One method is simply to perform all possible logistic regressions, which may be feasible if the number of candidate X variables is small. The regression equations with the smallest values of the Akaike information criterion and the Schwartz criterion can then be examined further to see which seem the most reasonable. When comparing many possible models, some of which may have numerous X variables, remember that the greater the number of X variables, the smaller the number of trials at each unique combination of X variable values, and thus the greater the chance of separation in the data, or small cell sample sizes . If there are too many X variables to examine each possible regression, then stepwise regression is often used. At each step, the decision to add or drop an X variable is based on a test of whether that variable will or does make a statistically significant contribution to the model.
Stepwise regression identifies a single regression instead of several possible candidates.
Such models should always be validated. First, make sure that the model makes sense theoretically, and is comparable to any results from fitting to other data sets. Then, try the model out on new data to see if it still holds. One validation method is to divide the data set into two parts, using one to fit the equation, and the other to decide whether it is a reasonable model. Validation is vital when using a stepwise procedure.