Since most social science research relies upon multiple data sources, merging data sets is an essential part of researchers' workflow. Unfortunately, a unique identifier that unambiguously links records is often unavailable and data sets may contain missing and inaccurate information. These problems are severe especially when merging large-scale administrative records. The authors develop a faster and more scalable algorithm to implement a canonical probabilistic model of record linkage that has many advantages over deterministic methods frequently used by social scientists. The proposed methodology efficiently handles millions of observations while accounting for missing data and measurement error, incorporating auxiliary information, and adjusting for uncertainty about merging in post-merge analyses. The authors conduct comprehensive simulation studies to evaluate the performance of our algorithm in realistic scenarios. They also apply our methodology to merging campaign contribution records, survey data, and nationwide voter files. They provide open-source software for implementing the proposed methodology. (Author/publisher)
Abstract