A Total Statistical Error Framework for Comparing Census Data Collection Methods

Abstract

Population censuses increasingly rely on imputation to assign usual-residence addresses for non-responding dwellings, yet no formal statistical framework has existed for comparing competing imputation methods on their combined coverage and address-accuracy performance. We develop such a framework within a Total Statistical Error (TSE) paradigm that is not restricted to survey-based data collection and applies equally to register-based and administrative data sources. The central quantity is a unit-level binary correctness indicator, equal to one if and only if a person is both enumerated and assigned to the correct usual-residence address; its complement is the unit TSE, and aggregating over the population yields a correctness rate that serves as the basis for head-to-head method comparison. We distinguish two assessment paradigms: Paradigm I, in which a post-enumeration survey (PES) provides reference values for a probability sample, and Paradigm II, in which a complete benchmark makes the correctness rate directly computable. We illustrate the framework using 2021 Australian census microdata, comparing k-nearest-neighbour and random forest imputation under a not-missing-at-random (NMAR) mechanism. A key finding is that dependent nonresponse between the census and a simulated PES causes naive Paradigm I subgroup estimates to be severely biased when subgroup deletion rates are small. Augmenting the PES response propensity model with the census nonresponse indicator reduces this bias by approximately 90%; simulation experiments show that the nonresponse indicator alone drives the correction, with the additionally included imputed set A values contributing negligible further reduction. This is the preferred strategy whenever dependent nonresponse between the census and PES is a concern.