Hi everyone, I am in the process of fitting a large number of binary logit models in a data mining type of project and have a general question about model comparison/selection: I have many fitted models with a single explanatory variable as an initial step for reducing the list of potential predictors. Some of the models obtain low AIC and very large Wald Chi Sq test stat values for the predictor, however, when I check the classification matrix, these models do NOT differentiate between the 2 responses at all: They predict all obs into 1 response category. Normally, I also look at hit rates, but this of course seems invalid when all predictions are of the same response category. Many of these models obtain lower AIC values and higher ChiSQ values than the models that do differentiate between the 2 responses. Why does this happen and what are best practices in this situation? Also, can anyone suggest a good web resource for reading more about this issue and logistic model comparison/selection in general? Thanks! Dan You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.