Correcting for Nonignorable Nonresponse Bias in Ordinal Observational Survey Data
2026-09-04
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., a respondent’s rating of the interview or 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 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, we show that accounting for nonignorable nonresponse produces substantively meaningful shifts for life satisfaction (estimated latent correlation ρ ≈ 0.47 $\rho \approx 0.47$ rho almost equals 0.47 ), while yielding only modest changes for retrospective economic evaluations ( ρ ≈ 0.14 $\rho \approx 0.14$ rho almost equals 0.14 ), highlighting when nonignorable nonresponse substantively affects survey estimates.