What is the recommended method of normalising scores derived from the MBC software? I presume that for a top level model it is exp(Utility), re-based to the total, with linear terms converted back to their range, or should one zero centre the utility first within each attribute? For sub-models should this can only be done for respondents who 'qualify' for the submodel (i.e. have an RLH), then optionally multiply the sub-model ZCDs by the 'top model' qualifying values. I'm not sure there is an easy way to approach this but want to know if I'm missing a trick as I get my head around the outputs.