Applications Using Mplus 2e: Applications Using Mplus 2nd EditionSuurem pilt
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Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119422730

Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using Mplus

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 many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA model, item response theory (IRT) model, graded response model (GRM), multiple imputation (MI) of missing values, plausible values of latent variables, moderated mediation model, Bayesian SEM, latent growth modeling (LGM) with individually varying times of observations, dynamic structural equation modeling (DSEM), residual dynamic structural equation modeling (RDSEM), testing measurement invariance of instrument with categorical variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal outcome, manual implementation of the BCH method and the three-step method for mixture modeling, Monte Carlo simulation power analysis for various SEM models, and estimate sample size for latent class analysis (LCA) model.

The statistical modeling program Mplus Version 8.2 is featured with all models updated. It provides researchers with a flexible tool that allows them to analyze data with an easy-to-use interface and graphical displays of data and analysis results.

Intended as both a teaching resource and a reference guide, and written in non-mathematical terms, Structural Equation Modeling: Applications Using Mplus, 2nd edition provides step-by-step instructions of model specification, estimation, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi-Group Models; Mixture Models; and Power Analysis and Sample Size Estimate for SEM.

  • Presents a useful reference guide for applications of SEM while systematically demonstrating various advanced SEM models
  • Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes
  • Provides step-by-step instructions of model specification and estimation, as well as detailed interpretation of Mplus results using real data sets
  • Introduces different methods for sample size estimate and statistical power analysis for SEM

Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to understand the theory and learn how to build their own SEM models using Mplus. 

