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Activity Number:
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210
- Contributed Poster Presentations: Survey Research Methods Section
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #313048
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Title:
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A Matching Error Model for Record Linkage
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Author(s):
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Ben Williams* and Lynne Stokes
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Companies:
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University of Denver and Southern Methodist University
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Keywords:
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record linkage;
non-probability;
sampling;
survey;
matching;
linkage
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Abstract:
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Much recent research has focused on methods for combining a probability sample with a non-probability sample to improve estimation. If units exist in both samples, it becomes necessary to link the information from the two samples for these units. Record linkage is a technique to link records from two lists that refer to the same unit but lack a unique identifier across both lists. Because record linkage is a probabilistic endeavor it introduces randomness into estimators that use the linked data. The effects of this randomness on regression involving the linked datasets has been examined (for example: Lahiri and Larsen, 2005). However, the effect of matching error has not been considered for the case of estimating the total of a population from a capture-recapture model. In this paper we present a general model for matching errors arising from a linkage procedure and examine the effects on bias and variance of some estimators used for such scenarios. Our work is motivated by the application of estimating fish catch in the Gulf of Mexico.
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Authors who are presenting talks have a * after their name.
