1. A method for selecting a target group of consumers from a larger group of consumers in a computer database, the method comprising: providing at least non-attitudinal variables for each consumer in the database; choosing a random subgroup of consumers from the larger group; gathering attitudinal data based on attitudinal variables from each member of the subgroup, the attitudinal data being unavailable on the database; creating attitudinal segments defined by attitudinal dimensions based on the attitudinal data; assigning each member of the subgroup to one of the attitudinal segments using the attitudinal data corresponding to each member of the subgroup; identifying a plurality of the non-attitudinal variables for each member of the subgroup based on strength of relationship between each of the non-attitudinal variables of the subgroup members and the dimensions that define each member's corresponding attitudinal segment; calculating a probability score for each member of the subgroup based on a degree of fit between each member of the subgroup and their corresponding attitudinal segment; developing mathematical algorithms each corresponding to a separate one of the attitudinal segments and capable of substantially predicting the probability score for each of the subgroup members with respect to their corresponding attitudinal segment using the identified plurality of non-attitudinal variables and their corresponding calculated probability score; calculating a probability score for each of the consumers in the larger group based on a degree of fit between each of the consumers in the larger group and any of the attitudinal segments by applying at least one of the developed algorithms to each consumer in the larger group; and selecting the target group of consumers from the larger group based on the calculated probability score for each of the consumers of the larger group.