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Selection Bias in Nonprobability Surveys: A Causal Inference Approach

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개인저자Mercer, Andrew William.
단체저자명University of Maryland, College Park. Survey Methodology.
서명/저자사항Selection Bias in Nonprobability Surveys: A Causal Inference Approach.
발행사항[S.l.] : University of Maryland, College Park., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항153 p.
소장본 주기School code: 0117.
ISBN9780438154216
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Frauke Kreuter.
요약Many in the survey research community have expressed concern at the growing popularity of nonprobability surveys. The absence of random selection prompts justified concerns about self-selection producing biased results and means that traditional
요약This dissertation proposes an alternative classification for sources of selection bias for nonprobability surveys based on principles borrowed from the field of causal inference. The proposed typology describes selection bias in terms of the thr
요약Next, we show how net selection bias can be decomposed into separate additive components associated with exchangeability, positivity, and composition respectively. Using 10 parallel nonprobability surveys from different sources, we estimate thes
요약Finally, using the same six measures of civic engagement, we compare the performance of four approaches to nonprobability estimation based on Bayesian additive regression trees. These are propensity weighting (PW), outcome regression (OR), and t
일반주제명Statistics.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14997618

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