Missing data are a common problem in adulthood and aging research. This entry overviews the problem and possible solutions. It begins by offering a definition of missing data, with examples. It presents a widely applied taxonomy of mechanisms that create the missing data. It then considers common, but limited, approaches: complete-cases, available cases, weighting analyses, and single imputation. More principled methods, namely multiple imputation, maximum likelihood, and inference from Bayesian posterior distributions, are then discussed.