Structural Equation Modeling

Name: Structural Equation Modeling | Applications Using Mplus
Brand: Wiley-Blackwell
Price: 80.99 EUR
Availability: OnlineOnly

Applications Using Mplus

Jichuan Wang Xiaoqian Wang(Autor*in)

Wiley-Blackwell (Verlag)

2. Auflage

Erschienen am 5. September 2019

536 Seiten

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Beschreibung

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.

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Inhalt

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)
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
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
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

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