Principal Component Analysis
Published Online: 30 JAN 2010
Copyright © 2010 John Wiley & Sons, Inc. All rights reserved.
Corsini Encyclopedia of Psychology
How to Cite
Kelley, M. E. 2010. Principal Component Analysis. Corsini Encyclopedia of Psychology. 1.
- Published Online: 30 JAN 2010
Principal component analysis (PCA) is a data reduction technique formalized by Hotelling (1933) and later characterized statistically by Anderson (1963), although the concept goes back as far as Pearson (1901). PCA, as well as factor analysis, is used in the social sciences mainly to characterize underlying latent variables, or factors, that are represented as weighted combinations of the observed variables. The most common use of PCA in the social sciences is in the form of scale construction (psychometrics), although it can also be used to summarize any set of related variables and has more recently been applied to high-dimensional data problems in both imaging and genetics.
- factor analysis;
- data reduction;
- latent variable