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 $ρ\approx 0.53$), 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、迭代加权或相关权重调整，使受访者与目标人群保持一致。但当受访者与无应答者在结果变量本身存在差异（非随机无应答）时，此类调整可能失效，甚至导致基本描述性统计产生偏差。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（例如，访谈员编码的合作程度）外推至无应答者，同时直接纳入可观测协变量并保留已知总体份额下事后分层的优势，从而校正非随机无应答。该方法将Peress（2010）的变应答倾向（VRP）框架从二元结果推广至有序结果，后者广泛用于测量信任、满意度和政策态度。所得估计量通过最大似然法计算，并封装于一个紧凑的R程序中，可同时处理有序和二元结果。利用2024年美国国家选举研究（ANES）数据，我们表明：校正非随机无应答后，生活满意度估计产生实质性有意义变化（估计潜在相关性ρ≈0.53），而回顾性经济评价的变化可忽略不计（ρ≈0），这揭示了非随机无应答在何种情况下会实质性地影响调查估计。