The traditional goals of longitudinal studies are many: consideration of stability and change; description of patterns of development and behavior; and understanding of the processes involved in disease, including disease onset, recovery, response to treatment, natural history of the aging process, and identification of factors that predict age-related outcomes. Researchers in aging seek to unravel the impact and interaction of physical and psychological processes on human development, health, and disease. From the point of view of statistical analysis, the critical aspect of data obtained from longitudinal studies is the inherent correlational structure of multiple measurements made on a single subject or other experimental unit, which must be appropriately treated in the analysis of the data. We discuss a series of nonparametric approaches that are both analytically accessible and particularly well suited to the analysis of sparse or otherwise limited longitudinal data.