Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study
- Creators
- Gillen, Ben
- Snowberg, Erik
- Yariv, Leeat
Abstract
Measurement error is ubiquitous in experimental work. It leads to imperfect statistical controls, attenuated estimated effects of elicited behaviors, and biased correlations between characteristics. We develop statistical techniques for handling experimental measurement error. These techniques are applied to data from the Caltech Cohort Study, which conducts repeated incentivized surveys of the Caltech student body. We replicate three classic experiments, demonstrating that results change substantially when measurement error is accounted for. Collectively, these results show that failing to properly account for measurement error may cause a field-wide bias leading scholars to identify "new" phenomena.
Additional Information
© 2019 by The University of Chicago. Electronically published June 13, 2019. Snowberg gratefully acknowledges the support of NSF grants SES-1156154 and SMA-1329195. Yariv gratefully acknowledges the support of NSF grants SES-0963583 and SES-1629613 and Gordon and Betty Moore Foundation grant 1158. We thank Jonathan Bendor, Christopher Blattman, Colin Camerer, Marco Castillo, Gary Charness, Lucas Coffman, Guillaume Frechette, Dan Friedman, Drew Fudenberg, Yoram Halevy, Ori Heffetz, Muriel Niederle, Alex Rees-Jones, Shyam Sunder, Roel van Veldhuizen, and Lise Vesterlund, as well as two anonymous reviewers and the editor, Emir Kamenica, for comments and suggestions. We also appreciate the input of seminar audiences at Caltech, Hong Kong University of Science and Technology, the Ifo Institute, Nanyang Technological University, the National University of Singapore, Stanford Institute for Theoretical Economics, the University of Bonn, the University of British Columbia, the University of Southern California, and the University of Zurich. Data are provided as supplementary material online.Attached Files
Published - 701681.pdf
Supplemental Material - 2016930Appendix.pdf
Supplemental Material - 2016930data.zip
Files
Additional details
- Eprint ID
- 98372
- Resolver ID
- CaltechAUTHORS:20190830-111734296
- NSF
- SES-1156154
- NSF
- SMA-1329195
- NSF
- SES-0963583
- NSF
- SES-1629613
- Gordon and Betty Moore Foundation
- 1158
- Created
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2019-08-30Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field