01379nas a2200157   4500008003900000245012700039210006900166300001200235490000700247520082800254100001701082700002701099700001801126700002401144856005301168       2015                          d00aStochastic Curtailment in Adaptive Mastery Testing: Improving the Efficiency of Confidence Interval–Based Stopping Rules0 aStochastic Curtailment in Adaptive Mastery Testing Improving the  a278-2920 v393 aA well-known stopping rule in adaptive mastery testing is to terminate the assessment once the examinee’s ability confidence interval lies entirely above or below the cut-off score. This article proposes new procedures that seek to improve such a variable-length stopping rule by coupling it with curtailment and stochastic curtailment. Under the new procedures, test termination can occur earlier if the probability is high enough that the current classification decision remains the same should the test continue. Computation of this probability utilizes normality of an asymptotically equivalent version of the maximum likelihood ability estimate. In two simulation sets, the new procedures showed a substantial reduction in average test length while maintaining similar classification accuracy to the original method.1 aSie, Haskell1 aFinkelman, Matthew, D.1 aBartroff, Jay1 aThompson, Nathan, A  uhttp://apm.sagepub.com/content/39/4/278.abstract