A Formal Theory of Survey Experiment Generalizability: Attention and Salience

[2405.06779] A Formal Theory of Survey Experiment Generalizability: Attention and Salience Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Econometrics arXiv:2405.06779 (econ) [Submitted on 10 May 2024 ( v1 ), last revised 14 Apr 2026 (this version, v4)] Title: A Formal Theory of Survey Experiment Generalizability: Attention and Salience Authors: Jiawei Fu , Xiaojun Li View a PDF of the paper titled A Formal Theory of Survey Experiment Generalizability: Attention and Salience, by Jiawei Fu and Xiaojun Li View PDF HTML (experimental) Abstract: Survey experiments are widely used to identify causal effects in political science and the social sciences. Yet researchers are typically interested in more than the internal validity of an experimentally induced contrast. They also want to know whether the estimated effect corresponds to the effect in the real world. We develop a formal theory of survey experiment generalizability grounded in behavioral microfoundations. The theory highlights two mechanisms. First, the survey environment shapes attention: it determines which considerations enter the respondent's active consideration set. Second, it shapes salience: conditional on consideration, it influences the relative weight assigned to those considerations. This framework yields two main results. Consideration-set compression generates amplification: survey-experimental effects can be larger in magnitude than their real-world counterparts, even for the same individuals, treatment content, and outcome. Context-dependent salience generates sign instability: the direction of the survey effect need not coincide with the direction of the corresponding real-world effect. The theory clarifies what survey experiments identify, when those effects are likely to generalize, and how survey designs can be modified to improve decision-environment transportability. Subjects: Econometrics (econ.EM) ; Applications (stat.AP) Cite as: arXiv:2405.06779 [econ.EM] (or arXiv:2405.06779v4 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2405.06779 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jiawei Fu [ view email ] [v1] Fri, 10 May 2024 19:10:18 UTC (497 KB) [v2] Sun, 1 Sep 2024 17:09:24 UTC (497 KB) [v3] Tue, 25 Mar 2025 00:21:05 UTC (498 KB) [v4] Tue, 14 Apr 2026 02:19:21 UTC (652 KB) Full-text links: Access Paper: View a PDF of the paper titled A Formal Theory of Survey Experiment Generalizability: Attention and Salience, by Jiawei Fu and Xiaojun Li View PDF HTML (experimental) TeX Source view license Current browse context: econ.EM < prev | next > new | recent | 2024-05 Change to browse by: econ stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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