@article {2394,
	title = {Stochastic Curtailment in Adaptive Mastery Testing: Improving the Efficiency of Confidence Interval{\textendash}Based Stopping Rules},
	journal = {Applied Psychological Measurement},
	volume = {39},
	number = {4},
	year = {2015},
	pages = {278-292},
	abstract = {A well-known stopping rule in adaptive mastery testing is to terminate the assessment once the examinee{\textquoteright}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.},
	doi = {10.1177/0146621614561314},
	url = {http://apm.sagepub.com/content/39/4/278.abstract},
	author = {Sie, Haskell and Finkelman, Matthew D. and Bartroff, Jay and Thompson, Nathan A.}
}
@article {2184,
	title = {Item Selection in Computerized Classification Testing},
	journal = {Educational and Psychological Measurement},
	volume = {69},
	number = {5},
	year = {2009},
	pages = {778-793},
	abstract = {<p>Several alternatives for item selection algorithms based on item response theory in computerized classification testing (CCT) have been suggested, with no conclusive evidence on the substantial superiority of a single method. It is argued that the lack of sizable effect is because some of the methods actually assess items very similarly through different calculations and will usually select the same item. Consideration of methods that assess information across a wider range is often unnecessary under realistic conditions, although it might be advantageous to utilize them only early in a test. In addition, the efficiency of item selection approaches depend on the termination criteria that are used, which is demonstrated through didactic example and Monte Carlo simulation. Item selection at the cut score, which seems conceptually appropriate for CCT, is not always the most efficient option. A broad framework for item selection in CCT is presented that incorporates these points.</p>
},
	doi = {10.1177/0013164408324460},
	url = {http://epm.sagepub.com/content/69/5/778.abstract},
	author = {Thompson, Nathan A.}
}
