In the previous chapter, we went over the concepts of path coefficients and covariance algebra. In reality, these terms, though used for exploratory factor analysis, come from the tradition of SEM. Exploratory factor analysis (EFA) simply attempted to model covariance structure based on identifying common sources of variance. Alternatively, SEM attempts to use covariance to model many, very explicit relationships between variables. Like EFA, SEM can incorporate both observed and unobserved variables, but unlike EFA, SEM does not necessarily need to have unobserved variables. In SEM, the relationships between variables can be represented as a series of paths, whether those variables are observed or latent. The correlation between any two variables is a path coefficient. Each observed variable will also have some residual correlation, and residual correlations may be correlated with one another, something that is not allowed in EFA.
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
Customer Reviews