The analysis of a subset of categories is a method that has very recently been developed by Greenacre and Pardo (2006). Advanced Multiple Correspondence Analysis This adjustment allows us to have higher and more meaningful percentages for the maps. Greenacre (1993) suggested an adjusted version of inertia, inspired from Joint Correspondence Analysis (JCA). As a matter of fact, the inertia does not only depend on the degree of association between the categories but is seriously inflated. XLSTAT also computes MCA by using the Burt table instead of the disjunctive table. In the case of MCA one can show that the total inertia is equal to the average number of categories minus one. How does Multiple Correspondence Analysis workĪ series of transformations allows the computing of the coordinates of the categories of the qualitative variables, as well as the coordinates of the observations in a representation space that is optimal for a criterion based on inertia. Multiple Correspondence Analysis (MCA) can also be understood as a generalization of Correspondence Analysis (CA) to the case where there are more than two variables. One can obtain maps where it is possible to visually observe the distances between the categories of the qualitative variables and between the observations. MCA is to qualitative variables what Principal Component Analysis is to quantitative variables. Multiple Correspondence Analysis ( MCA) is a method that allows studying the association between two or more qualitative variables.
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