Survey Methodology
Multiple imputation of missing values in household data with structural zeros

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by Olanrewaju Akande, Jerome Reiter and Andrés F. BarrientosNote 1

  • Release date: June 27, 2019
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Abstract

We present an approach for imputation of missing items in multivariate categorical data nested within households. The approach relies on a latent class model that (i) allows for household-level and individual-level variables, (ii) ensures that impossible household configurations have zero probability in the model, and (iii) can preserve multivariate distributions both within households and across households. We present a Gibbs sampler for estimating the model and generating imputations. We also describe strategies for improving the computational efficiency of the model estimation. We illustrate the performance of the approach with data that mimic the variables collected in typical population censuses.

Key Words:      Categorical; Census; Edit; Latent; Mixture; Nonresponse.

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How to cite

Akande, O., Reiter, J. and Barrientos, A.F. (2019). Multiple imputation of missing values in household data with structural zeros. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 45, No. 2. Paper available at https://www150.statcan.gc.ca/n1/pub/12-001-x/2019002/article/00005-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2019

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Review of the NDPMPM model
Section 3. Handling missing data using the NDPMPM
Section 4. Strategies for speeding up the MCMC sampler
Section 5. Empirical study
Section 6. Discussion
Acknowledgements
Appendix
References

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