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By David K. Hildebrand, James D. Laing, Howard Rosenthal

Ordinal facts should be rank ordered yet now not assumed to have equivalent distances among different types. utilizing help by way of judges for civil rights measures and bussing because the fundamental instance, this paper shows how such facts can most sensible be analyzed.

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Analysis of ordinal data

Ordinal facts might be rank ordered yet no longer assumed to have equivalent distances among different types. utilizing aid by means of judges for civil rights measures and bussing because the basic instance, this paper shows how such facts can top be analyzed.

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It turns out, as we shall see, that most of the existing measures of ordinal association can be given a unified interpretation via this benchmark. The replication procedure, as applied to the data in Table 1, follows. Thirteen of the 31 bureaucrats are selected at random. For each of these we predict left or left-center, the same prediction we made earlier for Democrats. Error occurs if the case is center, right center, or right. Similarly, we predict center for nine cases drawn at random, thus replicating the pre- Page 20 dictions made earlier for Independents.

Issue 2: Predicting Single Observations vs. Pairs of Observations A much more basic issue in dealing with ordinal association is whether the analysis should focus directly on single observations or on comparisons within pairs of observations. To clarify the distinction, we need to address an analytical problem involving two variables. The two variables describing bureaucratsnamely, party affiliation and support for social serviceswill serve to illustrate the general analysis of association for ordinal variables.

The researcher will use the ordinality to impose some constraints as to the configuration, pattern or "shape" of the unshaded success cells. " Except in very unusual circumstances, there should be a single bloc of shaded cells or of unshaded cells in a given column or row. A prediction like that shown in Table 4 leads to a configuration of cells that seems unlikely to arise from a theory relating two ordinal variables. Thus, when variables are ordinal, the ordinality will influence the prediction, even for single observations.

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