Unfortunately, three of my main variables are far from being normally distributed - these refer to questionnaire data totals (e.g. on on bullying [DV], in which most children are not bullied, so there is a very strong positive skew). This will not be the case with other data which is likely to be more normally distributed (e.g. academic data).

My question is whether there is a way of having a predictive model that can cater for a number of seriously skewed variables, while others are not. I understand that it is possible to transform data, but wonder if I then need to transform all variables (even those with a normal distribution) in order to keep consistency across variables. Or am I going to be limited to non-parametric correlations?

All advice gratefully received - I am pretty new to this so the learning curve is steep.

Many thanks in advance