A new item response theory model to adjust data allowing examinee choice.
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2018
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Abstract
In a typical questionnaire testing situation, examinees are not allowed to choose which
items they answer because of a technical issue in obtaining satisfactory statistical estimates
of examinee ability and item difficulty. This paper introduces a new item response theory
(IRT) model that incorporates information from a novel representation of questionnaire data
using network analysis. Three scenarios in which examinees select a subset of items were
simulated. In the first scenario, the assumptions required to apply the standard Rasch
model are met, thus establishing a reference for parameter accuracy. The second and third
scenarios include five increasing levels of violating those assumptions. The results show
substantial improvements over the standard model in item parameter recovery. Furthermore,
the accuracy was closer to the reference in almost every evaluated scenario. To the
best of our knowledge, this is the first proposal to obtain satisfactory IRT statistical estimates
in the last two scenarios.
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PENA, C. S.; COSTA, M. A.; OLIVEIRA, R. P. B. A new item response theory model to adjust data allowing examinee choice. PLoS One, v. 13, n. 2, p. 1-23, fev. 2018. Disponível em: <http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191600>. Acesso em: 16 jun. 2018.