Past research finds that people prefer to sit next to others who are similar to them in a variety of dimensions such as race, sex, and physical appearance. This preference for similarity in seating arrangements is called aggregation and is most commonly measured with the aggregation index (Campbell, Kruskal, & Wallace, Sociometry 29, 1–15, 1966). The aggregation index compares the observed dissimilarity in seating with the amount of dissimilarity that would be expected if seats were chosen randomly. However, the current closed-form equations for this method limit the ease, flexibility, and inferences that researchers have. This paper presents a new approach for studying aggregation that uses bootstrapped resampling of the seating environment to estimate the aggregation index parameters. This method, compiled as an executable program, SocialAggregation, reads a seating chart matrix provided by the researcher and automatically computes the observed number of dissimilar adjacencies, and simulates random seating preferences. The current method’s estimates not only converge with those of the original method, but it also handles a wider variety of situations and also allows for more precise hypothesis testing by directly modeling the distribution of the seating arrangements. Developing a better measure of aggregation opens new possibilities for understanding intergroup biases, and allows researchers to examine aggregation more efficiently.