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Whose data can we trust: How meta-predictions can be used to uncover credible respondents in survey data
Name
pone.0225432.pdf
Description
Published version
Size
811.99 KB
Format
Adobe PDF
Checksum (MD5)
34c532b17919fac3a12718ff572eb5b9
Author(s)
Radas, Sonja
Prelec, Drazen
Date Issued
2019
Journal
PLoS ONE
Publisher
Public Library of Science (PLoS)
Version
Final published version
Abstract
© 2019 Radas, Prelec. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Many areas of economics use subjective data, although it had been known to present problems regarding its reliability. To improve data quality, researchers may use scoring rules that reward respondents so that it is most profitable for them to tell the truth. However, if the subjects are not well informed about the topic or if they do not pay sufficient attention, they will produce data that could not be dependably used for decision-making even though subjects gave their honest answer. In this paper we show how meta-predictions (respondents’ predictions about choices of others) can be used for identification of respondents who produce dependable data. We use purchase intention survey, a popular method to elicit early adoption forecasts for a new concept, as a test bed for our approach. We present results from three online experiments, demonstrating that corrected purchase intentions are closer to the real outcomes.
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
10.1371/JOURNAL.PONE.0225432
