Structural equation modeling applications using Mplus

✍ Scribed by Wang, Jichuan;Wang, Xiaoqian(Contributor)

• Publisher: Wiley
• Year: 2019;2020
• Tongue: English
• Leaves: 537
• Series: Wiley series in probability and statistics
• Edition: Second edition
• Category: Library

✦ Synopsis

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 featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results. Key features: * Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus. * 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 detail interpretation of Mplus results. * Explores different methods for sample size estimate and statistical power analysis for SEM. By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.

✦ Table of Contents

Cover......Page 1
Title Page......Page 5
Copyright......Page 6
Contents......Page 7
Preface......Page 11
1.1 Introduction......Page 13
1.2 Model formulation......Page 15
1.2.1 Measurement models......Page 16
1.2.2 Structural models......Page 18
1.2.3 Model formulation in equations......Page 19
1.3 Model identification......Page 23
1.4 Model estimation......Page 26
1.4.1 Bayes estimator......Page 29
1.5 Model fit evaluation......Page 31
1.5.2 Comparative fit index (CFI)......Page 32
1.5.3 Tucker Lewis index (TLI) or non‐normed fit index (NNFI)......Page 33
1.5.5 Root mean‐square residual (RMR), standardized RMR (SRMR), and weighted RMR (WRMR)......Page 34
1.5.6 Information criteria indices......Page 36
1.5.7 Model fit evaluation with Bayes estimator......Page 37
1.5.8 Model comparison......Page 38
1.6 Model modification......Page 39
1.7 Computer programs for SEM......Page 40
2.1 Introduction......Page 45
2.2 Basics of CFA models......Page 46
2.2.2 Indicator variables......Page 51
2.2.3 Item parceling......Page 52
2.2.5 Measurement errors......Page 54
2.2.7 Scale reliability......Page 56
2.3 CFA models with continuous indicators......Page 57
2.3.1 Alternative methods for factor scaling......Page 64
2.3.3 Model modification based on modification indices......Page 69
2.3.4 Model estimated scale reliability......Page 70
2.3.5 Item parceling......Page 72
2.4.1 Testing non‐normality......Page 73
2.4.2 CFA models with non‐normal indicators......Page 74
2.4.3 CFA models with censored data......Page 79
2.5 CFA models with categorical indicators......Page 82
2.5.1 CFA models with binary indicators......Page 84
2.5.2 CFA models with ordinal categorical indicators......Page 88
2.6.1 The item response theory (IRT) model......Page 89
2.6.2 The graded response model (GRM)......Page 98
2.7 Higher‐order CFA models......Page 103
2.8 Bifactor models......Page 108
2.9 Bayesian CFA models......Page 114
2.10 Plausible values of latent variables......Page 122
2.A BSI-18 instrument......Page 125
2.B Item reliability......Page 126
2.C Cronbach's alpha coefficient......Page 128
2.D Calculating probabilities using probit regression coefficients......Page 129
3.1 Introduction......Page 131
3.2 Multiple indicators, multiple causes (MIMIC) model......Page 132
3.2.1 Interaction effects between covariates......Page 138
3.2.2 Differential item functioning (DIF)......Page 139
3.3 General structural equation models......Page 149
3.3.1 Testing indirect effects......Page 153
3.4 Correcting for measurement error in single indicator variables......Page 156
3.5 Testing interactions involving latent variables......Page 162
3.6 Moderated mediating effect models......Page 165
3.6.1 Bootstrap confidence intervals......Page 171
3.6.2 Estimating counterfactual‐based causal effects in Mplus......Page 172
3.7 Using plausible values of latent variables in secondary analysis......Page 176
3.8 Bayesian structural equation modeling (BSEM)......Page 179
3.A Influence of measurement errors......Page 185
3.B Fraction of missing information (FMI)......Page 187
4.1 Introduction......Page 189
4.2.1 Unconditional linear LGM......Page 190
4.2.2 LGM with time‐invariant covariates......Page 196
4.2.3 LGM with time‐invariant and time‐varying covariates......Page 201
4.3.1 LGM with polynomial time functions......Page 204
4.3.2 Piecewise LGM......Page 215
4.3.3 Free time scores......Page 222
4.3.4 LGM with distal outcomes......Page 223
4.4 Multiprocess LGM......Page 228
4.5 Two‐part LGM......Page 233
4.6 LGM with categorical outcomes......Page 241
4.7 LGM with individually varying times of observation......Page 250
4.8.1 DSEM using observed centering for covariates......Page 253
4.8.2 Residual DSEM (RDSEM) using observed centering for covariates......Page 257
4.8.3 Residual DSEM (RDSEM) using latent variable centering for covariates......Page 260
5.1 Introduction......Page 265
5.2 Multigroup CFA models......Page 266
5.2.1 Multigroup first‐order CFA......Page 270
5.2.2 Multigroup second‐order CFA......Page 301
5.2.3 Multigroup CFA with categorical indicators......Page 318
5.3 Multigroup SEM......Page 328
5.3.1 Testing invariance of structural path coefficients across groups......Page 334
5.3.2 Testing invariance of indirect effects across groups......Page 338
5.4 Multigroup latent growth modeling (LGM)......Page 339
5.4.1 Testing invariance of the growth function......Page 344
5.4.2 Testing invariance of latent growth factor means......Page 347
6.1 Introduction......Page 351
6.2 Latent class analysis (LCA) modeling......Page 352
6.2.1 Description of LCA models......Page 353
6.2.3 Predicting class membership......Page 359
6.2.4 Unconditional LCA......Page 360
6.2.5 Directly including covariates into LCA models......Page 372
6.2.6 Approaches for auxiliary variables in LCA models......Page 375
6.2.7 Implementing the PC, three‐step, Lanza's, and BCH methods......Page 377
6.2.8 LCA with residual covariances......Page 382
6.3.1 Longitudinal latent class analysis (LLCA)......Page 385
6.3.2 Latent transition analysis (LTA) models......Page 387
6.4 Growth mixture modeling (GMM)......Page 404
6.4.1 Unconditional growth mixture modeling (GMM)......Page 406
6.4.2 GMM with covariates and a distal outcome......Page 414
6.5 Factor mixture modeling (FMM)......Page 423
6.5.1 LCFA models......Page 429
6.A Including covariates in LTA model......Page 430
6.B Manually implementing three-step mixture modeling......Page 446
7.1 Introduction......Page 455
7.2 The rules of thumb for sample size in SEM......Page 456
7.3 The Satorra‐Saris method for estimating sample size......Page 457
7.3.1 Application of The Satorra‐Saris method to CFA models......Page 458
7.3.2 Application of the Satorra‐Saris's method to latent growth models......Page 466
7.4 Monte Carlo simulation for estimating sample sizes......Page 470
7.4.1 Application of a Monte Carlo simulation to CFA models......Page 471
7.4.2 Application of a Monte Carlo simulation to latent growth models......Page 475
7.4.3 Application of a Monte Carlo simulation to latent growth models with covariates......Page 479
7.4.4 Application of a Monte Carlo simulation to latent growth models with missing values......Page 481
7.5 Estimate sample size for SEM based on model fit indexes......Page 485
7.5.1 Application of the MacCallum-Browne-Sugawara's method......Page 486
7.5.2 Application of Kim's method......Page 489
7.6 Estimate sample sizes for latent class analysis (LCA) model......Page 491
References......Page 495
Index......Page 519
Wiley Series in Probability and Statistics......Page 525
EULA......Page 537