Principal component and factor analysis
Principal component analysis and factor analysis are some of the most confusing techniques in statistics, perhaps because they are very similar in so many ways. On the surface, these two methods appear to be the same, yet there is a huge difference between them that can significantly affect how you use them for data analysis. Below are some of the most notable similarities: They are both data reduction methods, meaning, they allow you to determine the variance in smaller sets of variables They are both run in a statistics program using a similar procedure and the resulting output looks relatively the same They both use the same steps to select the number of components or factors; extraction, interpretation, and rotation However, despite the techniques displaying all these resemblances, there is a substantial difference between them – principal component analysis measures the linear combination of variables while factor analysis measures a latent or hidden variable. Let’s look into each of them in detail.