00429nas a2200121   4500008004100000022001400041245006600055210006600121490000600187100001000193700001800203856008600221       2004                        eng d  a1575-910500aStatistics for detecting disclosed items in a CAT environment0 aStatistics for detecting disclosed items in a CAT environment0 v51 aLu, Y1 aHambleton, RK  uhttp://www.iacat.org/content/statistics-detecting-disclosed-items-cat-environment01711nas a2200169   4500008004100000245015800041210006900199300001000268490000700278520105400285100001901339700001801358700001601376700001201392700001601404856012101420       2003                        eng d00aSmall sample estimation in dichotomous item response models: Effect of priors based on judgmental information on the accuracy of item parameter estimates0 aSmall sample estimation in dichotomous item response models Effe  a27-510 v273 aLarge item banks with properly calibrated test items are essential for ensuring the validity of computer-based tests. At the same time, item calibrations with small samples are desirable to minimize the amount of pretesting and limit item exposure. Bayesian estimation procedures show considerable promise with small examinee samples. The purposes of the study were (a) to examine how prior information for Bayesian item parameter estimation can be specified and (b) to investigate the relationship between sample size and the specification of prior information on the accuracy of item parameter estimates. The results of the simulation study were clear: Estimation of item response theory (IRT) model item parameters can be improved considerably. Improvements in the one-parameter model were modest; considerable improvements with the two- and three-parameter models were observed. Both the study of different forms of priors and ways to improve the judgmental data used in forming the priors appear to be promising directions for future research. 1 aSwaminathan, H1 aHambleton, RK1 aSireci, S G1 aXing, D1 aRizavi, S M  uhttp://www.iacat.org/content/small-sample-estimation-dichotomous-item-response-models-effect-priors-based-judgmental