Experimental Designs

I’ve Got the Power: A Survey of Issues Surrounding Statistical Power in the Design and Analysis of Survey Experiments

Clayton Webb (University of Kansas) and Cameron Wimpy (Arkansas State University)

Abstract: The power of a hypothesis test to reject a false null hypothesis is a basic concept of statistical inference that is introduced in most, if not all, introductory texts. Despite this, a systematic survey of work published in the American Journal of Political Science (AJPS), the American Political Science Review (APSR), the Journal of Politics (JOP...

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The Consequences of Social Interaction on Outparty Affect and Stereotypes

Erin Rossiter (Washington University in St. Louis)

*Award for Best Graduate Student Poster - Applications*

Abstract: Americans increasingly dislike members of the opposite political party and associate negative stereotypes with them such as close-minded, mean, and hypocritical. Yet...

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The Heuristic Issue Voter: Issue Preferences and Candidate Choice

Gabriel Madson (Duke University)

Abstract: Issue voting, where citizens select candidates primarily for their positions on political issues, is a normatively appealing theory of voting. A public whose political behavior is driven by...

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The Puzzling Politics of R&d: Signaling Competence Through Risky Projects

Natalia Lamberova (University of California, Los Angeles)

Abstract: Why do some leaders devote significant funds to research and development (R&D) even though such investments are risky, less visible to the public...

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A General Method for Detecting & Characterizing Interference in Field Experiments

Connor Jerzak (Harvard University)

Abstract: With the rise of online social networks, there has been growing interest in modeling how experimental units influence each another---a phenomenon known as "interference'' in the causal inference literature. Current models for interference generally requires knowledge of the way in which units are connected. Yet, in most field experiments, such data is unavailable. In this paper, we propose a...

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Leveraging Observational Outcomes to Improve the Generalization of Experimental Results

Melody Huang (University of California, Los Angeles), Erin Hartman (University of California, Los Angeles), Naoki Egami (Columbia University) and Luke Miratrix (Harvard University)

*Award for Best Graduate Student Poster - Methods*

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Priming Bias Versus Post-Treatment Bias in Experimental Designs

Jacob Brown (Harvard University), Matthew Blackwell (Harvard University), Sophie Hill (Harvard University), Kosuke Imai (Harvard University) and Teppei Yamamoto (Massachusetts Institute of Technology)

Abstract:  It is now widely recognized that conditioning on variables affected by a treatment can induce post-treatment bias when estimating causal...

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