Data Science, Factor Analysis, Data, Machine Learning, Statistics.
criterion we pick only the principal components that have eigenvalues greater than 1. Each eigenvalue is associated with one eigenvector which, in fact, is a principal component. The Kaiser criterion applied to our results. If we were to use this method, we’d exclude the last three principal