Hi parsec2011,

Your interpretation is correct. Fisher's discriminant function scores are proportional to coefficients from a multiple regression with group membership as dependent variable, so yeah, bigger scores (far from zero) are better predictors. Just don't forget that Discriminant Analysis is focused on Discrimination/Classification, so it is usually not the best technique to test which variables are more related with the response.

Now, regarding the change in coefficients, it could probably be due to some violation in the assumptions of LDA. There is a multivariate normality assumption in the Independent Variables. I know you have a binary variable but Discriminant Models are quiet robust against non-normality, I've read that some dichotomous predictors may be used without problems. But there is also an assumption requiring a constant covariance matrix between the two groups. If this assumption is not met, a quadratic discriminant function is required instead of a linear one. I'd fit both models and assess the fit in order to find the one that produces the best results. By the way, the commandqdafits the Quadratic Discriminant Functions, it is also in theMASSlibrary.

Hope this helps