01445nas a2200157   4500008003900000245010600039210006900145300001200214490000700226520090200233100002301135700002501158700002601183700001501209856006301224       2019                          d00aEfficiency of Targeted Multistage Calibration Designs Under Practical Constraints: A Simulation Study0 aEfficiency of Targeted Multistage Calibration Designs Under Prac  a121-1460 v563 aAbstract Calibration of an item bank for computer adaptive testing requires substantial resources. In this study, we investigated whether the efficiency of calibration under the Rasch model could be enhanced by improving the match between item difficulty and student ability. We introduced targeted multistage calibration designs, a design type that considers ability-related background variables and performance for assigning students to suitable items. Furthermore, we investigated whether uncertainty about item difficulty could impair the assembling of efficient designs. The results indicated that targeted multistage calibration designs were more efficient than ordinary targeted designs under optimal conditions. Limited knowledge about item difficulty reduced the efficiency of one of the two investigated targeted multistage calibration designs, whereas targeted designs were more robust.1 aBerger, Stéphanie1 aVerschoor, Angela, J1 aEggen, Theo, J. H. M.1 aMoser, Urs  uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/jedm.1220300666nas a2200193   4500008003900000022001400039245008000053210006900133300001000202490000600212653004000218653002600258653003300284653002600317653002100343100002200364700002600386856006000412       2017                          d  a2165-659200aLatent-Class-Based Item Selection for  Computerized Adaptive Progress Tests0 aLatentClassBased Item Selection for Computerized Adaptive Progre  a22-430 v510acomputerized adaptive progress test10aitem selection method10aKullback-Leibler information10aLatent class analysis10alog-odds scoring1 avan Buuren, Nikky1 aEggen, Theo, J. H. M.  uhttp://iacat.org/jcat/index.php/jcat/article/view/62/2901549nas a2200145   4500008003900000245010400039210006900143300001200212490000700224520104200231100002601273700002601299700002501325856005301350       2016                          d00aMultidimensional Computerized Adaptive Testing for Classifying Examinees With Within-Dimensionality0 aMultidimensional Computerized Adaptive Testing for Classifying E  a387-4040 v403 aA classification method is presented for adaptive classification testing with a multidimensional item response theory (IRT) model in which items are intended to measure multiple traits, that is, within-dimensionality. The reference composite is used with the sequential probability ratio test (SPRT) to make decisions and decide whether testing can be stopped before reaching the maximum test length. Item-selection methods are provided that maximize the determinant of the information matrix at the cutoff point or at the projected ability estimate. A simulation study illustrates the efficiency and effectiveness of the classification method. Simulations were run with the new item-selection methods, random item selection, and maximization of the determinant of the information matrix at the ability estimate. The study also showed that the SPRT with multidimensional IRT has the same characteristics as the SPRT with unidimensional IRT and results in more accurate classifications than the latter when used for multidimensional data.1 avan Groen, Maaike, M.1 aEggen, Theo, J. H. M.1 aVeldkamp, Bernard, P  uhttp://apm.sagepub.com/content/40/6/387.abstract01545nas a2200145   4500008003900000245011500039210006900154300001200223490000700235520102700242100002601269700002601295700002501321856005301346       2014                          d00aItem Selection Methods Based on Multiple Objective Approaches for Classifying Respondents Into Multiple Levels0 aItem Selection Methods Based on Multiple Objective Approaches fo  a187-2000 v383 a<p>
	Computerized classification tests classify examinees into two or more levels while maximizing accuracy and minimizing test length. The majority of currently available item selection methods maximize information at one point on the ability scale, but in a test with multiple cutting points selection methods could take all these points simultaneously into account. If for each cutting point one objective is specified, the objectives can be combined into one optimization function using multiple objective approaches. Simulation studies were used to compare the efficiency and accuracy of eight selection methods in a test based on the sequential probability ratio test. Small differences were found in accuracy and efficiency between different methods depending on the item pool and settings of the classification method. The size of the indifference region had little influence on accuracy but considerable influence on efficiency. Content and exposure control had little influence on accuracy and efficiency.</p>
1 avan Groen, Maaike, M.1 aEggen, Theo, J. H. M.1 aVeldkamp, Bernard, P  uhttp://apm.sagepub.com/content/38/3/187.abstract