00949nas a2200169   4500008003900000022001400039245011500053210006900168260000800237300001600245490000700261520040300268100001700671700002400688700002100712856004600733       2018                          d  a1573-264900aSome recommendations for developing multidimensional computerized adaptive tests for patient-reported outcomes0 aSome recommendations for developing multidimensional computerize  cApr  a1055–10630 v273 aMultidimensional item response theory and computerized adaptive testing (CAT) are increasingly used in mental health, quality of life (QoL), and patient-reported outcome measurement. Although multidimensional assessment techniques hold promises, they are more challenging in their application than unidimensional ones. The authors comment on minimal standards when developing multidimensional CATs.1 aSmits, Niels1 aPaap, Muirne, C. S.1 aBöhnke, Jan, R.  uhttps://doi.org/10.1007/s11136-018-1821-800925nas a2200133   4500008003900000022001400039245012600053210006900179300001600248490000700264520045700271100001700728856004600745       2016                          d  a1573-264900aOn the effect of adding clinical samples to validation studies of patient-reported outcome item banks: a simulation study0 aeffect of adding clinical samples to validation studies of patie  a1635–16440 v253 aTo increase the precision of estimated item parameters of item response theory models for patient-reported outcomes, general population samples are often enriched with samples of clinical respondents. Calibration studies provide little information on how this sampling scheme is incorporated into model estimation. In a small simulation study the impact of ignoring the oversampling of clinical respondents on item and person parameters is illustrated.1 aSmits, Niels  uhttps://doi.org/10.1007/s11136-015-1199-901723nas 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.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