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Factor Analysis and Principal Components Analysis

On the surface, the methods of factor analysis and principal components analysis (PCA) share similarities and common purposes. In particular, they both involve the characterization of multiple variables into components, or factors. However, factor analysis is much more ambitious than PCA in that it involves modeling assumptions, in particular the modeling of latent, unobservable factors.

Principal Components

PCA can be used to reduce the dimensionality of data in the sense of transforming an original set of variables to a smaller number of transformed ones. Such a purpose is desirable as it allows for the parsimonious explanation of the systematic variation of data with as few variables as possible. Obtaining parsimonious representations of data is especially useful when confronted with large numbers of variables, such as those found ...

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