Factor analysis and principal component analysis are related techniques that help simplify complex data by identifying underlying patterns. While both methods involve calculating eigenvectors, they serve different purposes. Principal component analysis creates new variables that are combinations of existing ones, while factor analysis aims to uncover hidden variables that explain the observed data. In essence, principal component analysis focuses on reducing data dimensions, while factor analysis delves into understanding latent variables.