Computer adaptive testing with empirical prior information: a Gibbs sampler approach for ability estimation

Matteucci, Mariagiulia ; Veldkamp, Bernard P. (2009) Computer adaptive testing with empirical prior information: a Gibbs sampler approach for ability estimation. [Preprint]
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Abstract

In this paper, empirical prior information is introduced in computer adaptive testing. Despite its increasing use, the method suffers from a weak measurement precision, especially under particular conditions. Therefore, it is shown how the inclusion of background variables both in the initialization and the ability estimation is able to improve the accuracy of ability estimates. In particular, a Gibbs sampler scheme is proposed in the phases of interim and final ability estimation. By using simulated data, it is demonstrated that the method produces more accurate ability estimates, especially for short tests and when reproducing boundary abilities.

Abstract
Tipologia del documento
Preprint
Autori
AutoreAffiliazioneORCID
Matteucci, Mariagiulia
Veldkamp, Bernard P.
Parole chiave
Adaptive testing, empirical prior information, Gibbs sampler, measurement precision.
Settori scientifico-disciplinari
DOI
Data di deposito
27 Ott 2009 15:36
Ultima modifica
16 Mag 2011 12:11
URI

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