Structural Equation Modeling: : Applications Using Mplus / Wang, Jichuan.

Format:

Book

Author/Creator:

Wang, Jichuan, author.

Wang, Xiaoqian, author.

Series:

Wiley series in probability and statistics

Language:

English

Subjects (All):

Mplus.

Structural equation modeling--Data processing.

Structural equation modeling.

Multivariate analysis--Data processing.

Multivariate analysis.

Social sciences--Statistical methods--Data processing.

Social sciences.

Physical Description:

1 online resource (478 pages)

Edition:

1st edition

Other Title:

Applications using Mplus

Place of Publication:

Wiley, 2012.

System Details:

text file

Summary:

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.

Contents:

Introduction

Confirmatory factor analysis

Structural equations with latent variables

Latent growth models for longitudinal data analysis

Multi-group modeling

Mixture modeling

Sample size for structural equation modeling.

Notes:

Includes bibliographical references and index.

Online resource; Title from title page (viewed October 1, 2012)

OCLC:

848895176