A scaling approach to record linkage

Harvey Goldstein, katie Harron, Mario Cortino-Borja

Research output: Contribution to journalArticle (Academic Journal)

7 Citations (Scopus)
243 Downloads (Pure)

Abstract

With increasing availability of large data sets derived from administrative and other sources, there is an increasing demand for the successful linking of these to provide rich sources of data for further analysis. Variation in the quality of identifiers used to carry out linkage means that existing approaches are often based upon ‘probabilistic’ models, which are based on a number of assumptions, and can make heavy computational demands. In this paper we suggest a new approach to classifying record pairs in linkage, based upon weights (scores) derived using a scaling algorithm. The proposed method does not rely on training data, is computationally fast, requires only moderate amounts of storage and has intuitive appeal.
Original languageEnglish
Pages (from-to)2514-2521
Number of pages6
JournalStatistics in Medicine
Volume36
Issue number16
Early online date16 Mar 2017
DOIs
Publication statusPublished - 20 Jul 2017

Keywords

  • scaling
  • record linkage
  • correspondence analysis
  • data linkage

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    Goldstein, H., Harron, K., & Cortino-Borja, M. (2017). A scaling approach to record linkage. Statistics in Medicine, 36(16), 2514-2521. https://doi.org/10.1002/sim.7287