Work

Demystifying probabilistic linkage

Year: 2018

Type: article

Abstract: they are implemented than because of any intrinsic differences. In the way they are generally applied, probabilistic and deterministic procedures can be little more than alternative means to similar ends-or they can arrive at very different ends depending on choices that are made during implementation. Misconceptions about probabilistic linkage contribute to reluctance for implementing it and mistrust of its outputs. We aim to explain how the outputs of either approach can be tailored to suit the intended application, but also to highlight the ways in which probabilistic linkage is generally more flexible, more powerful and more informed by the data. This is accomplished by examining common misconceptions about probabilistic linkage and its difference from deterministic linkage, highlighting the potential impact of design choices on the outputs of either approach. We hope that better understanding of linkage designs will help to allay concerns about probabilistic linkage, and help data linkers to select and tailor procedures to produce outputs that are appropriate for their intended use.

Source: International Journal for Population Data Science

DOI: 10.23889/ijpds.v3i1.410

Authors: James C Doidge, Katie Harron

Institutions: University of South Australia, UCL Australia, University College London, London School of Hygiene & Tropical Medicine

Language: English

Topic: Data Quality and Management

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,747

Funders: Wellcome Trust, Medical Research Council, Economic and Social Research Council