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020 ▼a 9780438154216
035 ▼a (MiAaPQ)AAI10790998
035 ▼a (MiAaPQ)umd:18928
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 310
1001 ▼a Mercer, Andrew William.
24510 ▼a Selection Bias in Nonprobability Surveys: A Causal Inference Approach.
260 ▼a [S.l.] : ▼b University of Maryland, College Park., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 153 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Frauke Kreuter.
5021 ▼a Thesis (Ph.D.)--University of Maryland, College Park, 2018.
520 ▼a Many 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
520 ▼a This 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
520 ▼a Next, 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
520 ▼a Finally, 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
590 ▼a School code: 0117.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a University of Maryland, College Park. ▼b Survey Methodology.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0117
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997618 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자