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- Publisher
- Wiley
- Copyright
- Copyright © 1999 Wiley Subscription Services, Inc., A Wiley Company
- ISSN
- 8755-0024
- eISSN
- 1099-0747
- DOI
- 10.1002/(SICI)1099-0747(199903)15:1<1::AID-ASM356>3.0.CO;2-F
- Publisher site
- See Article on Publisher Site
Abstract
The aim of this paper is to give an overview of the methodological contribution given by Italian researchers in introducing a priori information into multidimensional data analysis techniques, paying special attention to categorical variables. The basic method is Non‐Symmetrical Correspondence Analysis, which enables the analysis of a contingency table when the behaviour of one variable is supposed to be dependent on the other cross‐classified variable. As usual correspondence analysis decomposes an association index (Pearson's Φ2), in a principal component sense, the proposed method is based on a decomposition of a predictability index (Goodman and Kruskal's τb).
Journal
Applied Stochastic Models and Data Analysis – Wiley
Published: Mar 1, 1999
Keywords: ; ; ;