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Correspondence analysis
Following the long-term championing of this technique by Benzecri (eg Benzecri, 1992) and others who termed the process Correspondence Analysis, this term has become the generally accepted name for a group of techniques used various to create a spatial representation of the rows and columns of a contingency table. It is related very closely to singular-value-decomposition and is similar to principal component analysis in that the technique seeks to find linear weights for the categories of two variables such that the correlation between them is maximized. Like eigendecomposition, a second set of weights can be found to provide a maximal correlation that accounts for the residual data after the first, and then a third, and so on. Although it was devised for categorical data, it can be applied to other kinds of data such as ranked or rated variables, and consequently has been introduced as a technique for the representation of repertory grid data in the computer programme of Feixas and Cornejo (2002). The eigendecomposition procedure is identical with that for singular-value-decomposition and the grid may be similarly re-constructed and the fit to the original grid data evaluated. It is similar to singular-value-decomposition in that it can scale rows and columns that have no variation (making it a suitable representation for dependency grids where such an occurrence is common), however it differs from that procedure in that it involves a specified pre-scaling of the grid (by a function of row and column totals). Because of this requirement it cannot be used to provide representations of grids that use negative ratings.


  • Benzecri, J.P. (1992) Correspondence Analysis Handbook. New York: Marcel Dekker.
  • Feixas, G., and Cornejo, J.M. (2002) GRIDCOR: Correspondence analysis for grid data v.4.0. [Computer software] Barcelona: Authors.

Richard C. Bell

Establ. 2003
Last update: 15 February 2004