Correspondence analysis as an aid to multicriteria decision making

Correspondence analysis is a relatively little used multivariate statistical technique in the English speaking world. This study aims to show how the technique can be used as an aid to multiple criteria decision making. Based on expressed pairwise indifferences, pairs of alternatives are classified into one of the three categories: ‘indifferent’, ‘indifferent-indifferent’ and otherwise. A coefficient of similarity is assigned to each category, and using this information, correspondence analysis is used to display these alternatives onto a map. The first principal axis of the map tends to be the axis of preference and groups of the most preferred and least preferred policies may be identified. Application of the method is further examined in the case of incomplete information and is found to be capable of providing an ‘accurate’ result. The maps are also compared with those obtained by Rivett using multidimensional scaling.