Principal component analysis (PCA) is a statistical tool that condenses the information contained in a large group of independent variables to a more manageable number of variables. This is useful when performing an analysis on data sets with a large number of variables. PCA restructures the original independent variables into new variables called principal components that maximize the information present in the data. The principal components then act as a substitute for the independent variables in an analysis. The purpose of this article is to present PCA in an understandable way for researchers without advanced statistical and mathematical backgrounds. To solidify the comprehension of the process and provide a template for researchers, we present an extended step-by-step example of PCA in use on a fictitious peri-implantitis data set.