As we become inundated with more and more data, we are tasked with trying to reduce the number of variables to analyse to something that is more manageable: enter principal components analysis (PCA). PCA is a commonly used data reduction technique. It is a technique very closely related to factor analysis. I'm going to write a future blog about the differences between the two techniques. In brief, given the computational power of modern computers, there is probably not much reason to ever perform a PCA rather than a FA. However, PCA has "first mover advantage" and appears here to stay for the short to medium term future.
If you really think you need to do a PCA rather than a FA (and there may be reasons for doing so), then I hope you enjoy this video series I put together demonstrating the technique and how to interpret the output.
