The widely used Fellegi-Sunter model for probabilistic record linkage does not leverage information contained in field values and consequently leads to identical classification of match status regardless of whether records agree on rare or common values. Since agreement on rare values is less likely to occur by chance than agreement on common values, records agreeing on rare values are more likely to be matches. Existing frequency-based methods typically rely on knowledge of error probabilities associated with field values and frequencies of agreed field values among matches, often derived using prior studies or training data. When such information is unavailable, applications of these methods are challenging. In this paper, we propose a simple two-step procedure for frequency-based matching using the Fellegi-Sunter framework to overcome these challenges. Matching weights are adjusted based on frequency distributions of the agreed field values among matches and non-matches, estimated by the Fellegi-Sunter model without relying on prior studies or training data. Through a real-world application and simulation, our method is found to produce comparable or better performance than the unadjusted method. Furthermore, frequency-based matching provides greater improvement in matching accuracy when using poorly discriminating fields with diminished benefit as the discriminating power of matching fields increases.
Keywords: Fellegi–Sunter model; frequency-based matching; latent class analysis; probabilistic matching; record linkage.
© 2021 Informa UK Limited, trading as Taylor & Francis Group.