Understanding individual differences in behavior has always been troublesome for many social science methodologies. Such differences often are well modeled by latent class methodology viewed from the perspective of finite mixture theory. One impediment to application of mixture models has been the general inaccessibility of such models for the general research community. A more serious conceptual problem, however, is that the form of component distributions must be specified a priori, and there is typically no theory to guide this selection, especially in exploratory settings where their use could be most revealing. This research focuses on the development and extension of a binomial mixture model as a solution to this problem. The investigator will conduct both Monte Carlo and analytical work. The model has applications both in survey research and in substantive areas such as developmental psychology.
