What is the difference between principal component analysis and factor analysis?
In principal component analysis the major objective is to select a number of components that will express as much of the total variance in the data as possible. However, the factors formed in the factor analysis are generated to identify the latent variables that are contributing to the common variance in the data. A factor analysis attempts to exclude unique variance from the analysis; whereas a PCA does not differentiate between common and unique variance. PCA analyses variance. FA analyses covariance.
