Exploring the associations between personality and response speed trajectories in low-stakes intelligence tests

Myszkowski, Nils; Storme, Martin; Kubiak, Emeric; Baron, Simon

doi:10.1016/j.paid.2022.111580

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The cognitive demand of progressive matrices may lead to variations in focus, with attention potentially waning or intensifying based on the perceived difficulty or engagement level of the items. For example, it has been suggested, via response time IRT models, that individuals, even in non-speeded tests, tend to vary in speed (and thus possibly effort), during a progressive test-some individuals remain relatively stable, while some increase in speed (they are more and more hasty) as others slow down (they are more and more cautious and usually perform better than the other groups) (Myszkowski et al 2022). Such fluctuations of motivation and effort can result in the success of an item depending on the success of a previous item, over and beyond the examinee's ability level.…”

Section: Sequential Local Dependenciesmentioning

confidence: 99%

“…The models may be extended in future studies to random lag effects, which would better account for (as well as allow us to explain) between-person variability in susceptibility to sequential effects. Regarding situational explanatory variables, we may also speculate that low stakes testing situations may lead to more sequential phenomena, such as giving up (Myszkowski et al 2022), which could also be a cause of sequential dependencies. Further, giving immediate feedback on item success or failure may also reinforce sequential dependencies.…”

Section: Limitations and Future Studiesmentioning

confidence: 99%

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Modeling Sequential Dependencies in Progressive Matrices: An Auto-Regressive Item Response Theory (AR-IRT) Approach

Myszkowski,

Storme

2024

J. Intell.

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Measurement models traditionally make the assumption that item responses are independent from one another, conditional upon the common factor. They typically explore for violations of this assumption using various methods, but rarely do they account for the possibility that an item predicts the next. Extending the development of auto-regressive models in the context of personality and judgment tests, we propose to extend binary item response models—using, as an example, the 2-parameter logistic (2PL) model—to include auto-regressive sequential dependencies. We motivate such models and illustrate them in the context of a publicly available progressive matrices dataset. We find an auto-regressive lag-1 2PL model to outperform a traditional 2PL model in fit as well as to provide more conservative discrimination parameters and standard errors. We conclude that sequential effects are likely overlooked in the context of cognitive ability testing in general and progressive matrices tests in particular. We discuss extensions, notably models with multiple lag effects and variable lag effects.

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Section: Sequential Local Dependenciesmentioning

confidence: 99%

Section: Limitations and Future Studiesmentioning

confidence: 99%

Modeling Sequential Dependencies in Progressive Matrices: An Auto-Regressive Item Response Theory (AR-IRT) Approach

Myszkowski,

Storme

2024

J. Intell.

Self Cite

View full text Add to dashboard Cite

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Item Response Theory for Creativity Measurement

Myszkowski

2024

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Item-response theory (IRT) represents a key advance in measurement theory. Yet, it is largely absent from curricula, textbooks and popular statistical software, and often introduced through a subset of models. This Element, intended for creativity and innovation researchers, researchers-in-training, and anyone interested in how individual creativity might be measured, aims to provide 1) an overview of classical test theory (CTT) and its shortcomings in creativity measurement situations (e.g., fluency scores, consensual assessment technique, etc.); 2) an introduction to IRT and its core concepts, using a broad view of IRT that notably sees CTT models as particular cases of IRT; 3) a practical strategic approach to IRT modeling; 4) example applications of this strategy from creativity research and the associated advantages; and 5) ideas for future work that could advance how IRT could better benefit creativity research, as well as connections with other popular frameworks.

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Exploring the associations between personality and response speed trajectories in low-stakes intelligence tests

Cited by 2 publications

References 17 publications

Modeling Sequential Dependencies in Progressive Matrices: An Auto-Regressive Item Response Theory (AR-IRT) Approach

Modeling Sequential Dependencies in Progressive Matrices: An Auto-Regressive Item Response Theory (AR-IRT) Approach

Item Response Theory for Creativity Measurement

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