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Activity Number:
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548
- Total Survey Errors in the Combination of Probability and Nonprobability Samples
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Type:
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Invited
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Date/Time:
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Wednesday, July 31, 2019 : 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 #300542
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Title:
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Total Survey Error: Approaches for Measuring Bias and Variance Components When Combining Probability and Non-Probability Samples
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Author(s):
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Nadarajasundaram Ganesh* and Edward Mulrow and Vicki Pineau and Michael Yang
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Companies:
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NORC at the University of Chicago and NORC at the University of Chicago and NORC at the University of Chicago and NORC at the University of Chicago
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Keywords:
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Calibration;
Non-probability sample;
Small area estimation;
Total Survey Error
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Abstract:
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Probability sampling has been the standard basis for design-based inference from a sample to a target population. In the era of big data and increasing data collection costs, however, there has been growing demand for methods to combine data from probability and nonprobability samples in order to improve the cost efficiency of survey estimation without loss of statistical accuracy. In a prior presentation, we discussed the use of small area estimation models to generate unbiased estimates and to estimate the bias associated with a non-probability sample assuming the smaller probability sample yields unbiased estimates. In this presentation, we discuss methods to estimate the variance associated with such unbiased small area estimates. Furthermore, we consider a class of biased small area estimators that could potentially result in estimates with smaller total survey error compared to the previously described unbiased small area estimators. We investigate the properties of our estimators and the properties of our bias and variance estimators using a large midterm election survey and a simulation study.
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Authors who are presenting talks have a * after their name.
