To answer questions about how students’ proficiencies are changing over time, educational researchers are looking for data sources that span many years. Clearly, for answering questions about student growth, a longitudinal study—in which a single sample is followed over many years—is preferable to repeated cross-sectional samples—in which a separate sample is taken every year. Repeated cross-sectional studies, such as the National Assessment of Educational Progress (NAEP), however, are often readily available. Repeated cross-sectional studies conflate several sources of variability (differences in the initial status of individuals, individual differences in the growth curves, and individual-by-measurement-occasion differences) in ways that are not easily separated. Although repeated cross-sectional studies can provide information about the growth of the averages, the growth of the averages corresponds to the average of the growth curves only in very restricted circumstances. This paper reviews the literature on modeling growth with an eye to characterizing the limitations of repeated cross-sectional studies and understanding the sensitivity of the results to key decisions (particularly, choices of cut points). In most cases, repeated cross-sectional studies should be used to confirm and contextualize the results of more targeted longitudinal studies.