In this chapter, we have reviewed the application of linear algebra to covariance and correlation matrices. We have shown in R how to use PCA to account for total variance in a set of variables and how to use EFA to model common variance among these variables. We have also discussed how these methods relate to formative and reflective constructs. Notably, EFA refers to a set of numerical methods, rather than referring to an analysis intent; but as we have shown here, EFA has significant applicability in exploratory analyses. In the next chapter, we will delve into confirmatory factor analysis (CFA), which models covariance or correlation structures without rotations.
Mastering Scientific Computing with R
Mastering Scientific Computing with R
Overview of this book
Table of Contents (17 chapters)
Mastering Scientific Computing with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Programming with R
Statistical Methods with R
Linear Models
Nonlinear Methods
Linear Algebra
Principal Component Analysis and the Common Factor Model
Structural Equation Modeling and Confirmatory Factor Analysis
Simulations
Optimization
Advanced Data Management
Index
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