In this study, we have employed the GSK and the non-standard log-linear model approaches to fit the class of distance sub-symmetry models to square contingency tables having ordered categories. SAS PROCs CATMOD and GENMOD were employed to implement our models. A macro generates the factor variables to implement models in the latter approach. Except for the DCS-k where no maximum likelihood closed form exists, all other models are easily implemented with the non standard log-linear model approach. The GSK on the other hand readily fits all the models considered in this study. These models are applied to the British generational data as well as the unaided distance vision data. Both data have received considerable attention and analyses in the literature. Results obtained where applicable agree with those published in previous literature on the subject. The approaches suggest here eliminate any programming that might be required in order to apply these class of models to square contingency tables.