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- Title
Sensitivity Analysis for Survey Weights.
- Authors
Hartman, Erin; Huang, Melody
- Abstract
Survey weighting allows researchers to account for bias in survey samples, due to unit nonresponse or convenience sampling, using measured demographic covariates. Unfortunately, in practice, it is impossible to know whether the estimated survey weights are sufficient to alleviate concerns about bias due to unobserved confounders or incorrect functional forms used in weighting. In the following paper, we propose two sensitivity analyses for the exclusion of important covariates: (1) a sensitivity analysis for partially observed confounders (i.e., variables measured across the survey sample, but not the target population) and (2) a sensitivity analysis for fully unobserved confounders (i.e., variables not measured in either the survey or the target population). We provide graphical and numerical summaries of the potential bias that arises from such confounders, and introduce a benchmarking approach that allows researchers to quantitatively reason about the sensitivity of their results. We demonstrate our proposed sensitivity analyses using state-level 2020 U.S. Presidential Election polls.
- Subjects
SENSITIVITY analysis; CONVENIENCE sampling (Statistics); UNITED States presidential election, 2020; ELECTION forecasting; PUBLIC opinion polls; RESEARCH personnel
- Publication
Political Analysis, 2024, Vol 32, Issue 1, p1
- ISSN
1047-1987
- Publication type
Article
- DOI
10.1017/pan.2023.12