Structural Equation Modeling Using R/SAS: A Step-by-Step Approach with Real Data Analysis

ISBN-13: 9781032431239 / Angielski

Yiu-Fai Yung

Opis

There has been considerable attention to making the methodologies of structural equation modeling available to researchers, practitioners, and students along with commonly used software. Structural Equation Modelling Using R/SAS aims to bring it all together to provide a concise point-of-reference for the most commonly used structural equation modeling from the fundamental level to the advanced level. This book is intended to contribute to the rapid development in structural equation modeling and its applications to real-world data. Straightforward explanations of the statistical theory and models related to structural equation models are provided, using a compilation of a variety of publicly available data, to provide an illustration of data analytics in a step-by-step fashion using commonly used statistical software of R and SAS. This book is appropriate for anyone who is interested in learning and practicing structural equation modeling, especially in using R and SAS. It is useful for applied statisticians, data scientists and practitioners, applied statistical analysts and scientists in public health, and academic researchers and graduate students in statistics, whilst also being of use to R&D professionals/practitioners in industry and governmental agencies.

Key Features:

• Extensive compilation of commonly used structural equation models and methods from fundamental to advanced levels
• Straightforward explanations of the theory related to the structural equation models
• Compilation of a variety of publicly available data
• Step-by-step illustrations of data analysis using commonly used statistical software R and SAS
• Data and computer programs are available for readers to replicate and implement the new methods to better understand the book contents and for future applications
• Handbook for applied statisticians and practitioners

Spis treści

1. Linear Regression to Path Analysis  2. Latent Variables - Confirmatory Factor Analysis  3. Mediation Analysis  4. Structural Equation Modeling with Non-Normal Data  5. Structural Equation Modeling with Categorical Data  6. Multi-Group Data Analysis: Continuous Data  7. Multi-Group Data Analysis: Categorical Data  8. Pain-Related Disability for People with Temporomandibular Disorder: Full Structural Equation Modeling  9. Breast-Cancer Post-Surgery Assessment—Latent Growth-Curve Modeling  10. Full Longitudinal Mediation Modeling  11. Multi-Level Structural Equation Modeling  12. Sample Size Determination and Power Analysis

O autorze

Ding-Geng Chen, Ph.D. Professor and Executive Director in Biostatistics College of Health Solutions Arizona State University, USA.

Yiu-Fai Yung, Ph.D. Senior Manager, Advanced Analytics R & D, SAS Institute Inc.