ABSTRACT
As was shown in Chapters 7 and 8, there are several alternative definitions of correspondence analysis (CA) and different ways of thinking about the method. In this book Benze´cri’s geometric approach has been emphasised, leading to data visualization. In Chapters 18 and 19 it was clear that the passage from simple two-variable CA to the multivariate form of the analysis is not straightforward, especially if one tries to generalize the geometric interpretation. An alternative approach to the multivariate case, which relies on exactly the same mathematics as multiple correspondence analysis (MCA), is to see the method as a way of quantifying categorical data, generalizing the optimal scaling ideas of Chapter 7. As before, there are several equivalent ways to think about MCA as a scaling technique, and these different approaches enrich our understanding of the method’s properties. The optimal scaling approach to MCA is often referred to in the literature as homogeneity analysis.
