Model-agnostic unsupervised detection of bots in a Likert-type questionnaire-Reference-Cited by-同舟云学术

Model-agnostic unsupervised detection of bots in a Likert-type questionnaire

Published:2023-11-20 Issue: Volume: Page:
ISSN:1554-3528
Container-title:Behavior Research Methods
language:en
Short-container-title:Behav Res

Author:

Ilagan Michael John,Falk Carl F.

Funder

Natural Sciences and Engineering Research Council of Canada

Fonds de recherche du Québec - Nature et technologies

Publisher

Springer Science and Business Media LLC

Subject

General Psychology,Psychology (miscellaneous),Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Experimental and Cognitive Psychology

Link

https://link.springer.com/content/pdf/10.3758/s13428-023-02246-7.pdf

Reference40 articles.

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2. Bengtsson, H. (2021). A unifying framework for parallel and distributed processing in R using futures. Retrieved from https://journal.r-project.org/archive/2021/RJ-2021-048/index.html (R package version 1.21.0)

3. Bock, R. D., & Gibbons, R. D. (2021). Item response theory. Hoboken, NJ: Wiley.

4. Buchanan, E. M., & Scofield, J. E. (2018). Methods to detect low quality data and its implication for psychological research. Behavior Research Methods, 50, 2586–2596. https://doi.org/10.3758/s13428-018-1035-6

5. Chmielewski, M., & Kucker, S. C. (2020). An MTurk crisis? Shifts in data quality and the impact on study results. Social Psychological and Personality Science, 11(4), 464–473. https://doi.org/10.1177/1948550619875149

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