This article explores the impact of the temporal design, i.e. the sampling of times of measurement, on the statistical and substantive conclusions drawn from longitudinal biomedical and social science research. It is shown that for a study of a given duration, if observations are spaced too far apart the resulting data can support misleading conclusions, whereas if observations are spaced relatively close together, a much more veridical picture of the process of interest is provided. The application of these ideas in several areas is discussed, including correlation and regression analysis where a variable measured at one time is used to predict a variable measured at a later time; growth curve analyses; and analyses involving stage-sequential processes. We argue that longitudinal designs should relate the choice of timing and spacing of observations in longitudinal studies to characteristics of the processes being measured. In addition, consideration of the possible effects of measurement design on results of statistical analyses may aid in their interpretation. New approaches involving intensive data collection with much shorter measurement intervals, such as Ecological Momentary Assessment, are promising, but are costly and are not suitable for every research question. More information is needed to help guide researchers in their choice of temporal design.