This research provides a demonstration of the utility of mixture Rasch models (MRMs) for the analysis of survey data. Specifically, a framework based on a mixture partial credit model (MPCM) will be presented. MRMs are able to provide information regarding latent classes (subpopulations without manifest grouping variables) and separate item parameter estimates for each of these latent classes. Analyses can provide insight into how a survey scale is functioning and how survey respondents differ from one another. The paper provides a detailed example with real survey data from a higher education survey administered to college seniors through all stages of model estimation and selection, description of model results, and follow-up analyses using the MRM results. The results found three distinct classes and discussed each class in terms of the pattern of item parameter estimates within class. The paper also investigated differences of class assignment based on the college the student belongs to on campus.