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Probabilistic Matching

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Encyclopedia of Big Data

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Definition/Introduction

Probabilistic matching differs from the simplest data matching technique, deterministic matching. For deterministic matching, two records are said to match if one or more identifiers are identical. Deterministic record linkage is a good option when the entities in the data sets have identified common identifiers with a relatively high quality of data. Probabilistic matching is a statistical approach in measuring the probability that two records represent the same subject or individual based on whether they agree or disagree on the various identifiers (Dusetzina et al. 2014).

It calculates linkage composite weights based on likeness scores for identifier values and uses thresholds to determine a match, nonmatch, or possible match. The quality of resulting matches can depend upon one’s confidence in the specification of the matching rules (Zhang and Stevens 2012). It is designed to work using a wider set of data elements and all available identifiers for matching...

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Further Readings

  • Dusetzina, S. B., Tyree, S., Meyer, A. M., et al. (2014). Linking data for health services research: A framework and instructional guide. Rockville: Agency for Healthcare Research and Quality (US).

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  • Fellegi, I. P., & Sunter, A. B. (1969). A theory for record linkage. Journal of the American Statistical Association, 64, 1183–1210.

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  • Schumacher, S. (2007). Probabilistic versus deterministic data matching: Making an accurate decision, information management special reports. Washington, DC: The Office of the National Coordinator for Health Information Technology (ONC).

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  • Winkler, W. E. (1999). The state of record linkage and current research problems. Washington, DC: Statistical Research Division, US Census Bureau.

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  • Zhang, T., & Stevens, D. W. (2012). Integrated data system person identification: Accuracy requirements and methods. https://ssrn.com/abstract=2512590; https://doi.org/10.2139/ssrn.2512590.

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Author information

Authors and Affiliations

  1. Department of Accounting, Finance and Economics, Merrick School of Business, University of Baltimore, Baltimore, MD, USA

    Ting Zhang

Authors
  1. Ting Zhang
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Correspondence to Ting Zhang .

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Editors and Affiliations

  1. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Laurie A. Schintler

  2. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Connie L. McNeely

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Zhang, T. (2018). Probabilistic Matching. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_501-1

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  • DOI: https://doi.org/10.1007/978-3-319-32001-4_501-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32001-4

  • Online ISBN: 978-3-319-32001-4

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

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