Many researchers studying the effectiveness of working in groups have compared group performance with the scores of individuals combined into nominal groups. Traditionally, methods for forming nominal groups have been shown to be poor, and more recent procedures (Wright, 2007) are difficult to use for complex designs and are inflexible. A new procedure is introduced and tested in which thousands of possible combinations of nominal groups are sampled. Sample characteristics, such as the mean, variance, and distribution, of all these sets are calculated, and the set that is most representative of all of these sets is returned. The user can choose among different ways of conceptualizing the meaning of most representative, but on the basis of simulations and the fact that most subsequent statistical procedures are based on the mean and variance, we argue that finding the set with the mean and variance most similar to the means of the representative statistics for all of the sets is the preferred approach. The algorithm is implemented in a stand-alone C++ executable program and as an R function. Both of these allow anyone to use the procedures freely.