Katie Harron*, Harvey Goldstein, Chris Dibben

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingForeword/postscript

Abstract / Description of output

Recent developments in data linkage methodology have concentrated on bias in the analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage. This introductory chapter provides a brief background to the development of data linkage methods and introduces a few common terms. It highlights the most important issues that have emerged in recent years and describes how this book attempts to deal with these issues. Data linkage methods may fall into two categories: the deterministic linkage method and the probabilistic linkage method. Linkage error occurs when record pairs are misclassified as links or non-links. The impact of linkage error on analysis of linked data depends on the structure of the data, the distribution of error and the analysis to be performed. Privacy-preserving data linkage attempts to avoid the controversial release of personal identifiers by providing means of linking and performing analysis on encrypted data.

Original languageEnglish
Title of host publicationMethodological Developments in Data Linkage
Number of pages7
ISBN (Electronic)9781119072454
ISBN (Print)9781118745878
Publication statusPublished - 5 Feb 2016

Keywords / Materials (for Non-textual outputs)

  • Data analysis
  • Data linkage methods
  • Encrypted data
  • Linkage errors


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