Correspondence analysis

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Correspondence analysis (CA) or reciprocal averaging is a multivariate statistical technique proposed[1] by Herman Otto Hartley (Hirschfeld)[2] and later developed by Jean-Paul Benzécri.[3] 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 on the same scale for CA to be applicable, keeping in mind that 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.[4][5]


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.


From table C, compute a set of weights for the columns and the rows (sometimes called masses),[6][7] where row weights are

and column weights are

where is the total number of observations and is a column vector of ones with the appropriate dimension.

Next, compute a table S, where C is divided by the sum of C

Finally, compute a table M from S and the weights as such

Interpretation of preprocessing[edit]

The vectors and give the marginal probabilities of being the row and column classes, respectively, while gives the joint probability distribution of rows and columns. Therefore gives deviations from independence. These deviations, squared and appropriately scaled, are summed up to yield the chi-squared statistic on .

Orthogonal components[edit]

The table M is then decomposed with the generalized singular value decomposition where the left and right singular vectors are constrained by weights. The weights are diagonal tables


where the diagonal elements of are and the off-diagonal elements are all 0.

M is then decomposed via the generalized singular value decomposition


Factor scores[edit]

Factor scores for the row items of table C are

and for the column items

Extensions and applications[edit]

Several variants of CA are available, including detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA). The extension of correspondence analysis to many categorical variables is called multiple correspondence analysis. An adaptation of correspondence analysis to the problem of discrimination based upon qualitative variables (i.e., the equivalent of discriminant analysis for qualitative data) is called discriminant correspondence analysis or barycentric discriminant analysis.

In the social sciences, correspondence analysis, and particularly its extension multiple correspondence analysis, was made known outside France through French sociologist Pierre Bourdieu's application of it.[8]


  • The data visualization system Orange include the module: orngCA.
  • The statistical system R includes the packages: MASS, ade4, ca, vegan, ExPosition, andFactoMineR which perform correspondence analysis and multiple correspondence analysis.

See also[edit]


  1. ^ Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP ISBN 0-19-850994-4
  2. ^ Hirschfeld, H.O. (1935) "A connection between correlation and contingency", Proc. Cambridge Philosophical Society, 31, 520–524
  3. ^ Benzécri, J.-P. (1973). L'Analyse des Données. Volume II. L'Analyse des Correspondances. Paris, France: Dunod.
  4. ^ Greenacre, Michael (1983). Theory and Applications of Correspondence Analysis. London: Academic Press. ISBN 0-12-299050-1.
  5. ^ Greenacre, Michael (2007). Correspondence Analysis in Practice, Second Edition. London: Chapman & Hall/CRC.
  6. ^ Greenacre, Michael (1983). Theory and Applications of Correspondence Analysis. London: Academic Press. ISBN 0-12-299050-1.
  7. ^ Greenacre, Michael (2007). Correspondence Analysis in Practice, Second Edition. London: Chapman & Hall/CRC.
  8. ^ Bourdieu, Pierre (1984). Distinction. Routledge. p. 41. ISBN 0674212770.

External links[edit]

  • Greenacre, Michael (2008), La Práctica del Análisis de Correspondencias, BBVA Foundation, Madrid, Spanish translation of Correspondence Analysis in Practice, available for free download from BBVA Foundation publications
  • Greenacre, Michael (2010), Biplots in Practice, BBVA Foundation, Madrid, available for free download at