Second, the GGUM allows for a distinctively different use of response categories across items. It does this by implementing response category threshold parameters that vary across items. A marginal maximum likelihood algorithm is implemented to estimate GGUM item parameters, whereas person parameters are derived from an expected a posteriori technique. Recovery simulations suggest that accurate item parameter estimates can be obtained with approximately 750 subjects. Additionally, accurate person estimates are derived with approximately 20 6-category items. The applicability of the GGUM to common attitude testing situations is illustrated with real data on student attitudes toward abortion.