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Activity Number:
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311
- Does Missing Data Affect Outcomes Examined Using Nationally Representative Survey Databases? A Comparison of Traditional and Data Science Approaches
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Type:
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Invited
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #320378
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Title:
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Making Use of Summary Information from Large Databases Without Access to Their Individual Data
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Author(s):
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Peisong Han*
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Companies:
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University of Michigan
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Keywords:
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data integration;
large databases;
estimation efficiency;
population heterogeneity;
summary information
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Abstract:
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It is common to have access to summary information from external studies without access to their individual data. Such information can be useful in model building for an internal study of interest and can improve parameter estimation efficiency when incorporated. However, external studies may target populations different from the internal study, in which case an incorporation of the corresponding summary information may introduce estimation bias. We develop a method that selects the external studies whose target population is the same as the internal study and simultaneously incorporates their available information into estimation. The resulting estimator has the efficiency as if we knew which external studies target the same population and made use of information from those studies alone.
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Authors who are presenting talks have a * after their name.
