nonresponseweighted

Nonresponse weighting, sometimes described by the term nonresponseweighted, refers to procedures that adjust survey data to account for unit nonresponse. In surveys, not all selected individuals respond, and respondents may differ systematically from nonrespondents. Weighting aims to compensate for this difference so survey estimates better reflect the target population.

Common methods include response propensity weighting and calibration. Response propensity weighting models the probability of response as a function of auxiliary variables (such as demographics or prior behavior) and assigns weights equal to the inverse of these probabilities. Calibration techniques—also called post-stratification or raking—modify weights so that the weighted sample matches known population totals on one or more characteristics (for example, age, sex, region). Weights are often normalized to preserve the overall sample size and may be trimmed to reduce the influence of extremely large weights, balancing bias reduction against increased variance.

Applications and considerations. Nonresponse weighting is widely used in national surveys, health studies, and opinion polls to reduce nonresponse bias. Its effectiveness depends on the availability and quality of auxiliary information and on the assumption that adjusted respondents are representative given the weighting model. Limitations include model misspecification and the potential for high-variance estimates due to large or highly variable weights. In practice, weighting is often used in combination with other approaches, such as imputation or follow-up efforts to reduce nonresponse.

Implementation steps typically include defining the target population, selecting auxiliary variables, estimating response propensities or calibrating to population totals, computing and applying weights, and evaluating the effective sample size and estimator performance.