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.
Handouts, Programs, and Data
Principal Component Analysis and Factor Analysis
Principal Component Analysis and Factor Analysis Example
Principal Component Analysis Stata Program and Output
Principal Component Analysis in Stata.do
Principal Component Analysis R Program and Output
Principal Component Analysis in R.R
Principal Component Analysis SAS Program and Output
Principal Component Analysis in SAS.sas
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