@article {2070,
	title = {catR: An R Package for Computerized Adaptive Testing},
	journal = {Applied Psychological Measurement},
	year = {2011},
	abstract = {<p>Computerized adaptive testing (CAT) is an active current research field in psychometrics and educational measurement. However, there is very little software available to handle such adaptive tasks. The R package catR was developed to perform adaptive testing with as much flexibility as possible, in an attempt to provide a developmental and testing platform to the interested user. Several item-selection rules and ability estimators are implemented. The item bank can be provided by the user or randomly generated from parent distributions of item parameters. Three stopping rules are available. The output can be graphically displayed.</p>
},
	keywords = {computer program, computerized adaptive testing, Estimation, Item Response Theory},
	doi = {10.1177/0146621611407482},
	author = {Magis, D. and Ra{\^\i}che, G.}
}
@article {319,
	title = {SIMCAT 1.0: A SAS computer program for simulating computer adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {30},
	number = {1},
	year = {2006},
	pages = {60-61},
	publisher = {Sage Publications: US},
	abstract = {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 \& Ra{\^\i}che, 2005; Ra{\^\i}che \& 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)},
	keywords = {computer adaptive testing, computer program, estimated proficiency level, Monte Carlo methodologies, Rasch logistic model},
	isbn = {0146-6216 (Print)},
	author = {Ra{\^\i}che, G. and Blais, J-G.}
}
