Variance Estimation after Mass Imputation Based on Combined Administrative and Survey Data

Abstract

This article discusses methods for evaluating the variance of estimated frequency tables based on mass imputation. We consider a general set-up in which data may be available from both administrative sources and a sample survey. Mass imputation involves predicting the missing values of a target variable for the entire population. The motivating application for this article is the Dutch virtual population census, for which it has been proposed to use mass imputation to estimate tables involving educational attainment. We present a new analytical design-based variance estimator for a frequency table based on mass imputation. We also discuss a more general bootstrap method that can be used to estimate this variance. Both approaches are compared in a simulation study on artificial data and in an application to real data of the Dutch census of 2011.

Keywords

Data integration finite-population bootstrap categorical data census tables

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