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Wiley Series in Probability and Statistics Ser.: Structural Equation Modeling : Applications Using Mplus by Xiaoqian Wang and Jichuan Wang (2012, Hardcover)

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STRUCTURAL EQUATION MODELING: APPLICATIONS USING MPLUS By Jichuan Wang, Xiaoqian Wang - Hardcover **Mint Condition**.

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Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Both basic concepts and examples of Class Factor Analysis (CFA) and SEM models are explored along with some recently developed advanced methods in SEM. By following the examples provided in this book, readers will be able to build their own SEM models using M plus . Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.

Product Identifiers

Publisher
Wiley & Sons, Incorporated, John
ISBN-10
1119978297
ISBN-13
9781119978299
eBay Product ID (ePID)
114116779

Product Key Features

Author
Jichuan Wang, Xiaoqian Wang
Publication Name
Structural Equation Modeling : Applications Using Mplus
Format
Hardcover
Language
English
Series
Wiley Series in Probability and Statistics Ser.
Publication Year
2012
Type
Textbook
Number of Pages
478 Pages

Dimensions

Item Length
9.3in
Item Height
1.1in
Item Width
6.3in
Item Weight
26.5 Oz

Additional Product Features

Lc Classification Number
Qa278.3.W36 2012
Edition Number
3
Table of Content
Preface ix 1 Introduction 1 1.1 Model formulation 2 1.1.1 Measurement model 4 1.1.2 Structural model 6 1.1.3 Model formulation in equations 7 1.2 Model identification 11 1.3 Model estimation 14 1.4 Model evaluation 17 1.5 Model modification 23 1.6 Computer programs for SEM 24 Appendix 1.A Expressing variances and covariances among observed variables as functions of model parameters 25 Appendix 1.B Maximum likelihood function for SEM 27 2 Confirmatory factor analysis 29 2.1 Basics of CFA model 30 2.2 CFA model with continuous indicators 42 2.3 CFA model with non-normal and censored continuous indicators 58 2.3.1 Testing non-normality 58 2.3.2 CFA model with non-normal indicators 59 2.3.3 CFA model with censored data 65 2.4 CFA model with categorical indicators 68 2.4.1 CFA model with binary indicators 69 2.4.2 CFA model with ordered categorical indicators 77 2.5 Higher order CFA model 78 Appendix 2.A BSI-18 instrument 86 Appendix 2.B Item reliability 86 Appendix 2.C Cronbach's alpha coefficient 88 Appendix 2.D Calculating probabilities using PROBIT regression coefficients 88 3 Structural equations with latent variables 90 3.1 MIMIC model 90 3.2 Structural equation model 119 3.3 Correcting for measurement errors in single indicator variables 130 3.4 Testing interactions involving latent variables 134 Appendix 3.A Influence of measurement errors 139 4 Latent growth models for longitudinal data analysis 141 4.1 Linear LGM 142 4.2 Nonlinear LGM 157 4.3 Multi-process LGM 183 4.4 Two-part LGM 188 4.5 LGM with categorical outcomes 196 5 Multi-group modeling 207 5.1 Multi-group CFA model 208 5.1.1 Multi-group first-order CFA 212 5.1.2 Multi-group second-order CFA 245 5.2 Multi-group SEM model 268 5.3 Multi-group LGM 278 6 Mixture modeling 289 6.1 LCA model 290 6.1.1 Example of LCA 296 6.1.2 Example of LCA model with covariates 309 6.2 LTA model 318 6.2.1 Example of LTA 320 6.3 Growth mixture model 340 6.3.1 Example of GMM 342 6.4 Factor mixture model 365 Appendix 6.A Including covariate in the LTA model 375 7 Sample size for structural equation modeling 391 7.1 The rules of thumb for sample size needed for SEM 391 7.2 Satorra and Saris's method for sample size estimation 393 7.2.1 Application of Satorra and Saris's method to CFA model 394 7.2.2 Application of Satorra and Saris's method to LGM 401 7.3 Monte Carlo simulation for sample size estimation 405 7.3.1 Application of Monte Carlo simulation to CFA model 406 7.3.2 Application of Monte Carlo simulation to LGM 412 7.3.3 Application of Monte Carlo simulation to LGM with covariate 415 7.3.4 Application of Monte Carlo simulation to LGM with missing values 417 7.4 Estimate sample size for SEM based on model fit indices 422 7.4.1 Application of MacCallum, Browne and Sugawara's method 423 7.4.2 Application of Kim's method 424 References 429 Index 447
Copyright Date
2012
Topic
Probability & Statistics / Multivariate Analysis, Statistics
Lccn
2012-016113
Dewey Decimal
519.5/3
Intended Audience
Scholarly & Professional
Dewey Edition
23
Illustrated
Yes
Genre
Mathematics, Social Science

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Wiley Series in Probability and Statistics Ser.: Structural Equation Modeling : Applications Using Mplus by Xiaoqian Wang and Jichuan Wang (2012, Hardcover)

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Additional Product Features

All listings for this product

item 1 Structural Equation Modeling: Applications Using Mplus by

item 2 Structural Equation Modeling: Applications Using Mplus by Jichuan Wang: Used

item 3 Structural Equation Modeling: Applications Using Mplus Wang, Jichuan and Wang...

item 4 STRUCTURAL EQUATION MODELING: APPLICATIONS USING MPLUS By Jichuan Wang Mint

item 5 Structural Equation Modeling: Applications Using Mplus : Methods and Applicat...

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