TY - JOUR
T1 - Stochastic Curtailment in Adaptive Mastery Testing: Improving the Efficiency of Confidence Intervalâ€“Based Stopping Rules
JF - Applied Psychological Measurement
Y1 - 2015
A1 - Sie, Haskell
A1 - Finkelman, Matthew D.
A1 - Bartroff, Jay
A1 - Thompson, Nathan A.
AB - A well-known stopping rule in adaptive mastery testing is to terminate the assessment once the examineeâ€™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.
VL - 39
UR - http://apm.sagepub.com/content/39/4/278.abstract
ER -