Which type of record linkage relies on certainty in matching data records?

Deterministic record linkage is focused on ensuring that there is absolute certainty when matching data records. This method utilizes specific, predefined criteria or identifiers that must match exactly across the datasets involved. For instance, when two records are linked deterministically, it could be based on unique identifiers such as Social Security numbers, medical record numbers, or other distinct attributes that only apply to one individual.

This approach is beneficial in contexts where precision is critical and errors due to incorrect matches could lead to significant issues, such as in cancer registries where accurate patient information is vital for treatment and research. Deterministic linkage guarantees that when two records are matched, they are definitely about the same individual, providing a high level of confidence in the resulting dataset.

The emphasis on exact matches in deterministic linkage makes it different from other methods where uncertainty or partial matches can play a role. For instance, probabilistic linkage deals with data that might not match exactly, applying algorithms to estimate the likelihood that records correspond to the same entity based on their similarity. In contrast, deterministic linkage does not allow for this ambiguity and demands a strict adherence to matching criteria.