P
Paul De Boeck
Researcher at Ohio State University
Publications - 217
Citations - 11170
Paul De Boeck is an academic researcher from Ohio State University. The author has contributed to research in topics: Item response theory & Differential item functioning. The author has an hindex of 45, co-authored 209 publications receiving 9517 citations. Previous affiliations of Paul De Boeck include Catholic University of Leuven & University of Amsterdam.
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Attempting to differentiate fast and slow intelligence: Using generalized item response trees to examine the role of speed on intelligence tests
TL;DR: This paper used a two-parameter item response tree model, which allows the researcher to calculate separate sets of item parameters for when an item is answered quickly versus when it is answered slowly, suggesting that fast responses to an item may contain more information about the ability of the respondent than slow responses.
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Structural analysis of the intension and extension of semantic concepts
TL;DR: The proposed method reveals the internal structure of the extension as well as the intension of a concept, together with a correspondence relation that shows the mutual dependence of both structures.
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Predicting conjunction typicalities by component typicalities.
TL;DR: Analysis of a large aggregated data set showed that a calibrated minimum rule model and some extensions of this model accounted for a very large part of the variance in the conjunction typicalities and can also account for the so-called guppy effect.
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How Much Power and Speed Is Measured in This Test
TL;DR: Speed and power are investigated through the effect of posterior time limits on two main aspects: whether it is more power-related or more speed-related; how well the latent variable (of whatever kind) is measured through the item(s).
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Componential IRT Models for Polytomous Items
Machteld Hoskens,Paul De Boeck +1 more
TL;DR: In this article, the authors use transformation matrices to constrain the parameters of response categories so as to reflect the componential design of the response categories, which can be used to study both main effects and interaction effects of components.