Individual differences in development or growth are typically handled under conventional analytical approaches by blocking on the variables thought to contribute to variation, such as sex or age. But such approaches fail when the differences are attributable to latent characteristics (i.e., variables not directly observable beforehand) within the population. Strategies are proposed herein for identifying and describing individual differences in such cases, using a combination of empirical Bayes methods and finite mixture modeling. Illustrations are provided using data from a longitudinal study of perceptual development, but such strategies are applicable within any domain for which one has repeated measures. The examples illustrate the importance of considering the possibilities of subgroups within the larger population and the perils of ignoring population heterogeneity when it exists.