A VARIMAX rotation is a change of coordinates used in principal component analysis (PCA) that maximizes the sum of the variances of the squared loadings. Thus, all the coefficients (squared correlation with factors) will be either large or near zero, with few intermediate values.
The goal is to associate each variable to at most one factor. The interpretation of the results of the PCA will be simplified. Then each variable will be associated to one and one only factor, they are split (as much as possible) into disjoint sets.
In this tutorial, we show how to perform this kind of rotation from the results of a standard PCA in Tanagra.
Keywords: PCA, principal component analysis, VARIMAX, QUARTIMAX
Components : Principal Component Analysis, Factor Rotation
Tutorial: en_Tanagra_Pca_Varimax.pdf
Dataset: crime_dataset_from_DASL.xls
References:
Tanagra, "New features for PCA in Tanagra"
Tanagra, "Principal Component Analysis (PCA)"
Wikipedia, "Varimax rotation"
H. Abdi, "Factor rotations in Factor Analyses"
