Dimension reduction for categorical/character variables

[littlestone]

Hello all

Suppose I have many variables (both numerical and character) and I want to reduce them.

I know Proc VARCLUS can work for correlated numerical variables, howvere, is there any method that can work for categorical/character variables?

[PGStats]

It is not clear what kind of reduction you are seeking, but look at correspondance analysis (proc corresp)

PG

[littlestone]

I apologize for not expressing my questions clearly:

My goal of reduction is to reduce the number of categorical/character variables; for example, say I have 200 character variables, I want to reduce this number to 30 (or 50) which is more amenable to analysis (such as building a logistic regression model).

[Rick_SAS]

Your choices vary according to whether your variables are ordinal or nominal, but look at the DISTANCE procedure and the various proximity measures it provides. Some are applicable for discrete variables.  You can then get a "distance" matrix (dis/similarity matrix) that can be used to cluster variables.

I haven't done this myself (I use correpondence analysis instead) butI think it will work.