title: A scaling approach to record linkage.
abstract: With increasing availability of large datasets 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. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: University of Bristol, Bristol, U.K.|University College London, London, U.K.
authors.forename: Harvey
authors.identifier: http://orcid.org/0000-0003-3878-4122
authors.initials: H
authors.lastname: Goldstein
authors.affiliation: London School of Hygiene and Tropical Medicine, London, U.K.
authors.forename: Katie
authors.initials: K
authors.lastname: Harron
authors.affiliation: University College London, London, U.K.
authors.forename: Mario
authors.initials: M
authors.lastname: Cortina-Borja
mesh_terms: D000465:Algorithms; D056808:Biostatistics; D006801:Humans; D016013:Likelihood Functions; D008498:Medical Record Linkage / Q000379:methods*; D015233:Models, Statistical; D012984:Software
keywords: correspondence analysis; data linkage; record linkage; scaling