Hierarchical Logistic Regression Models for Imputation of Unresolved Enumeration Status in Undercount Estimation

原文摘要

In the process of collecting Post-Enumeration Survey (PES) data to evaluate census coverage, it is inevitable that there will be some individuals whose enumeration status (outcome in the census-PES match) remains unresolved even after extensive field follow-up operations. Earlier work developed a logistic regression framework for imputing the probability that unresolved individuals were enumerated in the census, so that the probability of having been enumerated is allowed to depend on covariates. The covariates may include demographic characteristics, geographic information, and census codes that summarize information on the characteristics of the match (e.g., the before-follow-up match code assigned by clerks to describe the type of match between PES and census records). In the production of 1990 undercount estimates, the basic logistic regression model was expanded into a mixed hierarchical model to allow for the presence of group-specific effects, where groups are characterized by common before-follow-up match code. Parameter estimates for individual match-code groups thus “borrow strength” across groups by making use of observed relationships between group-specific parameter estimates in the various groups and the characteristics of the groups. This allows predictions to be made for groups for which there are few or no resolved cases to which to fit the model. The model was fitted by an approximate expectation-conditional-maximization (ECM) algorithm, using a large-sample approximation to the posterior distributions of group parameters. Uncertainty in estimation of model parameters was evaluated using a resampling procedure and became part of the evaluation of total error in PES estimates of population. Results from fitting the model in the 1990 Census and PES are described.

摘要来源：OpenAlex 索引摘要（稿件版本未注明）