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Abstract: Correspondence analysis appears to be sensitive to modalities with low margins leading to outliers. We use the notions of influence function and Hadamard differentiability to provide a criterion for deciding when such an outlier can be considered as too influential. [Copyright &y& Elsevier]

Since correspondence analysis appears to be sensitive to outliers, it is important to be able to evaluate the sensitivity of the data on the results. This article deals with measuring the influence of rows and columns on the results obtained with correspondence analysis. To establish the influence of individuals on the analysis, we use the notion of influence curve and we propose a general criterion based on the mean square error to measure the sensitivity of the correspondence analysis and its robustness. A numerical example is presented to illustrate the notions developed in this article. [ABSTRACT FROM AUTHOR]