Martin, I'm not sure if this is a distinction that necessarily warrants using the logit vs. probit (in the binary response case), but in the logit model the errors follow a logistic distribution and the variance is (3.14^2)/3 = 3.29 whereas for the probit model the distribution of the errors are assumed to follow a normal distribution (I believe the variance is standardized to = 1.0)..........so, I suspect one decision point is whether you assume your errors are normally distributed or not. On page 83 in Regression models for Categorical and Limted DV's Long states that "the choice between logit and probit models is largely one of convenience and convention, since the substantive results are generally indistinguishable....for some users, the simple interpretation of logit coefficients as odds ratios is the deciding factor..or, if an analysis also includes equations with a nominal DV, the logit model may be preferred since the probit model for nominal DV's is computationally!
too demand...in other cases, the need to generalize a model may be an issue. For example, multiple-equation systems involving qualitative DV's are basd on the probit model..................."
Martin, I'm not sure this helped clarify probit vs logit, but awhilst ago I ran analysis using both models, and if I recall correctly my interpretation was not substantively different. I suppose one of the key decisions is your assumption about error variance/error distribution.
Dale Glaser
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