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Structural Equation Modeling: Applications Using Mplus

Book Description

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

  1. Cover
  2. Wiley Series in Probability and Statistics
  3. Title Page
  4. Copyright
  5. Preface
  6. Chapter 1: Introduction
    1. 1.1 Model Formulation
    2. 1.2 Model Identification
    3. 1.3 Model Estimation
    4. 1.4 Model Evaluation
    5. 1.5 Model Modification
    6. 1.6 Computer Programs for SEM
    7. Appendix 1.A Expressing Variances and Covariances Among Observed Variables as Functions of Model Parameters
    8. Appendix 1.B Maximum Likelihood Function for SEM
  7. Chapter 2: Confirmatory Factor Analysis
    1. 2.1 Basics of CFA Model
    2. 2.2 CFA Model with Continuous Indicators
    3. 2.3 CFA Model with Non-Normal and Censored Continuous Indicators
    4. 2.4 CFA Model with Categorical Indicators
    5. 2.5 Higher Order CFA Model
    6. Appendix 2.A BSI-18 Instrument
    7. Appendix 2.B Item Reliability
    8. Appendix 2.C Cronbach's Alpha Coefficient
    9. Appendix 2.D Calculating Probabilities Using PROBIT Regression Coefficients
  8. Chapter 3: Structural Equations with Latent Variables
    1. 3.1 MIMIC Model
    2. 3.2 Structural Equation Model
    3. 3.3 Correcting for Measurement Errors in Single Indicator Variables
    4. 3.4 Testing Interactions Involving Latent Variables
    5. Appendix 3.A Influence of Measurement Errors
  9. Chapter 4: Latent Growth Models for Longitudinal Data Analysis
    1. 4.1 Linear LGM
    2. 4.2 Nonlinear LGM
    3. 4.3 Multi-Process LGM
    4. 4.4 Two-Part LGM
    5. 4.5 LGM with Categorical Outcomes
  10. Chapter 5: Multi-Group Modeling
    1. 5.1 Multi-Group CFA Model
    2. 5.2 Multi-Group SEM model
    3. 5.3 Multi-Group LGM
  11. Chapter 6: Mixture Modeling
    1. 6.1 LCA Model
    2. 6.2 LTA model
    3. 6.3 Growth Mixture Model
    4. 6.4 Factor Mixture Model
    5. Appendix 6.A Including covariate in the LTA model
  12. Chapter 7: Sample Size for Structural Equation Modeling
    1. 7.1 The Rules of Thumb for Sample Size Needed for SEM
    2. 7.2 Satorra and Saris's Method for Sample Size Estimation
    3. 7.3 Monte Carlo Simulation for Sample Size Estimation
    4. 7.4 Estimate Sample Size for SEM Based on Model Fit Indices
  13. References
  14. Index
  15. Wiley Series in Probability and Statistics