01437nas a2200205   4500008004100000245008800041210007000129300001200199490000700211520067700218653002100895653003000916653002400946653002500970653002600995653005201021100001901073700002301092856011601115       2003                        eng d00aOptimal stratification of item pools in α-stratified computerized adaptive testing0 aOptimal stratification of item pools in αstratified computerized  a262-2740 v273 aA method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in α-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous item pool from the computerized adaptive testing (CAT) version of the Graduate Record Exams (GRE) Quantitative Test. The results indicate that the new method performs well in practical situations. It improves item exposure control, reduces the mean squared error in the θ estimates, and increases test reliability. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Content (Test)10aItem Response Theory10aMathematical Modeling10aTest Construction computerized adaptive testing1 aChang, Hua-Hua1 avan der Linden, WJ  uhttp://mail.iacat.org/content/optimal-stratification-item-pools-%CE%B1-stratified-computerized-adaptive-testing02086nas a2200229   4500008004100000245012900041210006900170300001200239490000700251520124000258653001801498653002101516653004501537653003001582653001801612653002501630653002601655100001601681700001401697700001901711856012601730       2002                        eng d00aA comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model0 acomparison of item selection techniques and exposure control mec  a147-1630 v263 aThe use of more performance items in large-scale testing has led to an increase in the research investigating the use of polytomously scored items in computer adaptive testing (CAT). Because this research has to be complemented with information pertaining to exposure control, the present research investigated the impact of using five different exposure control algorithms in two sized item pools calibrated using the generalized partial credit model. The results of the simulation study indicated that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap, increase pool utilization, and only minorly degrade measurement precision. Use of the more restrictive exposure control algorithms, such as the Sympson-Hetter and conditional Sympson-Hetter, controlled exposure to a greater extent but at the cost of measurement precision. Because convergence of the exposure control parameters was problematic for some of the more restrictive exposure control algorithms, use of the more simplistic exposure control mechanisms, particularly when the test length to item pool size ratio is large, is recommended. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10a(Statistical)10aAdaptive Testing10aAlgorithms computerized adaptive testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aMathematical Modeling1 aPastor, D A1 aDodd, B G1 aChang, Hua-Hua  uhttp://mail.iacat.org/content/comparison-item-selection-techniques-and-exposure-control-mechanisms-cats-using-generalized01390nas a2200193   4500008004100000245009400041210006900135300001200204490000700216520067900223653002100902653003000923653002500953653005700978100001401035700001901049700001901068856010901087       2000                        eng d00aA comparison of item selection rules at the early stages of computerized adaptive testing0 acomparison of item selection rules at the early stages of comput  a241-2550 v243 aThe effects of 5 item selection rules--Fisher information (FI), Fisher interval information (FII), Fisher information with a posterior distribution (FIP), Kullback-Leibler information (KL), and Kullback-Leibler information with a posterior distribution (KLP)--were compared with respect to the efficiency and precision of trait (θ) estimation at the early stages of computerized adaptive testing (CAT). FII, FIP, KL, and KLP performed marginally better than FI at the early stages of CAT for θ=-3 and -2. For tests longer than 10 items, there appeared to be no precision advantage for any of the selection rules. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis (Test)10aStatistical Estimation computerized adaptive testing1 aChen, S-Y1 aAnkenmann, R D1 aChang, Hua-Hua  uhttp://mail.iacat.org/content/comparison-item-selection-rules-early-stages-computerized-adaptive-testing