Preface ix
1 Introduction to structural equation modeling
1(32)
1.1 Introduction
1(2)
1.2 Model formulation
3(8)
1.2.1 Measurement models
4(2)
1.2.2 Structural models
6(1)
1.2.3 Model formulation in equations
7(4)
1.3 Model identification
11(3)
1.4 Model estimation
14(5)
1.4.1 Bayes estimator
17(2)
1.5 Model fit evaluation
19(8)
1.5.1 The model x2 statistic
20(1)
1.5.2 Comparative fit index (CFI)
20(1)
1.5.3 Tucker Lewis index (TLI) or non-normed fit index (NNFI)
21(1)
1.5.4 Root mean square error of approximation (RMSEA)
22(1)
1.5.5 Root mean-square residual (RMR), standardized RMR (SRMR), and weighted RMR (WRMR)
22(2)
1.5.6 Information criteria indices
24(1)
1.5.7 Model fit evaluation with Bayes estimator
25(1)
1.5.8 Model comparison
26(1)
1.6 Model modification
27(1)
1.7 Computer programs for SEM
28(2)
Appendix 1.A Expressing variances and covariances among observed variables as functions of model parameters
30(2)
Appendix 1.B Maximum likelihood function for SEM
32(1)
2 Confirmatory factor analysis
33(86)
2.1 Introduction
33(1)
2.2 Basics of CFA models
34(11)
2.2.1 Latent variables/factors
39(1)
2.2.2 Indicator variables
39(1)
2.2.3 Item parceling
40(2)
2.2.4 Factor loadings
42(1)
2.2.5 Measurement errors
42(2)
2.2.6 Item reliability
44(1)
2.2.7 Scale reliability
44(1)
2.3 CFA models with continuous indicators
45(16)
2.3.1 Alternative methods for factor scaling
52(5)
2.3.2 Model estimated item reliability
57(1)
2.3.3 Model modification based on modification indices
57(1)
2.3.4 Model estimated scale reliability
58(2)
2.3.5 Item parceling
60(1)
2.4 CFA models with non-normal and censored continuous indicators
61(9)
2.4.1 Testing non-normality
61(1)
2.4.2 CFA models with non-normal indicators
62(5)
2.4.3 CFA models with censored data
67(3)
2.5 CFA models with categorical indicators
70(7)
2.5.1 CFA models with binary indicators
72(4)
2.5.2 CFA models with ordinal categorical indicators
76(1)
2.6 The item response theory (IRT) model and the graded response model (GRM)
77(14)
2.6.1 The item response theory (IRT) model
77(9)
2.6.2 The graded response model (GRM)
86(5)
2.7 Higher-order CFA models
91(5)
2.8 Bifactor models
96(6)
2.9 Bayesian CFA models
102(8)
2.10 Plausible values of latent variables
110(3)
Appendix 2.A BSI-18 instrument
113(1)
Appendix 2.B Item reliability
114(2)
Appendix 2.C Cronbach's alpha coefficient
116(1)
Appendix 2.D Calculating probabilities using probit regression coefficients
117(2)
3 Structural equation models
119(58)
3.1 Introduction
119(1)
3.2 Multiple indicators, multiple causes (MIMIC) model
120(17)
3.2.1 Interaction effects between covariates
126(1)
3.2.2 Differential item functioning (DIF)
127(10)
3.3 General structural equation models
137(7)
3.3.1 Testing indirect effects
141(3)
3.4 Correcting for measurement error in single indicator variables
144(6)
3.5 Testing interactions involving latent variables
150(3)
3.6 Moderated mediating effect models
153(11)
3.6.1 Bootstrap confidence intervals
159(1)
3.6.2 Estimating counterfactual-based causal effects in Mplus
160(4)
3.7 Using plausible values of latent variables in secondary analysis
164(3)
3.8 Bayesian structural equation modeling (BSEM)
167(6)
Appendix 3.A Influence of measurement errors
173(2)
Appendix 3.B Fraction of missing information (FMI)
175(2)
4 Latent growth modeling (LGM) for longitudinal data analysis
177(76)
4.1 Introduction
177(1)
4.2 Linear LGM
178(14)
4.2.1 Unconditional linear LGM
178(6)
4.2.2 LGM with time-invariant covariates
184(5)
4.2.3 LGM with time-invariant and time-varying covariates
189(3)
4.3 Nonlinear LGM
192(24)
4.3.1 LGM with polynomial time functions
192(11)
4.3.2 Piecewise LGM
203(7)
4.3.3 Free time scores
210(1)
4.3.4 LGM with distal outcomes
211(5)
4.4 Multiprocess LGM
216(5)
4.5 Two-part LGM
221(8)
4.6 LGM with categorical outcomes
229(9)
4.7 LGM with individually varying times of observation
238(3)
4.8 Dynamic structural equation modeling (DSEM)
241(12)
4.8.1 DSEM using observed centering for covariates
241(4)
4.8.2 Residual DSEM (RDSEM) using observed centering for covariates
245(3)
4.8.3 Residual DSEM (RDSEM) using latent variable centering for covariates
248(5)
5 Multigroup modeling
253(86)
5.1 Introduction
253(1)
5.2 Multigroup CFA models
254(62)
5.2.1 Multigroup first-order CFA
258(31)
5.2.2 Multigroup second-order CFA
289(17)
5.2.3 Multigroup CFA with categorical indicators
306(10)
5.3 Multigroup SEM
316(11)
5.3.1 Testing invariance of structural path coefficients across groups
322(4)
5.3.2 Testing invariance of indirect effects across groups
326(1)
5.4 Multigroup latent growth modeling (LGM)
327(12)
5.4.1 Testing invariance of the growth function
332(3)
5.4.2 Testing invariance of latent growth factor means
335(4)
6 Mixture modeling
339(104)
6.1 Introduction
339(1)
6.2 Latent class analysis (LCA) modeling
340(33)
6.2.1 Description of LCA models
341(6)
6.2.2 Denning the latent classes
347(1)
6.2.3 Predicting class membership
347(1)
6.2.4 Unconditional LCA
348(12)
6.2.5 Directly including covariates into LCA models
360(3)
6.2.6 Approaches for auxiliary variables in LCA models
363(2)
6.2.7 Implementing the PC, three-step, Lanza's, and BCH methods
365(5)
6.2.8 LCA with residual covariances
370(3)
6.3 Extending LCA to longitudinal data analysis
373(19)
6.3.1 Longitudinal latent class analysis (LLCA)
373(2)
6.3.2 Latent transition analysis (LTA) models
375(17)
6.4 Growth mixture modeling (GMM)
392(19)
6.4.1 Unconditional growth mixture modeling (GMM)
394(8)
6.4.2 GMM with covariates and a distal outcome
402(9)
6.5 Factor mixture modeling (FMM)
411(7)
6.5.1 LCFA models
417(1)
Appendix 6.A Including covariates in LTA model
418(16)
Appendix 6.B Manually implementing three-step mixture modeling
434(9)
7 Sample size for structural equation modeling
443(40)
7.1 Introduction
443(1)
7.2 The rules of thumb for sample size in SEM
444(1)
7.3 The Satorra-Saris method for estimating sample size
445(13)
7.3.1 Application of The Satorra-Saris method to CFA models
446(8)
7.3.2 Application of the Satorra-Saris's method to latent growth models
454(4)
7.4 Monte Carlo simulation for estimating sample sizes
458(15)
7.4.1 Application of a Monte Carlo simulation to CFA models
459(4)
7.4.2 Application of a Monte Carlo simulation to latent growth models
463(4)
7.4.3 Application of a Monte Carlo simulation to latent growth models with covariates
467(2)
7.4.4 Application of a Monte Carlo simulation to latent growth models with missing values
469(4)
7.5 Estimate sample size for SEM based on model fit indexes
473(6)
7.5.1 Application of the MacCallum-Browne-Sugawara's method
474(3)
7.5.2 Application of Kim's method
477(2)
7.6 Estimate sample sizes for latent class analysis (LCA) model
479(4)
References 483(24)
Index 507
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.
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