Abstract:Many political surveys rely on post-stratification, raking, or related weighting adjustments to align respondents with the target population. But when respondents differ from nonrespondents on the outcome itself (nonignorable nonresponse), these adjustments can fail, introducing bias even into basic descriptives. We provide a practical method that corrects for nonignorable nonresponse by leveraging response-propensity proxies (e.g., interviewer-coded cooperativeness) observed among respondents to extrapolate toward nonrespondents, while directly integrating observable covariates and retaining the benefits of post-stratification with known population shares. The method generalizes the variable-response-propensity (VRP) framework of Peress (2010) from binary to ordinal outcomes, which are widely used to measure trust, satisfaction, and policy attitudes. The resulting estimator is computed by maximum likelihood and implemented in a compact R routine that handles both ordinal and binary outcomes. Using the 2024 American National Election Study (ANES), we show that accounting for nonignorable nonresponse produces substantively meaningful shifts for life satisfaction (estimated latent correlation $\rho \approx 0.53$), while yielding negligible changes for retrospective economic evaluations ($\rho \approx 0$), highlighting when nonignorable nonresponse substantively affects survey estimates.
| Comments: | 17 pages |
| Subjects: | Methodology (stat.ME) |
| Cite as: | arXiv:2602.07704 [stat.ME] |
| (or arXiv:2602.07704v2 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2602.07704 arXiv-issued DOI via DataCite |
From: Lukáš Lafférs [view email]
[v1]
Sat, 7 Feb 2026 21:15:33 UTC (638 KB)
[v2]
Mon, 25 May 2026 13:12:17 UTC (417 KB)
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