JSM 2004 - Toronto

Abstract #300553

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Activity Number: 100
Type: Topic Contributed
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #300553
Title: A Bayesian Record Linkage Methodology for Multiple Imputation of Missing Links
Author(s): Michael H. McGlincy*+
Companies: Strategic Matching, Inc.
Address: PO Box 334, Morrisonville, NY, 12962,
Keywords: probabilistic ; record ; linkage ; Bayesian ; imputation
Abstract:

Probabilistic record linkage can be an effective research technique even if available records lack strong personal identifiers or if identifying fields contain many errors or omissions. Traditional methodologies typically select a single set of linked record pairs for research based on a match weight test statistic and clerical review of marginal pairs. However, false positives links and false negative links can make such datasets unrepresentative of the total population of true linked pairs. The methodology described here addresses this problem. First, a full Bayesian model is developed for the posterior probability that a record pair is a true match given observed agreements and disagreements of comparison fields. Second, observed-data posterior distributions for model parameters and true match status are estimated simultaneously through MCMC data augmentation with multiple chains. This gives multiple complete and unbiased sets of imputed linked record pairs. Finally, population estimates are obtained from each imputation and consolidated using established techniques. Application of the methodology by a consortium of traffic safety researchers is described.


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