Correspondence analysis (CA) is a generalized principal component analysis tailored for the analysis of qualitative data. Originally, CA was created to analyze contingency tables, but CA is so versatile that it is used with a number of other data table types.
The goal of CA is to transform a data table into two sets of factor scores: one for the rows and one for the columns. The factor scores give the best representation of the similarity structure of the rows and the columns of the table. In addition, the factors scores can be plotted as maps, which display the essential information of the original table. In these maps, rows and columns are displayed as points whose coordinates are the factor scores and whose dimensions are called ...
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