If you have more than 2 groups, DFA is the only choice. When to use it, and when to use logistic regression I just want to talk very briefly about logistic regression. Logit analysis is regression of a series of independent variables on a binary dependent variable. This is still fairly rare, because few people have the tools and skills to work with it. There are forms that use logit regression and forms that use discriminant functions, but basically what it does is to help understand structure in complex data sets.
Logit regression is a hierarchical dichotomous technique based on intensive succession computations.
For discriminant analysis, it would be the variable that best separates the group means. It then takes those subsets and redivides them, and so on. One of the beauties of logistic regression is that it potentially solves the problem of secondary variation that I presented a few days ago, i.e., that in most data sets, the secondary variation is different along different parts of the primary gradient. A tree analysis will treat each of those separately, so that you don't lose interesting secondary structure in one part of the gradient just because it was subsumed elsewhere. Some of you may have read about classification trees, and regression trees are quite similar.