The theoretical aims of the project were to explore latent trait theory and robust methodologies in an attempt to derive estimation schemes that would yield correct and useful results in the face of contamination. In both biological and psychophysical applications of measurement models, the assumption of a single underlying dimension (a latent trait) has been coupled with some response function. This implies that as the latent trait escalates, the probability of a positive response on a specified dependent variable increases. In this study, the technique developed for parameter estimation in latent trait analysis combines traditional Jackknifing with the Sine M-Estimate to yield improved accuracy and efficiency of estimation. Asymptotically optimal methods of parameter estimation were found not to be necessarily optimal for small samples, nor were they found to be necessarily robust against departures from their assumptions in large samples. The new method, the AMT-Jackknife, was found to be superior to all other tested procedures (including maximum likelihood) in extensive computer simulations. In applying the new technique to the prediction of recidivism among a sample of Federal parolees, accuracy was improved significantly. Tabular data and four references are provided. (Author abstract modified)