Placebo Selection in Survey Experiments: An Agnostic Approach
Ethan Porter and
Yamil R. Velez
Political Analysis, 2022, vol. 30, issue 4, 481-494
Abstract:
Although placebo conditions are ubiquitous in survey experiments, little evidence guides common practices for their use and selection. How should scholars choose and construct placebos? First, we review the role of placebos in published survey experiments, finding that placebos are used inconsistently. Then, drawing on the medical literature, we clarify the role that placebos play in accounting for nonspecific effects (NSEs), or the effects of ancillary features of experiments. We argue that, in the absence of precise knowledge of NSEs that placebos are adjusting for, researchers should average over a corpus of many placebos. We demonstrate this agnostic approach to placebo construction through the use of GPT-2, a generative language model trained on a database of over 1 million internet news pages. Using GPT-2, we devise 5,000 distinct placebos and administer two experiments (N = 2,975). Our results illustrate how researchers can minimize their role in placebo selection through automated processes. We conclude by offering tools for incorporating computer-generated placebo text vignettes into survey experiments and developing recommendations for best practice.
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:30:y:2022:i:4:p:481-494_2
Access Statistics for this article
More articles in Political Analysis from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().