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The traditional methods of finding mixture components of rank data are mostlybased on distance and latent class models; these models may exhibit thephenomenon of masking of groups of small sizes; probably due to the sphericalnature of rank data. Our approach diverges from the traditional methods; it isdirectional and uses a logical principle, the law of contradiction. We discussthe concept of a mixture for rank data essentially in terms of the notion ofglobal homogeneity of its group components. Local heterogeneities may appearonce the group components of the mixture have been discovered. This is done viathe exploratory analysis of rank data by taxicab correspondence analysis withthe nega coding: If the first factor is an affine function of the Borda count,then we say that the rank data are globally homogenous, and localheterogeneities may appear on the consequent factors; otherwise, the rank dataeither are globally homogenous with outliers, or a mixture of globallyhomogenous groups. Also we introduce a new coefficient of global homogeneity,GHC. GHC is based on the first taxicab dispersion measure: it takes valuesbetween 0 and 100\%, so it is easily interpretable. GHC measures the extent ofcrossing of scores of voters between two or three blocks seriation of the itemswhere the Borda count statistic provides consensus ordering of the items on thefirst axis. Examples are provided. Key words: Preferences; rankings; Bordacount; global homogeneity coefficient; nega coding; law of contradiction;mixture; outliers; taxicab correspondence analysis; masking.

Globally Homogenous Mixture Components and Local Heterogeneity of Rank Data