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correspondence analysis
Correspondence analysis (CA) is a multivariate statistical technique proposed〔Dodge, Y. (2003) ''The Oxford Dictionary of Statistical Terms'', OUP ISBN 0-19-850994-4〕 by Hirschfeld〔Hirschfeld, H.O. (1935) "A connection between correlation and contingency", ''Proc. Cambridge Philosophical Society'', 31, 520–524〕 and later developed by Jean-Paul Benzécri. It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data. In a similar manner to principal component analysis, it provides a means of displaying or summarising a set of data in two-dimensional graphical form.
All data should be nonnegative and on the same scale for CA to be applicable, and the method treats rows and columns equivalently. It is traditionally applied to contingency tables — CA decomposes the chi-squared statistic associated with this table into orthogonal factors. Because CA is a descriptive technique, it can be applied to tables whether or not the statistic is appropriate.
== Details ==
Like principal components analysis, correspondence analysis creates orthogonal components and, for each item in a table, a set of scores (sometimes called factor scores, see Factor analysis). Correspondence analysis is performed on a contingency table, ''C'', of size ''m×n'' where ''m'' is the number of rows and ''n'' is the number of columns.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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