# Improving the Efficiency of Model-Informed Decisionmaking in Responsive and Adaptive Survey Designs

> **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $229,539

## Abstract

PROJECT SUMMARY
 Sample surveys are a critical resource for health research. Large-scale national health surveys, such as
the National Health Interview Survey (NHIS), and a multitude of smaller-scale surveys funded by NIH, that are
essential for understanding the health of the population. However, sample surveys of all sizes are increasingly
facing the dual pressure of rising nonresponse and shrinking budgets. This pressure threatens the quality of
survey estimates.
 Survey methodologists have responded to these pressures by proposing two new classes of designs which
are aimed at increasing quality or controlling costs. The first new class of designs, known as responsive survey
designs, uses incoming data from the field to trigger changes in the design. In effect, these responsive designs
identify cases that are not responding well under the current protocol, and offering them a new protocol that is
more likely to induce response. The second class of designs is known as adaptive survey design. These
designs attempt to identify subgroups in the population for whom different designs may be more effective. The
goal is to identify the optimal design, with respect to a stated objective, that is tailored to individuals—i.e.,
assigns different designs to subgroups.
 Both classes of designs rely upon inputs for decision-making. Often, these inputs are in the form of model
predictions about the probability of response. Unfortunately, the quality of those inputs has not been evaluated
in either of these new classes of survey designs. We propose to evaluate the quality of these inputs. In
particular, we will evaluate the impact of model selection procedures on the effectiveness of responsive and
adaptive survey designs.
 We will use data from the largest ongoing survey in the U.S., the American Community Survey (ACS), to
accelerate progress on evaluating different approaches to informing the data collection design. The ACS is a
mandatory survey with a 95% response rate that also uses a phased design with multiple protocols, making it
ideal for this study. We will vary both the information being used to direct data collection at the sample case
level, and the primary objective of the targeted use of more costly methods.
 Our objectives are two-fold. First, we will evaluate the impact of model selection on the effectiveness of
using predictions from these models in order to inform design decisions aimed at achieving different survey
objectives. Second, we will evaluate the ability to achieve different survey objectives (bias reduction, MSE
minimization, and response rate maximization) via reallocation of effort.
 The results will help accelerate research to inform future study designs to improve survey estimates, and to
do so within limited resources.

## Key facts

- **NIH application ID:** 9973051
- **Project number:** 5R21AG062843-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** James R Wagner
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $229,539
- **Award type:** 5
- **Project period:** 2019-07-15 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9973051

## Citation

> US National Institutes of Health, RePORTER application 9973051, Improving the Efficiency of Model-Informed Decisionmaking in Responsive and Adaptive Survey Designs (5R21AG062843-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9973051. Licensed CC0.

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