ABSTRACT
Up to now we have analysed the association between two categorical variables or between two sets of categorical variables where the row variables are different from the column variables. In this and the two following chapters we turn our attention to the association within one set of variables, where we are interested in how strongly and in which way these variables are interrelated. In this chapter we will concentrate on the two classic ways to approach this problem, called multiple correspondence analysis , or MCA for short. One way is to think of MCA as the analysis of the whole data set coded in the form of dummy variables, called the indicator matrix , while the other way is to think of it as analysing all two-way cross-tabulations amongst the variables, called the Burt matrix . These two ways are very closely connected, but suffer from some deficiencies which we will try to correct in the following chapter, Chapter 19, where several improved versions of MCA are presented.
