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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records |
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Identification Number: |
doi:10.7910/DVN/YGUHTD |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2019-01-04 |
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Version: |
1 |
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Bibliographic Citation: |
Enamorado, Ted; Fifield, Benjamin; Imai, Kosuke, 2019, "Replication Data for: Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records", https://doi.org/10.7910/DVN/YGUHTD, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication Data for: Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records |
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Identification Number: |
doi:10.7910/DVN/YGUHTD |
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Authoring Entity: |
Enamorado, Ted (Princeton University) |
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Fifield, Benjamin (Princeton University) |
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Imai, Kosuke (Harvard University) |
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Distributor: |
Harvard Dataverse |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Fifield, Benjamin |
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Depositor: |
Fifield, Benjamin |
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Date of Deposit: |
2018-10-09 |
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Holdings Information: |
https://doi.org/10.7910/DVN/YGUHTD |
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Study Scope |
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Keywords: |
Social Sciences |
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Abstract: |
<b> Abstract: </b> 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 may contain missing and inaccurate information. These problems are severe especially when merging large-scale administrative records. We develop a fast and 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. We conduct comprehensive simulation studies to evaluate the performance of our algorithm in realistic scenarios. We also apply our methodology to merging campaign contribution records, survey data, and nationwide voter files. An open-source software package is available for implementing the proposed methodology. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
This dataset not to be distributed/posted outside of the Harvard Dataverse. All downloads should take place directly on Harvard Dataverse to ensure data integrity. |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Enamorado, Ted, Benjamin Fifield, and Kosuke Imai. 2019. “Using a Probabilistic Model to Assist Merging of Large-Scale Administrative Records.” <i>American Political Science Review</i> 113 (2): 353--371. |
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Identification Number: |
10.1017/S0003055418000783 |
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Bibliographic Citation: |
Enamorado, Ted, Benjamin Fifield, and Kosuke Imai. 2019. “Using a Probabilistic Model to Assist Merging of Large-Scale Administrative Records.” <i>American Political Science Review</i> 113 (2): 353--371. |
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Label: |
appendix.tar |
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Text: |
Replication materials for the appendix. |
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Notes: |
application/x-tar |
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Label: |
mainpaper.tar |
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Text: |
Replication materials for the main paper. |
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Notes: |
application/x-tar |
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Label: |
README.pdf |
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Text: |
Guide to the replication files for "Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records." |
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Notes: |
application/pdf |