Goldstein, H., Harron, K. & Cortina-Borja, M. (2017). A scaling approach to record linkage. Statistics in Medicine,36(16), R. D'Agostino, S. Day, E. Goetghebeur and J. Greenhouse. 2514-2521. United Kingdom: John Wiley and Sons Ltd. Retrieved from https://doi.org/10.1002/sim.7287
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.
Institute for Learning Sciences and Teacher Education
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