01432nas a2200205   4500008004100000245008800041210007000129300001200199490000700211520067700218653002100895653003000916653002400946653002500970653002600995653005201021100001901073700002301092856011101115       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://iacat.org/content/optimal-stratification-item-pools-%CE%B1-stratified-computerized-adaptive-testing01516nas a2200157   4500008004100000245009500041210006900136300001200205490000700217520090100224653002101125653003001146653004501176100002301221856011401244       2003                        eng d00aSome alternatives to Sympson-Hetter item-exposure control in computerized adaptive testing0 aSome alternatives to SympsonHetter itemexposure control in compu  a249-2650 v283 aTheHetter and Sympson (1997; 1985) method is a method of probabilistic item-exposure control in computerized adaptive testing. Setting its control parameters to admissible values requires an iterative process of computer simulations that has been found to be time consuming, particularly if the parameters have to be set conditional on a realistic set of values for the examinees’ ability parameter. Formal properties of the method are identified that help us explain why this iterative process can be slow and does not guarantee admissibility. In addition, some alternatives to the SH method are introduced. The behavior of these alternatives was estimated for an adaptive test from an item pool from the Law School Admission Test (LSAT). Two of the alternatives showed attractive behavior and converged smoothly to admissibility for all items in a relatively small number of iteration steps. 10aAdaptive Testing10aComputer Assisted Testing10aTest Items computerized adaptive testing1 avan der Linden, WJ  uhttp://iacat.org/content/some-alternatives-sympson-hetter-item-exposure-control-computerized-adaptive-testing01512nas a2200229   4500008004100000245008700041210006900128300001200197490000700209520075300216653002100969653001300990653003001003653003401033653001101067653001501078653001501093653001801108100002301126700002701149856010601176       2003                        eng d00aUsing response times to detect aberrant responses in computerized adaptive testing0 aUsing response times to detect aberrant responses in computerize  a251-2650 v683 aA lognormal model for response times is used to check response times for aberrances in examinee behavior on computerized adaptive tests. Both classical procedures and Bayesian posterior predictive checks are presented. For a fixed examinee, responses and response times are independent; checks based on response times offer thus information independent of the results of checks on response patterns. Empirical examples of the use of classical and Bayesian checks for detecting two different types of aberrances in response times are presented. The detection rates for the Bayesian checks outperformed those for the classical checks, but at the cost of higher false-alarm rates. A guideline for the choice between the two types of checks is offered.10aAdaptive Testing10aBehavior10aComputer Assisted Testing10acomputerized adaptive testing10aModels10aperson Fit10aPrediction10aReaction Time1 avan der Linden, WJ1 aKrimpen-Stoop, E M L A  uhttp://iacat.org/content/using-response-times-detect-aberrant-responses-computerized-adaptive-testing