Maximum likelihood and Bayesian estimates of proficiency, typically used in adaptive testing, use item weights that depend on test taker proficiency to estimate test taker proficiency. In this study, several methods were explored through computer simulation using fixed item weights, which depend mainly on the items difficulty. The simpler scores involve averages of the weights of the items answered correctly, sometimes also including average weights for the items answered incorrectly. Results show that the root mean squared error for the fixed-weight scores are somewhat larger than those for maximum likelihood scores. The fixed item weights require some adjustment to estimate extreme values of proficiency accurately. Three appendixes contain details of scoring procedures, quadratic adjustments to extreme item weights, and a set of computer programs for simulations. (Contains 10 figures, 3 tables, and 4 references.) (Author/SLD)