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

pca_gsp.dta

Principal Component Analysis R Program and Output

Principal Component Analysis in R.R

pca_gsp.csv

Principal Component Analysis SAS Program and Output

Principal Component Analysis in SAS.sas

pca_gsp.csv

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