Principal Components Analysis (PCA)

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.