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 $ρ\approx 0.49$), while yielding negligible changes for retrospective economic evaluations ($ρ\approx 0$), highlighting when nonignorable nonresponse substantively affects survey estimates.

翻译：许多政治调查依赖事后分层、加权调整或相关加权方法使受访者与目标人群保持一致。但当受访者与非受访者在结果变量本身存在差异时（不可忽略的无应答），这些调整可能失效，甚至给基本描述性统计带来偏差。我们提出一种实用方法，通过利用受访者中观测到的应答倾向代理变量（如访员编码的合作意愿）向非受访者外推，同时直接整合可观测协变量并保留已知总体份额的事后分层优势，从而校正不可忽略的无应答偏差。该方法将Peress（2010）的变量应答倾向（VRP）框架从二元结果推广至序数结果——后者广泛用于测量信任度、满意度与政策态度。所得估计量通过最大似然法计算，并封装为可处理序数与二元结果的简洁R程序。基于2024年美国国家选举研究（ANES）数据，我们发现：考虑不可忽略的无应答会使生活满意度的估计产生实质性偏移（潜在相关系数估计值$ρ\approx 0.49$），而对回溯性经济评价的影响可忽略不计（$ρ\approx 0$），这揭示了不可忽略的无应答何时会实质影响调查估计。