I'm working with R and want to run a correspondance analysis on a dataset containing, among the others, the following factors:
city district: 27 levels, each corresponding to a given district
objective 1, objective 2, ... - factors with two levels (yes/no) corresponding to whether or not a person has chosen a given objective as important to him.
The question is: Does it matter if I transform the single variable city district into 27 binary variables? Would it impact the outcome of a correspondence analysis, including how good the data variance is described by each of the principal components?