# Principal Components Analysis vs. Factor Analysis

Here is an interesting example of how similar mechanics lead to two very different statistical tools. Principal Components Analysis (PCA) is a powerful method for data compression, in the sense of capturing the information contained in a large set of variables by a smaller set of linear combinations of those variables. As such, it is widely used in applications that require data compression, such as visualization of high-dimensional data and prediction. Factor Analysis (FA), technically considered a close cousin of PCA, is popular in the social sciences, and is used for the purpose of discovering a small number of ‘underlying … Continue reading Principal Components Analysis vs. Factor Analysis