%0 Conference Paper
%B Annual Conference of the International Association for Computerized Adaptive Testing
%D 2011
%T Detecting DIF between Conventional and Computerized Adaptive Testing: A Monte Carlo Study
%A Barth B. Riley
%A Adam C. Carle
%K 95% Credible Interval
%K CAT
%K DIF
%K differential item function
%K modified robust Z statistic
%K Monte Carlo methodologies
%X 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.

- Exception: low difficulty items (< -2.5 logits).
- Not significantly affected by % of DIF items.
- Was affected by mean trait level difference.
- 95% Credibility Interval evidenced slightly higher power to detect DIF, but also higher false positive rate.

%B Annual Conference of the International Association for Computerized Adaptive Testing
%8 10/2011
%G eng
%0 Journal Article
%J Applied Psychological Measurement
%D 2006
%T SIMCAT 1.0: A SAS computer program for simulating computer adaptive testing
%A Raîche, G.
%A Blais, J-G.
%K computer adaptive testing
%K computer program
%K estimated proficiency level
%K Monte Carlo methodologies
%K Rasch logistic model
%X Monte 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)
%B Applied Psychological Measurement
%I Sage Publications: US
%V 30
%P 60-61
%@ 0146-6216 (Print)
%G eng
%M 2005-16359-005