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This is a method for multidimensional quantification of categorical data in a two-way table (e.g., a contingency table, an examinees-by-questions table of multiple-choice responses). The word “dual” reflects its symmetric scaling of rows and columns of a table. Dual scaling determines weights for options of multiple-choice items in such a way that scores of the subjects, given by weighted sums of weights of chosen options, attain greatest discriminability, hence, maximal internal consistency; in turn, those option weights are expressed as weighted sums of scores of the examinees who chose the options. This property is often referred to as Louis Guttman's principle of internal consistency. Frederic M. Lord verified that such a weighting scheme as this maximizes the internal consistency reliability of the derived scores.

Historical Development

Early work on multidimensional quantification of categorical data can be traced back to Marion Richardson and G. Frederick Kuder in 1933, using what Paul Horst in 1935 called the method of reciprocal averages. H. O. Hirschfeld in 1935 presented a mathematically equivalent method, simultaneous linear regressions, as termed by James C. Lingoes in 1964. Ronald A. Fisher in 1940 proposed discriminant analysis of categorical data, and K. Maung in 1941 showed that Fisher's quantification method could be equivalently formulated by four distinct objective functions. Whereas Maung's paper was for the contingency table, Guttman in 1941 presented three mathematically equivalent approaches to the quantification of multiple-choice data, also extending it to paired comparison and rank order data in 1946. The foundation for quantification was firmly laid by 1946. In 1950, Chikio Hayashi launched a project on Hayashi's quantification theory in Japan, Type III of which corresponds to dual scaling of the contingency table. In the early 1960s, Jean-Paul Benzécri started research in France, which was developed into correspondence analysis for the contingency table and multiple correspondence analysis for multiple-choice data. Both Hayashi and Benzécri had many followers, as referred to by the Hayashi School and the Benzécri School of data analysis. Similar consorted efforts led to the Leiden Group (headed by Jan de Leeuw) in the Netherlands and the Toronto Group (headed by Shizuhiko Nishisato) in Canada in the late 1960s.

A plethora of names have been proposed for the method, such as Guttman scaling (Guttman); the method of reciprocal averages (Richardson, Kuder, Horst); simultaneous linear regressions (Hirschfeld); Fisher's appropriate scoring (Fisher); Hayashi's theory of quantification Type III (Hayashi); optimal scaling (R. Darrell Bock); correspondence/multiple-correspondence analysis (Benzécri, Brigitte Escofier); biplot (Ruben Gabriel, John Gower, David Hand); homogeneity analysis (de Leeuw, Willem Heiser, Jacqueline Meulman); and dual scaling (Nishisato). The name dual scaling was proposed in 1976 in response to the criticism that the popular name optimal scaling was not specific enough to describe the method. According to Meulman in 1998, dual scaling is a “comprehensive framework for multidimensional analysis of categorical data,” (p. 291) because it handles a wider range of categorical data than such methods as correspondence analysis and optimal scaling, of which applications are mainly to incidence data.

Basic Formulas

The method employs singular value decomposition for categorical data. Consider a two-way contingency table with the typical element being the frequency fij in row i and column j. The singular value decomposition of this element can be expressed

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