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Activity Number:
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556
- Revisions
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Type:
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Contributed
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Date/Time:
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Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Government Statistics Section
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Abstract #323362
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Title:
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Inference Using Non-Probability Samples from a Demographic Survey
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Author(s):
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Giang Trinh* and Thomas Chesnut
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Companies:
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U.S. Census and U.S. Census
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Keywords:
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non-probability sample;
quasi-randomization;
super-population modeling;
linear mixed-effects models;
logistic regression;
small area estimation
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Abstract:
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Sample data sourced from surveys with high nonresponse rates or ‘big data’ present challenges for practitioners using these data for inference due to their non-probabilistic traits. To address these challenges, two model-based methods enabling inference from non-probability samples include quasi-randomization and super-population modeling. The purpose of this research is to simulate non-probability samples from a synthetic population and assess the quality of the inferences derived using the proposed model-based methods. Specifically, we will apply these methods to derive estimates for a target survey measure of unemployment in California. A new approach using a linear mixed-effects model of unemployment is introduced in an application of the super-population model-based method.
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Authors who are presenting talks have a * after their name.
