  After a brief review of recent advances in sequential analysis involving
sequential generalized likelihood ratio tests, we discuss their use in
psychometric testing and extend the asymptotic optimality theory of these
sequential tests to the case of sequentially generated experiments, of
particular interest in computerized adaptive testing. We then show how these
methods can be used to design adaptive mastery tests, which are asymptotically
optimal and are also shown to provide substantial improvements over currently
used sequential and fixed length tests.
