Structural Equation Modeling Using R/SAS
A Step-by-Step Approach with Real Data Analysis
- 408 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Structural Equation Modeling Using R/SAS
A Step-by-Step Approach with Real Data Analysis
About this book
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
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Table of Contents
- List of Figures
- Preface
- Authors
- 1 Linear Regression to Path Analysis
- 2 Latent VariablesāConfirmatory 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 AssessmentāLatent Growth-Curve Modeling
- 10 Full Longitudinal Mediation Modeling
- 11 Multi-Level Structural Equation Modeling
- 12 Sample Size Determination and Power Analysis
- Bibliography
- Index