ABSTRACT
The appropriate choice of a measure of association is more than merely a purist's concern, for the conclusions reached in the analysis of a given set of data can depend crucially on the measure employed. A general framework has been proposed for constructing measures of association for bivariate ordinal hypotheses. The concern is with the problem of a researcher, who has formulated a bivariate hypothesis—that is, a proposition asserting a relation between two variables—and must choose an appropriate measure of association. A bivariate ordinal hypothesis asserts a relation between two ordinal variables. Although ordinal variables have a number of unsatisfactory characteristics, they are likely to remain a prominent feature of empirical social research for some time to come. For ordinal data, numerical algebraic models are unavailable, and so three general types of ordinal relations are identified: no-reversals, asymmetric, and strict.
