To make scores from tests designed for special populations exchangeable, the tests must first be equated on the same scale. This study examined the utility of a Rasch model in equating motor function tasks. Using an existing gross motor function data set and a semisimulation design, an artificial equating and cross-validation sample, as well as two artificial tests, were created. Based on these samples and tests, the accuracy and stability of Rasch equating was empirically determined using a standardized difference statistic. It was found that Rasch equating could accurately equate tests and was generalizable when applied to a cross-validation sample. After equating, tests can be compared on the same scale, and interpretation of cross-test scores becomes possible. In addition, with the conversion table and graph generated from Rasch equating, the application of test equating was demonstrated as simple and practical.