A scaling approach to record linkage

Harvey Goldstein, katie Harron, Mario Cortino-Borja

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)
304 Downloads (Pure)


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
Issue number16
Early online date16 Mar 2017
Publication statusPublished - 20 Jul 2017


  • scaling
  • record linkage
  • correspondence analysis
  • data linkage


Dive into the research topics of 'A scaling approach to record linkage'. Together they form a unique fingerprint.

Cite this