Monte Carlo simulation was used to investigate score bias and information characteristics of Owen's Bayesian adaptive testing strategy, and to examine possible causes of score bias. Factors investigated in three related studies included effects of item discrimination, effects of fixed vs. variable test length, and effects of an accurate prior theta estimate. Data were generated from a three-parameter logistic model for 3,100 simulees in each of eight data sets; Bayesian adaptive tests were administered, drawing items from a "perfect" item pool. The results indicate that theta estimates from Owen's Bayesian adaptive testing method are affected by the prior theta estimate used and that the method does not provide measurements that are unbiased and equiprecise except under the unrealistic condition of an accurate prior theta estimate. (Author/PN)