00515nas a2200133 4500008003900000245012900039210006900168300000900237490000600246100001800252700002700270700002400297856006000321 2018 d00aFactors Affecting the Classification Accuracy and Average Length of a Variable-Length Cognitive Diagnostic Computerized Test0 aFactors Affecting the Classification Accuracy and Average Length a1-140 v61 aHuebner, Alan1 aFinkelman, Matthew, D.1 aWeissman, Alexander uhttp://iacat.org/jcat/index.php/jcat/article/view/55/3001809nas a2200133 4500008003900000245010300039210006900142300001200211490000700223520134300230100002201573700002701595856005301622 2016 d00aOn Computing the Key Probability in the Stochastically Curtailed Sequential Probability Ratio Test0 aComputing the Key Probability in the Stochastically Curtailed Se a142-1560 v403 aThe Stochastically Curtailed Sequential Probability Ratio Test (SCSPRT) is a termination criterion for computerized classification tests (CCTs) that has been shown to be more efficient than the well-known Sequential Probability Ratio Test (SPRT). The performance of the SCSPRT depends on computing the probability that at a given stage in the test, an examinee’s current interim classification status will not change before the end of the test. Previous work discusses two methods of computing this probability, an exact method in which all potential responses to remaining items are considered and an approximation based on the central limit theorem (CLT) requiring less computation. Generally, the CLT method should be used early in the test when the number of remaining items is large, and the exact method is more appropriate at later stages of the test when few items remain. However, there is currently a dearth of information as to the performance of the SCSPRT when using the two methods. For the first time, the exact and CLT methods of computing the crucial probability are compared in a simulation study to explore whether there is any effect on the accuracy or efficiency of the CCT. The article is focused toward practitioners and researchers interested in using the SCSPRT as a termination criterion in an operational CCT.1 aHuebner, Alan, R.1 aFinkelman, Matthew, D. uhttp://apm.sagepub.com/content/40/2/142.abstract01723nas a2200145 4500008003900000245015500039210006900194300001000263490000700273520118100280100001701461700002701478700002001505856005201525 2016 d00aStochastic Curtailment of Questionnaires for Three-Level Classification: Shortening the CES-D for Assessing Low, Moderate, and High Risk of Depression0 aStochastic Curtailment of Questionnaires for ThreeLevel Classifi a22-360 v403 aIn 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–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.1 aSmits, Niels1 aFinkelman, Matthew, D.1 aKelderman, Henk uhttp://apm.sagepub.com/content/40/1/22.abstract01379nas 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.abstract01461nas a2200157 4500008003900000245006800039210006800107300001200175490000700187520097800194100001701172700002701189700001701216700001701233856005301250 2015 d00aUtilizing Response Times in Computerized Classification Testing0 aUtilizing Response Times in Computerized Classification Testing a389-4050 v393 aA well-known approach in computerized mastery testing is to combine the Sequential Probability Ratio Test (SPRT) stopping rule with item selection to maximize Fisher information at the mastery threshold. This article proposes a new approach in which a time limit is defined for the test and examinees’ response times are considered in both item selection and test termination. Item selection is performed by maximizing Fisher information per time unit, rather than Fisher information itself. The test is terminated once the SPRT makes a classification decision, the time limit is exceeded, or there is no remaining item that has a high enough probability of being answered before the time limit. In a simulation study, the new procedure showed a substantial reduction in average testing time while slightly improving classification accuracy compared with the original method. In addition, the new procedure reduced the percentage of examinees who exceeded the time limit.1 aSie, Haskell1 aFinkelman, Matthew, D.1 aRiley, Barth1 aSmits, Niels uhttp://apm.sagepub.com/content/39/5/389.abstract