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Principal Component Analysis

  1. Mary E. Kelley

Published Online: 30 JAN 2010

DOI: 10.1002/9780470479216.corpsy0709

Corsini Encyclopedia of Psychology

Corsini Encyclopedia of Psychology

How to Cite

Kelley, M. E. 2010. Principal Component Analysis. Corsini Encyclopedia of Psychology. 1.

Author Information

  1. Emory University

Publication History

  1. Published Online: 30 JAN 2010

Abstract

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.

Keywords:

  • psychometrics;
  • factor analysis;
  • data reduction;
  • latent variable