Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel two-step method to reduce bias in survey estimates caused by nonresponse. By utilizing confidential continuous auxiliary data to estimate response propensity, the approach improves the accuracy of statistical inference for survey data users.
Area Of Science
- Statistics
- Survey Methodology
- Data Science
Background
- Increasing nonresponse rates in probability surveys lead to biased statistical inference.
- Auxiliary information can reduce estimation bias, but confidentiality concerns often lead to discretization of continuous variables, weakening their utility.
- Discretized auxiliary data may not fully capture the relationship with survey outcomes, limiting improvements in survey estimates.
Purpose Of The Study
- To propose a novel two-step strategy to effectively utilize confidential continuous auxiliary data for improving survey estimates.
- To address the challenge of weakened utility of auxiliary information due to discretization for confidentiality.
- To develop a method that enhances the accuracy and efficiency of statistical inference in surveys with high nonresponse.
Main Methods
- A two-step strategy is proposed: statistical agencies use confidential continuous auxiliary data to estimate response propensity scores.
- These propensity scores are incorporated into a modified population dataset for data users.
- Data users employ a Bayesian model with splines, including discretized variables and propensity scores, for predictive survey inference.
Main Results
- Simulations demonstrate that the proposed method yields more efficient estimates of population means.
- The method provides better coverage for 95% credible intervals compared to alternative approaches.
- The approach was successfully illustrated using the Ohio Army National Guard Mental Health Initiative (OHARNG-MHI) dataset.
Conclusions
- The proposed two-step strategy effectively leverages confidential continuous auxiliary data to mitigate nonresponse bias in survey estimates.
- The method enhances the precision and reliability of statistical inference, offering improved credible interval coverage.
- The developed methods are accessible through the R package AuxSurvey, promoting wider application in survey research.
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