Heteroskedasticity is a very different problem in models like -probit- and -logit-. Think of it this way: your dependent variable is a
probability. A probabiltiy embodies uncertainty, and that uncertainty comes from all variables we have not included in our model. In one sense this makes it very easy to deal with heteroskedasticity: We just define our dependent variable of interest to be the probability given the control variabels in our model. The results of your model give an accurate description of what you have found in your data. However, we often want to give parameters a counterfactual interpretation (e.g. "if the men suddenly became women, then the probabiltiy changes by x percentage points"). Such a counterfactual interpretation is only correct if we can assume that there is no heteroscedasticity. Several solutions have been proposed and I trust none of them: they are just
too sensitive. If you really want to do something about it, than I you'll really need to do some reading. Since these models are so
sensitive, you really need to know what you are doing. A good entry point for that literature is (Williams 2009). But my position is that that problem is basically unsolvable, so not worth worrying about.
Hope that helps,
- Williams, R. 2009. Using heterogenous choice models to compare logit and probit coefficients across groups. Sociological Methods & Research 37: 531--559.