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Principal Components, Factors and Correspondence Analysis

  • J. D. Jobson
Part of the Springer Texts in Statistics book series (STS)

Abstract

In exploratory studies, researchers often include as many variables as possible to ensure that no relevant variables will be omitted. The resulting data matrices can sometimes be large and difficult to analyze, particularly if the level of correlation among the variables is high. In techniques such as multiple regression and discriminant analysis, variable selection procedures can be employed as a data reduction technique; however this method can result in the loss of one or more important dimensions. An alternative approach is to use all of the variables in X to obtain a smaller set of new variables that can be used to approximate X. The new variables are called principal components or factors and are designed to carry most of the information in the columns of X. The higher the level of correlation among the columns of X the fewer the number of new variables required. The techniques of principal components analysis and factor analysis are examples of data reduction techniques.

Keywords

Correspondence Analysis Multiple Correspondence Analysis Factor Analysis Model Total Inertia Robust Principal Component Analysis 
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 1992

Authors and Affiliations

  • J. D. Jobson
    • 1
  1. 1.Faculty of BusinessUniversity of AlbertaEdmontonCanada

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