Survey Methodology
Estimating the false negatives due to blocking in record linkage

by Abel Dasylva and Arthur GoussanouNote 1

  • Release date: January 6, 2022
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Abstract

When linking massive data sets, blocking is used to select a manageable subset of record pairs at the expense of losing a few matched pairs. This loss is an important component of the overall linkage error, because blocking decisions are made early on in the linkage process, with no way to revise them in subsequent steps. Yet, measuring this contribution is still a major challenge because of the need to model all the pairs in the Cartesian product of the sources, not just those satisfying the blocking criteria. Unfortunately, previous error models are of little use because they typically do not meet this requirement. This paper addresses the issue with a new finite mixture model, which dispenses with clerical reviews, training data, or the assumption that the linkage variables are conditionally independent. It applies when applying a standard blocking procedure for the linkage of a file to a register or a census with complete coverage, where both sources are free of duplicate records.

Key Words:   Indexing; Massive data sets; Entity resolution; Data integration; Machine learning; Classification.

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How to cite

Dasylva, A., and Goussanou, A. (2021). Estimating the false negatives due to blocking in record linkage. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 47, No. 2. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2021002/article/00002-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.

© His Majesty the King in Right of Canada as represented by the Minister of Industry, 2022

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Catalogue No. 12-001-X

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Definitions, notations and assumptions
Section 3. Neighbours and errors
Section 4. Finite mixture model
Section 5. Estimation procedure
Section 6. Empirical study
Section 7. Conclusions and future work
Disclaimer
Acknowledgements
Appendix A
References

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