Information equivalence in survey experiments
Open accessAbstract
Survey experiments often manipulate the description of attributes in a hypothetical scenario, with the goal of learning about those attributes’ real-world effects. Such inferences rely on an underappreciated assumption: experimental conditions must be information equivalent (IE) with respect to background features of the scenario. IE is often violated because subjects, when presented with information about one attribute, update their beliefs about others too. Labeling a country “a democracy,” for example, affects subjects’ beliefs about the country’s geographic location. When IE is violated, the effect of the manipulation need not correspond to the quantity of interest (the effect of beliefs about the focal attribute). We formally define the IE assumption, relating it to the exclusion restriction in instrumental-variable analysis. We show how to predict IE violations ex ante and diagnose them ex post with placebo tests. We evaluate three strategies for achieving IE. Abstract encouragement is ineffective. Specifying background details reduces imbalance on the specified details and highly correlated details, but not others. Embedding a natural experiment in the scenario can reduce imbalance on all background beliefs, but raises other issues. We illustrate with four survey experiments, focusing on an extension of a prominent study of the democratic peace.
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When asked to rate 'the Republican Party' and 'the Democratic Party' on feeling thermometers, respondents need to conjure an image of the typical member of the group they are rating.
“When asked to rate”
From Measuring affective polarization: Matching concepts to measures (2026) · cites this paper as Dafoe et al., 2018
The Formal Exposition (section 2; page unavailable) says survey respondents consider scenarios evoked in their minds, and section 7.1 says they may infer unspecified background features. That supports the general idea that respondents fill in details, but the paper does not address feeling-thermometer ratings of political parties or say respondents picture a typical party member. (LLM, conf=0.76)
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- Authors
- Dafoe, A., Zhang, B., & Caughey, D.
- Affiliations
- Yale University (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Dafoe, Allan
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2018-08-03
- Volume
- 26
- Issue
- 4
- Pages
- 399-416
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W2885361509
- Cited by
- 356
- References
- 41
- Citations by year
- 2026: 38 · 2025: 70 · 2024: 61 · 2023: 54 · 2022: 54 · 2021: 32 · 2020: 24 · 2019: 14 · 2018: 6 · 2017: 3
- FWCI
- 15.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Political Analysis (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation
- Yale University
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Keywords
- information equivalence, survey experiment, hypothetical scenarios, belief updating, exclusion restriction, placebo tests, background beliefs, natural experiment, democratic peace
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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