Online surveys: lessons learned in detecting and protecting against insincerity and bots
Author:
Funder
University of Utah Graduate Research Fellowship
University of Utah Consortium for Families & Health Research
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11135-024-01973-z.pdf
Reference40 articles.
1. Agans, J.P., Schade, S.A., Hanna, S.R., Chiang, S.-C., Shirzad, K., Bai, S.: The inaccuracy of data from online surveys: A cautionary analysis. Qual. Quant. (2024). https://doi.org/10.1007/s11135-023-01733-5
2. Ahler, D.J., Roush, C.E., Sood, G.: The micro-task market for lemons: Data quality on Amazon’s mechanical Turk. Political Sci. Res. Methods. (2021). https://doi.org/10.1017/psrm.2021.57
3. Beckford, J., Flanagan, S.R., Coriolan, A., Harris, O., Campbell, A., Skyers, N.: Barriers and facilitators to participation of men who have sex with men and transgender women in HIV research in Jamaica. Cult. Health. Sex. (2020). https://doi.org/10.1080/13691058.2019.1634222
4. Boas, T.C., Christenson, D.P., Glick, D.M.: Recruiting large online samples in the United States and India: Facebook, Mechanical Turk, and Qualtrics. Political Sci. Res. Methods. (2020). https://doi.org/10.1017/psrm.2018.28
5. Brainard, J., Killett, A., Houghton, J., Bunn, D., Watts, L., Mumford, S., O’Brien, S.J., Lane, K.: The wasps are clever: Keeping out and finding bot answers in internet surveys used for health research. Preprints (2022). https://doi.org/10.20944/preprints202203.0243.v2
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