As previously indicated, the goal of age-period-cohort (APC) analysis is to distinguish and statistically estimate the unique effects associated with age, period, and cohort. The extent to which this goal can be realized depends on research designs and modeling strategies. In this chapter, we focus on three research designs that allow analysts to distinguish age, period, and cohort effects and thus are more suitable for APC analysis than cross-sectional or single-cohort panel designs. We identify two to three prototypical datasets that characterize the application of APC analysis in each of three designs. In each case, we commence with statements of substantive problems that arise from the scientic literature and then describe data that will be analyzed in subsequent chapters to address these problems. We draw examples on a wide variety of topics throughout the book but focus on aging, longevity, and health disparities, as these are problems that have long histories of temporal analysis and for which APC analysis is highly salient.