There is sometimes confusion between principal component analysis (PCA) and factor analysis (FA). Both methods have the aim of reducing the dimensionality of a vector of random variables. However, the most fundamental difference is that factor analysis explicitly specifies a model relating the observed variables to a smaller set of underlying unobservable factors. This assumed model may fit the data or not. In contrast PCA is just a data transformation method. Furthermore while Factor Analysis aims at explaining (covariances) or correlations, PCA concentrates on variances.


The E-Learning project SOGA-R was developed at the Department of Earth Sciences by Kai Hartmann, Joachim Krois and Annette Rudolph. You can reach us via mail by soga[at]

Creative Commons License
You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: Hartmann, K., Krois, J., Rudolph, A. (2023): Statistics and Geodata Analysis using R (SOGA-R). Department of Earth Sciences, Freie Universitaet Berlin.