01510nas a2200145   4500008003900000245008500039210006900124300001200193490000700205520103200212100001901244700002301263700002501286856005301311       2015                          d00aNew Item Selection Methods for Cognitive Diagnosis Computerized Adaptive Testing0 aNew Item Selection Methods for Cognitive Diagnosis Computerized   a167-1880 v393 aThis article introduces two new item selection methods, the modified posterior-weighted Kullback–Leibler index (MPWKL) and the generalized deterministic inputs, noisy “and” gate (G-DINA) model discrimination index (GDI), that can be used in cognitive diagnosis computerized adaptive testing. The efficiency of the new methods is compared with the posterior-weighted Kullback–Leibler (PWKL) item selection index using a simulation study in the context of the G-DINA model. The impact of item quality, generating models, and test termination rules on attribute classification accuracy or test length is also investigated. The results of the study show that the MPWKL and GDI perform very similarly, and have higher correct attribute classification rates or shorter mean test lengths compared with the PWKL. In addition, the GDI has the shortest implementation time among the three indices. The proportion of item usage with respect to the required attributes across the different conditions is also tracked and discussed.1 aKaplan, Mehmet1 ade la Torre, Jimmy1 aBarrada, Juan Ramón  uhttp://apm.sagepub.com/content/39/3/167.abstract01871nas a2200157   4500008003900000245008900039210006900128300001200197490000700209520135600216100002501572700001601597700002101613700002601634856005301660       2010                          d00aA Method for the Comparison of Item Selection Rules in Computerized Adaptive Testing0 aMethod for the Comparison of Item Selection Rules in Computerize  a438-4520 v343 a<p>In a typical study comparing the relative efficiency of two item selection rules in computerized adaptive testing, the common result is that they simultaneously differ in accuracy and security, making it difficult to reach a conclusion on which is the more appropriate rule. This study proposes a strategy to conduct a global comparison of two or more selection rules. A plot showing the performance of each selection rule for several maximum exposure rates is obtained and the whole plot is compared with other rule plots. The strategy was applied in a simulation study with fixed-length CATs for the comparison of six item selection rules: the point Fisher information, Fisher information weighted by likelihood, Kullback-Leibler weighted by likelihood, maximum information stratification with blocking, progressive and proportional methods. Our results show that there is no optimal rule for any overlap value or root mean square error (RMSE). The fact that a rule, for a given level of overlap, has lower RMSE than another does not imply that this pattern holds for another overlap rate. A fair comparison of the rules requires extensive manipulation of the maximum exposure rates. The best methods were the Kullback-Leibler weighted by likelihood, the proportional method, and the maximum information stratification method with blocking.</p>
1 aBarrada, Juan Ramón1 aOlea, Julio1 aPonsoda, Vicente1 aAbad, Francisco José  uhttp://apm.sagepub.com/content/34/6/438.abstract01773nas a2200145   4500008003900000245006900039210006900108300001000177490000700187520131500194100002501509700002501534700001601559856005201575       2009                          d00aMultiple Maximum Exposure Rates in Computerized Adaptive Testing0 aMultiple Maximum Exposure Rates in Computerized Adaptive Testing  a58-730 v333 a<p>Computerized adaptive testing is subject to security problems, as the item bank content remains operative over long periods and administration time is flexible for examinees. Spreading the content of a part of the item bank could lead to an overestimation of the examinees&#39; trait level. The most common way of reducing this risk is to impose a maximum exposure rate (rmax) that no item should exceed. Several methods have been proposed with this aim. All of these methods establish a single value of rmax throughout the test. This study presents a new method, the multiple-rmax method, that defines as many values of rmax as the number of items presented in the test. In this way, it is possible to impose a high degree of randomness in item selection at the beginning of the test, leaving the administration of items with the best psychometric properties to the moment when the trait level estimation is most accurate. The implementation of the multiple-r max method is described and is tested in simulated item banks and in an operative bank. Compared with a single maximum exposure method, the new method has a more balanced usage of the item bank and delays the possible distortion of trait estimation due to security problems, with either no or only slight decrements of measurement accuracy.</p>
1 aBarrada, Juan Ramón1 aVeldkamp, Bernard, P1 aOlea, Julio  uhttp://apm.sagepub.com/content/33/1/58.abstract