Introduction

Katie Harron*, Harvey Goldstein, Chris Dibben

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

Abstract

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
EditorsKatie Harron, Harvey Goldstein, Chris Dibben
Pages1-7
Number of pages7
ISBN (Electronic)9781119072454
DOIs
Publication statusPublished - 5 Feb 2016

Publication series

NameWiley Series in Probability and Statistics
PublisherWiley
ISSN (Print)1940-6347

Keywords

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

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