Principal Components and Factor Analysis

  • Brian Everitt
  • Sophia Rabe-Hesketh
Part of the Statistics for Biology and Health book series (SBH)

Abstract

In this chapter, two methods of examining the relationships among a set of variables will be examined. The first, principal components analysis (PCA), is essentially a method of data reduction that aims to reduce the dimensionality of multivariate data and, thus, aid in its understanding. The second technique to be discussed is exploratory factor analysis, which has somewhat similar aims to principal components analysis, but in addition tries to uncover something more fundamental about the data.

Keywords

Principal Component Analysis Correlation Matrix Exploratory Factor Analysis Principal Component Score Cumulative Proportion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Brian Everitt
    • 1
  • Sophia Rabe-Hesketh
    • 1
  1. 1.Biostatistics and Computing DepartmentInstitute of PsychiatryLondonUK

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