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Authors: Maria Dias 1 ; Catia Cepeda 2 ; Dina Rindlisbacher 3 ; Edouard Battegay 3 ; Marcus Cheetham 3 and Hugo Gamboa 1

Affiliations: 1 LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal ; 2 LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal, Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland ; 3 Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland, Center of Competence Multimorbidity, University of Zurich, Zurich, Switzerland

ISBN: 978-989-758-353-7

Keyword(s): Uncertainty, Questionnaire, Human-Computer Interaction, Mouse-tracking, Signal Processing, Machine Learning.

Abstract: Online questionnaire-based research is growing at a fast pace. Mouse-tracking methods provide a potentially important data source for this research by enabling the capture of respondents’ online behaviour while answering questionnaire items. This behaviour can give insight into respondents’ perceptual, cognitive and affective processes. The present work focused on the potential use of mouse movements to indicate uncertainty when answering questionnaire items and used machine learning methods as a basis to model these. N=79 participants completed an online questionnaire while mouse data was tracked. Mouse movement features were extracted and selected for model training and testing. Using logistic regression and k-fold cross-validation, the model achieved an estimated performance accuracy of 89%. The findings show that uncertainty is indicated by an increase in the number of horizontal direction inversions and the distance covered by the mouse and by longer interaction times with and a higher number of revisits to questionnaire items that evoked uncertainty. Future work should validate these methods further. (More)

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Paper citation in several formats:
Dias, M.; Cepeda, C.; Rindlisbacher, D.; Battegay, E.; Cheetham, M. and Gamboa, H. (2019). Predicting Response Uncertainty in Online Surveys: A Proof of Concept.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-353-7, pages 155-162. DOI: 10.5220/0007381801550162

@conference{biosignals19,
author={Maria Camila Dias. and Catia Cepeda. and Dina Rindlisbacher. and Edouard Battegay. and Cheetham, M. and Hugo Gamboa.},
title={Predicting Response Uncertainty in Online Surveys: A Proof of Concept},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,},
year={2019},
pages={155-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007381801550162},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,
TI - Predicting Response Uncertainty in Online Surveys: A Proof of Concept
SN - 978-989-758-353-7
AU - Dias, M.
AU - Cepeda, C.
AU - Rindlisbacher, D.
AU - Battegay, E.
AU - Cheetham, M.
AU - Gamboa, H.
PY - 2019
SP - 155
EP - 162
DO - 10.5220/0007381801550162

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