Título: Using a Probabilistic Model to Assist Merging of Large-scale Data Sets

Autor(es):  Ted Enamorado, Ben Field y Kosuke Imai

Presenta:  Ted Enamorado (Princeton University)

Lugar y hora: Martes 28 de marzo de 2017, 13:00 hrs. Sala P 301, Edificio Placa (Diagonal Paraguay 257)

Abstract

Since most social science research relies upon multiple data sources, merging data sets is an essential part of workflow for many researchers. In many situations, however, a unique identifier that unambiguously links data sets is unavailable and data sets may contain missing and inaccurate information. As a result, researchers can no longer combine data sets "by hand'' without sacrificing the quality of the resulting merged data set. This problem is especially severe when merging large-scale administrative records such as voter files. The existing algorithms to automate the merging process do not scale, result in many fewer matches, and require arbitrary decisions by researchers. To overcome this challenge, we develop a fast algorithm to implement the canonical probabilistic model of record linkage for merging large data sets. Researchers can combine this model with a small amount of human coding to produce a high-quality merged data set. The proposed methodology can handle millions of observations and account for missing data and auxiliary information. We conduct simulation studies to show that our algorithm performs well in a variety of practically relevant settings. Finally, we use our methodology to merge the campaign contribution data (5 million records), the Cooperative Congressional Election Study data (50 thousand records), and the nationwide voter file (160 million records).