Accelerating Executive Function Assessments With Group Sequential Designs. Rojo, M., Wong, Q. W., Pahor, A., Seitz, A., Jaeggi, S., Ramani, G., Goffney, I., Gardner, J. R., & Barbour, D. November, 2023. Publisher: OSF![Accelerating Executive Function Assessments With Group Sequential Designs [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Inferences about executive functions (EFs) are commonly drawn via lengthy serial administration of simple independent assessments. Classical methods for EF estimation often require excessive measurements and provide little or no flexibility to dynamically adjust test length for each individual. In order to decrease test duration and mitigate respondent burden, active testing modalities that incorporate more efficient data collection strategies are indispensable. To this end, we propose sequential analysis to improve upon traditional testing methods in behavioral science. In this paper, we show that sequential testing can be used to rapidly screen for a difference in the EF of a given individual with respect to a baseline level. In cognitive tests consisting of repeated identical tasks, a sequential framework can be utilized to actively detect significant differences in cognitive performance with high confidence more rapidly than conventional non-sequential approaches. Ultimately, sequential analysis could be applied to a variety of problems in cognitive and perceptual domains to improve efficiency gains and achieve substantial test length reduction.
@article{rojo_accelerating_2023,
title = {Accelerating {Executive} {Function} {Assessments} {With} {Group} {Sequential} {Designs}},
url = {https://osf.io/mbtgk},
doi = {10.31234/osf.io/mbtgk},
abstract = {Inferences about executive functions (EFs) are commonly drawn via lengthy serial administration of simple independent assessments. Classical methods for EF estimation often require excessive measurements and provide little or no flexibility to dynamically adjust test length for each individual. In order to decrease test duration and mitigate respondent burden, active testing modalities that incorporate more efficient data collection strategies are indispensable. To this end, we propose sequential analysis to improve upon traditional testing methods in behavioral science. In this paper, we show that sequential testing can be used to rapidly screen for a difference in the EF of a given individual with respect to a baseline level. In cognitive tests consisting of repeated identical tasks, a sequential framework can be utilized to actively detect significant differences in cognitive performance with high confidence more rapidly than conventional non-sequential approaches. Ultimately, sequential analysis could be applied to a variety of problems in cognitive and perceptual domains to improve efficiency gains and achieve substantial test length reduction.},
language = {en-us},
urldate = {2024-01-21},
author = {Rojo, Mariluz and Wong, Quinn Wai and Pahor, Anja and Seitz, Aaron and Jaeggi, Susanne and Ramani, Geetha and Goffney, Imani and Gardner, Jacob R. and Barbour, Dennis},
month = nov,
year = {2023},
note = {Publisher: OSF},
}
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