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Factor Analysis and Risk Perception

  • J. P. Pages
  • J. Brenot
  • M. H. Barny
Part of the Eurocourses: Chemical and Environmental Science book series (EUCE, volume 2)

Abstract

Factorial methods are used by scientists to study statistical relationships between many variables. Among these methods, Principal Component Analysis (PCA) has a major importance because it allows to describe simultaneously two sets, one of individuals and the other of variables, which constitute respectively the lines and the columns of a data array “individuals x variables”. As output, a geometric description is given where individuals and variables appear as points in vector subspaces, and then an analysis based on visualization may follow. PCA is a corner stone technique because many factorial methods can be considered as application of PCA to particular sets of data with particular choices for measuring distances between individuals and the intensity of the relationship between variables.

Keywords

Principal Component Analysis Risk Perception Public Opinion Data Array Canonical Correlation 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 Dordrecht 1991

Authors and Affiliations

  • J. P. Pages
    • 1
  • J. Brenot
    • 1
  • M. H. Barny
    • 1
  1. 1.Commissariat à l’Energie AtomiqueIPSNFontenay-aux-Roses CedexFrance

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