Abstract
This paper is concerned with measuring influence of rows and columns on the eigenvalues obtained in correspondence analysis (CA) of two-way contingency tables. As in principal component analysis, the eigenvalues are of great importance in CA. The goodness of a two dimensional correspondence plot is determined by the ratio of the sum of the two largest eigenvalues to the sum of all the eigenvalues. By investigating those rows and columns with high influence, a correspondence plot may be improved. In the paper the influence function (IF) of rows on the eigenvalues is derived along with its sample version, the empirical influence function (EIF). A numerical example is presented to evaluate the EIF. The sample influence function (SIF) is also evaluated to check the adequacy of the EIF.
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