Abstract
Multivariate analysis is now conventional for data treatment. Avaiable methods are classified according to the purpose of the study (prediction, decision making, clustering, classification) and the types of variable (the nature of the information) available. These aspects must be considered simultaneously. Within that frame, correspondence factor analysis (CFA) is proposed as a suitable method for handling categorial variables (qualitative or semiquantitative information) and descriptive decision-making (symmetry between lines and columns). The theoretical principles of CFA are outlined. The utility of CFA is illustrated by three examples: a complex sampling design related to use of sewage sludge, assessment of the selectivity of insect behaviour towards chemical products in trees, and an environmental study of mercury contamination of freshwater fish.
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