Principal Component Analysis

Principal Component Analysis and Factor Analysis are data reduction methods to re-express multivariate data with fewer dimensions.  Factor analysis assumes the existence of a few common factors driving the variation in the data, while principal component analysis does not.  These methods are used after conducting surveys to “uncover” the common factors or obtain fewer components to be used in subsequent analysis.   


Principal Component Analysis and Factor Analysis: topics covered
  • PCA methodology
  • Component/factor retention
  • Component/factor rotation (orthogonal vs. oblique)
  • When to use PCA
  • Exploratory Factor Analysis methodology


Principal Component Analysis and Factor Analysis


Principal Component Analysis and Factor Analysis Example


Principal Component Analysis and Factor Analysis in Stata


Principal Component Analysis and Factor Analysis in R


Principal Component Analysis and Factor Analysis in SAS