02412nas a2200289   4500008004100000020002200041245010800063210006900171260004500240300001000285490000700295520141000302653003201712653002501744653002501769653002501794653002601819653001801845653003701863653002101900653001301921100001501934700001401949700001901963700002001982856012002002       2007                        eng d  a1015-5759 (Print)00aPsychometric properties of an emotional adjustment measure: An application of the graded response model0 aPsychometric properties of an emotional adjustment measure An ap  bHogrefe & Huber Publishers GmbH: Germany  a39-460 v233 aItem response theory (IRT) provides valuable methods for the analysis of the psychometric properties of a psychological measure. However, IRT has been mainly used for assessing achievements and ability rather than personality factors. This paper presents an application of the IRT to a personality measure. Thus, the psychometric properties of a new emotional adjustment measure that consists of a 28-six graded response items is shown. Classical test theory (CTT) analyses as well as IRT analyses are carried out. Samejima's (1969) graded-response model has been used for estimating item parameters. Results show that the bank of items fulfills model assumptions and fits the data reasonably well, demonstrating the suitability of the IRT models for the description and use of data originating from personality measures. In this sense, the model fulfills the expectations that IRT has undoubted advantages: (1) The invariance of the estimated parameters, (2) the treatment given to the standard error of measurement, and (3) the possibilities offered for the construction of computerized adaptive tests (CAT). The bank of items shows good reliability. It also shows convergent validity compared to the Eysenck Personality Inventory (EPQ-A; Eysenck & Eysenck, 1975) and the Big Five Questionnaire (BFQ; Caprara, Barbaranelli, & Borgogni, 1993). (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive tests10aEmotional Adjustment10aItem Response Theory10aPersonality Measures10apersonnel recruitment10aPsychometrics10aSamejima's graded response model10atest reliability10avalidity1 aRubio, V J1 aAguado, D1 aHontangas, P M1 aHernández, J M  uhttp://www.iacat.org/content/psychometric-properties-emotional-adjustment-measure-application-graded-response-model01907nas a2200217   4500008004100000020002200041245007000063210006900133260004100202300001200243490000700255520117100262653003201433653003301465653001801498653002401516100001301540700001801553700002301571856009501594       2004                        eng d  a0022-0655 (Print)00aConstructing rotating item pools for constrained adaptive testing0 aConstructing rotating item pools for constrained adaptive testin  bBlackwell Publishing: United Kingdom  a345-3590 v413 aPreventing items in adaptive testing from being over- or underexposed is one of the main problems in computerized adaptive testing. Though the problem of overexposed items can be solved using a probabilistic item-exposure control method, such methods are unable to deal with the problem of underexposed items. Using a system of rotating item pools, on the other hand, is a method that potentially solves both problems. In this method, a master pool is divided into (possibly overlapping) smaller item pools, which are required to have similar distributions of content and statistical attributes. These pools are rotated among the testing sites to realize desirable exposure rates for the items. A test assembly model, motivated by Gulliksen's matched random subtests method, was explored to help solve the problem of dividing a master pool into a set of smaller pools. Different methods to solve the model are proposed. An item pool from the Law School Admission Test was used to evaluate the performances of computerized adaptive tests from systems of rotating item pools constructed using these methods. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive tests10aconstrained adaptive testing10aitem exposure10arotating item pools1 aAriel, A1 aVeldkamp, B P1 avan der Linden, WJ  uhttp://www.iacat.org/content/constructing-rotating-item-pools-constrained-adaptive-testing02036nas a2200181   4500008004100000020002200041245006400063210006400127260004300191300001200234490000700246520141900253653003201672653003401704100001801738700001701756856008101773       2004                        eng d  a1076-9986 (Print)00aEvaluation of the CATSIB DIF procedure in a pretest setting0 aEvaluation of the CATSIB DIF procedure in a pretest setting  bAmerican Educational Research Assn: US  a177-1990 v293 aA new procedure, CATSIB, for assessing differential item functioning (DIF) on computerized adaptive tests (CATs) is proposed. CATSIB, a modified SIBTEST procedure, matches test takers on estimated ability and controls for impact-induced Type I error inflation by employing a CAT version of the SIBTEST "regression correction." The performance of CATSIB in terms of detection of DIF in pretest items was evaluated in a simulation study. Simulated test takers were adoptively administered 25 operational items from a pool of 1,000 and were linearly administered 16 pretest items that were evaluated for DIF. Sample size varied from 250 to 500 in each group. Simulated impact levels ranged from a 0- to 1-standard-deviation difference in mean ability levels. The results showed that CATSIB with the regression correction displayed good control over Type 1 error, whereas CATSIB without the regression correction displayed impact-induced Type 1 error inflation. With 500 test takers in each group, power rates were exceptionally high (84% to 99%) for values of DIF at the boundary between moderate and large DIF. For smaller samples of 250 test takers in each group, the corresponding power rates ranged from 47% to 95%. In addition, in all cases, CATSIB was very accurate in estimating the true values of DIF, displaying at most only minor estimation bias. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive tests10adifferential item functioning1 aNandakumar, R1 aRoussos, L A  uhttp://www.iacat.org/content/evaluation-catsib-dif-procedure-pretest-setting