01227nas a2200193 4500008004100000245009400041210006900135260001200204520052700216653002600743653000800769653000800777653003100785653003200816653003000848100002000878700001900898856011600917 2011 eng d00aDetecting DIF between Conventional and Computerized Adaptive Testing: A Monte Carlo Study0 aDetecting DIF between Conventional and Computerized Adaptive Tes c10/20113 a

A comparison od two procedures, Modified Robust Z and 95% Credible Interval, were compared in a Monte Carlo study. Both procedures evidenced adequate control of false positive DIF results.

10a95% Credible Interval10aCAT10aDIF10adifferential item function10amodified robust Z statistic10aMonte Carlo methodologies1 aRiley, Barth, B1 aCarle, Adam, C uhttp://iacat.org/content/detecting-dif-between-conventional-and-computerized-adaptive-testing-monte-carlo-study02342nas a2200217 4500008004100000020002200041245008000063210006900143260002600212300001000238490000700248520160400255653003001859653002101889653003201910653003001942653002501972100001501997700001502012856009702027 2006 eng d a0146-6216 (Print)00aSIMCAT 1.0: A SAS computer program for simulating computer adaptive testing0 aSIMCAT 10 A SAS computer program for simulating computer adaptiv bSage Publications: US a60-610 v303 aMonte Carlo methodologies are frequently applied to study the sampling distribution of the estimated proficiency level in adaptive testing. These methods eliminate real situational constraints. However, these Monte Carlo methodologies are not currently supported by the available software programs, and when these programs are available, their flexibility is limited. SIMCAT 1.0 is aimed at the simulation of adaptive testing sessions under different adaptive expected a posteriori (EAP) proficiency-level estimation methods (Blais & Raîche, 2005; Raîche & Blais, 2005) based on the one-parameter Rasch logistic model. These methods are all adaptive in the a priori proficiency-level estimation, the proficiency-level estimation bias correction, the integration interval, or a combination of these factors. The use of these adaptive EAP estimation methods diminishes considerably the shrinking, and therefore biasing, effect of the estimated a priori proficiency level encountered when this a priori is fixed at a constant value independently of the computed previous value of the proficiency level. SIMCAT 1.0 also computes empirical and estimated skewness and kurtosis coefficients, such as the standard error, of the estimated proficiency-level sampling distribution. In this way, the program allows one to compare empirical and estimated properties of the estimated proficiency-level sampling distribution under different variations of the EAP estimation method: standard error and bias, like the skewness and kurtosis coefficients. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputer adaptive testing10acomputer program10aestimated proficiency level10aMonte Carlo methodologies10aRasch logistic model1 aRaîche, G1 aBlais, J-G uhttp://iacat.org/content/simcat-10-sas-computer-program-simulating-computer-adaptive-testing