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Handling nonresponse in surveys: analytic corrections compared with converting nonresponders
Paul Jenkins1, Giulia Earle-Richardson, Patrick Burdick
1Bassett Research Institute, Cooperstown, NY, USA. Paul.jenkins@bassett.org
American Journal of Epidemiology
|November 14, 2007
Summary
Double sampling reduced bias in health surveys but increased variance compared to propensity score weighting methods. Researchers found that the bias reduction may not outweigh the increased variance, impacting population estimates.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Health surveys are crucial for population health estimates.
- Bias in survey data can lead to inaccurate conclusions.
- Propensity score methods and double sampling are used to reduce bias.
Purpose of the Study:
- To compare the bias reduction effectiveness of double sampling against two propensity score weighting methods.
- To evaluate the impact of these methods on the variance of estimates.
Main Methods:
- Combined a large health survey with a simulation study.
- Used census data from one county and double sampling in six others.
- Modeled propensity scores using logistic regression of demographic variables to simulate response.
Main Results:
- Significant predictors of response included multiple occupancy, bank card ownership, gender, home ownership, head of household's age, and income.
- Double-sampling estimates were closer to population values than propensity score weighting methods.
- Double sampling resulted in greater variance (p < 0.01) compared to weighting methods.
Conclusions:
- Double sampling offers marginal bias reduction but at the cost of increased variance.
- The trade-off between bias reduction and increased variance needs careful consideration when choosing a method.
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