The Grade of Membership (GOM) model is a general multivariate procedure for analyzing high dimensional discrete response data. It does this by estimating, using maximum likelihood principles, two types of parameters. One describes the probability that a person who is exactly like one of the K analytically defined types has a particular response on a given variable. The second describes each individual's degree of membership in each of the K types. This "partial" membership score reflects the logic of the fuzzy partitions (rather than of discrete groups) that are employed in the analyses. By modifying the probability structure of the basic model we show how the procedure can be applied to a number of different types of data and analytic problems. The utility of the different GOM models for different types of aging research is discussed.