Estimating ability parameters in latent trait models in general and in the Rasch Model in particular is almost always hampered by noise in the data. This noise can be caused by guessing, inattention to easy questions, and other factors which are unrelated to ability. It was found that although no one of the tested schemes is uniformly superior to all others, a robustified Jackknife is generally the best scheme; it was also very efficient for tests with 40 or fewer items. The Jackknifing scheme is proposed for estimating ability for practical work. Tabular data and 22 references are provided. (Author abstract modified)