Obviously in linear regression, the coefficient tells you whether the effect of a change in an explanatory variable on the response variable is positive or negative and how much a change of one unit of the explanatory variable effects the reponse variable.
For a binary probit model, I understand you can't interpret the coefficients in the same way. The sign of the coefficient will tell you whether, everything else held constant, a change in a particular explanatory variable has a negative or positive affect on the probability of a response variable being true, but I'm not sure what else can be interpreted apart from that.
I have fitted a binary probit model to a dataset using mainly dummy variables as explanatory variables, and other than saying whether each has a positive/negative effect on the response and the percentage of correctly classified observations the model has when run on the dataset, I'm not sure what else I can say about it. Any ideas?