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Activity Number: 526 - Trends in Sample Design
Type: Contributed
Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #322315
Title: Optimizing Data Collection Interventions to Balance Cost and Quality in a Sequential Multimode Survey
Author(s): Stephanie Coffey* and Michael Elliott
Companies: US Census Bureau and University of Michigan
Keywords: adaptive design; responsive design; Bayesian methods; optimization; survey costs
Abstract:

Decreasing response rates and rising data collection costs mean that it is getting more expensive to conduct high quality survey data collections. Responsive and adaptive designs have emerged as a framework for reallocating data collection resources in order to control survey costs and errors. Here, we report on a responsive design experiment that optimized the cost-quality tradeoff by minimizing a function of data collection costs and the RMSE of a key survey measure, salary. We leveraged a Bayesian framework to incorporate prior information and generate estimates of response propensity, salary, and data collection costs for use in our optimization rule. At three time points, we implement the rule and identify cases for which reduced effort would have minimal effect on the RMSE of mean salary, while allowing us to reduce data collection costs. We find that this optimization allowed us to reduce data collection costs by nearly 10%, without a statistically or practically significant increase in the RMSE of mean salary or a decrease in the unweighted response rate. This experiment demonstrates the potential for these types of designs to effectively allocate data collection resources.


Authors who are presenting talks have a * after their name.

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