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2007 - Midwest Political Science Association Pages: 31 pages || Words: 9988 words || 
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1. Peden, Mindy. "Probably Democratic: Probability Theory, Chance, and Democratic Theory" Paper presented at the annual meeting of the Midwest Political Science Association, Palmer House Hotel, Chicago, IL, Apr 12, 2007 <Not Available>. 2019-12-11 <http://citation.allacademic.com/meta/p198084_index.html>
Publication Type: Conference Paper/Unpublished Manuscript
Abstract: This paper explores possible alternative uses for the role of luck in theorizing democracy and explores how the potentially fortune friendly use of probability theory in the social sciences has been misrepresented to highlight an imaginary predictability of political life that leads many theorists to shy away from its use.

2017 - APSA Annual Meeting & Exhibition Words: 230 words || 
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2. Aronow, Peter., Baron, Jonathon. and Coppock, Alexander. "Leveraging Probability Samples for Better Estimates from Non-Probability Samples" Paper presented at the annual meeting of the APSA Annual Meeting & Exhibition, TBA, San Francisco, CA, Aug 31, 2017 <Not Available>. 2019-12-11 <http://citation.allacademic.com/meta/p1251030_index.html>
Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: Researchers commonly reweight responses from non-probability samples to match the demographic characteristics of a given target population. When nonresponse is ignorable conditional on demographics, this procedure allows researchers to generate good estimates of population quantities. However, reweighting with respect to demographics is often insufficient and a larger conditioning set that includes non-demographic information is required. In this paper, we propose the “pseudo-probability survey design,” which involves: (1) fielding a subset of questions from a known, benchmark probability sample on a non-probability sample; and (2) reweighting the resulting data to match the demographics and responses of the joint population distribution of the substantive questions of interest. We demonstrate that the availability of a larger conditioning set can make inferences drawn from a non-probability sample more credible. We discuss well-known results on nonparametric identification, estimation, inference, and consequences of misspecification. We illustrate this approach using an augmented replication of a subset of questions asked in the 2016 American National Election Studies survey (ANES), performed on Mechanical Turk in concurrence with the ANES survey. The present study, which we call a pseudo-ANES, considers the empirical implications of conditioning sets composed of demographics, general self-reported political attitudes, and subject-specific attitudes in the domain of gun rights. Full results illustrating whether or not our pseudo-ANES can recover ANES benchmarks will be presented pending the release of the 2016 ANES data, according to our preanalysis plan.

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