Demystifying probabilistic linkage: Common myths and misconceptions.

Area of Science:

• Data Science
• Information Science
• Computer Science

Background:

• Record linkage is crucial for data integration, but distinctions between probabilistic and deterministic methods are often unclear.
• Misconceptions about probabilistic linkage lead to its underutilization and mistrust in its results.
• Current understanding often oversimplifies the differences, attributing them to inherent method properties rather than implementation details.

Purpose of the Study:

• To clarify the distinctions between probabilistic and deterministic record linkage approaches.
• To address misconceptions surrounding probabilistic linkage and its outputs.
• To demonstrate how implementation choices influence linkage outcomes and guide users in selecting appropriate methods.

Main Methods:

• Comparative analysis of probabilistic and deterministic record linkage procedures.
• Examination of how design choices impact the outputs of both linkage methods.
• Discussion of common misconceptions and their effect on method selection.

Main Results:

• The differences between probabilistic and deterministic linkage are primarily implementation-dependent, not intrinsic.
• Both methods can be tailored to achieve similar or different outcomes based on design decisions.
• Probabilistic linkage offers greater flexibility, power, and data utilization when appropriately implemented.

Conclusions:

• Clarifying implementation nuances can reduce mistrust in probabilistic linkage.
• Understanding linkage design choices is key to selecting and tailoring methods for specific applications.
• Probabilistic linkage, when well-implemented, provides a more powerful and flexible approach to record linkage.