Abstract

Demographic data suffer from sampling errors and from biases arising from coverage and content errors that may be systematic and noncompensating. Common and problematic errors for demographic estimation are those affecting the reporting of age, parity, and deaths. Age misreporting affects population counts and vital rates. Techniques of data evaluation and correction relying on individual-level analysis include the postenumeration survey, imputation, capture–recapture methods, and statistical analysis. Techniques using aggregate-level data employ digit preference indices, sex and age ratios, and smoothing; demographic accounting and internal consistency; and parametric functions, relational models, statistical modeling, and time-based methods.