@article {2459,
	title = {Stochastic Curtailment of Questionnaires for Three-Level Classification: Shortening the CES-D for Assessing Low, Moderate, and High Risk of Depression},
	journal = {Applied Psychological Measurement},
	volume = {40},
	number = {1},
	year = {2016},
	pages = {22-36},
	abstract = {In clinical assessment, efficient screeners are needed to ensure low respondent burden. In this article, Stochastic Curtailment (SC), a method for efficient computerized testing for classification into two classes for observable outcomes, was extended to three classes. In a post hoc simulation study using the item scores on the Center for Epidemiologic Studies{\textendash}Depression Scale (CES-D) of a large sample, three versions of SC, SC via Empirical Proportions (SC-EP), SC via Simple Ordinal Regression (SC-SOR), and SC via Multiple Ordinal Regression (SC-MOR) were compared at both respondent burden and classification accuracy. All methods were applied under the regular item order of the CES-D and under an ordering that was optimal in terms of the predictive power of the items. Under the regular item ordering, the three methods were equally accurate, but SC-SOR and SC-MOR needed less items. Under the optimal ordering, additional gains in efficiency were found, but SC-MOR suffered from capitalization on chance substantially. It was concluded that SC-SOR is an efficient and accurate method for clinical screening. Strengths and weaknesses of the methods are discussed.},
	doi = {10.1177/0146621615592294},
	url = {http://apm.sagepub.com/content/40/1/22.abstract},
	author = {Smits, Niels and Finkelman, Matthew D. and Kelderman, Henk}
}
@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.}
}
