Multiple Factor Analysis by Example Using R

Chapter 3

Factorial Analysis of Mixed Data

The need to introduce simultaneously quantitative and qualitative variables (known as mixed data) as active elements of one factorial analysis is common. The usual methodology is to transform the quantitative variables into qualitative variables, breaking down their variation interval into classes, and submitting the resulting homogeneous table to a multiple correspondence analysis (MCA). This methodology is relatively easy to implement and is used whenever there are enough individuals; generally more than 100, a limit below which MCA results are not very stable.

In two cases, there are advantages to conserving the quantitative variables:

1. When the number of qualitative variables is very low compared ...