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Factor Analysis

The defining characteristic that distinguishes between PCA and factor analysis is that in PCA we assume that all variability in an item should be used in the analysis, while in factor analysis, we define a priori the number of factors that we want to extract, and the extracted axes will be scaled to the variance along the new improved axes.

The factor analysis output includes the scores, loadings, and correlation matrices.

Pre-Processing the Data

PCA and factor analysis methods allow optional pre-processing of the data prior to the main analysis. Examples of data transformations that can be used (as part of PCA or factor analysis) are: