The main 3 difference between these two techniques are as follows -
In Principal Components Analysis, the components are calculated as linear combinations of the original variables. In Factor Analysis, the original variables are defined as linear combinations of the factors.
Principal Components Analysis is used as a variable reduction technique whereas Factor Analysis is used to understand what constructs underlie the data.
In Principal Components Analysis, the goal is to explain as much of the total variance in the variables as possible. The goal in Factor Analysis is to explain the covariances or correlations between the variables.
