Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 285 - New Advances in Sample Design and Adjusting for Survey Nonresponse
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
Sponsor: Survey Research Methods Section
Abstract #318676
Title: Designing and Implementing Multi-Wave Sampling Surveys in R
Author(s): Jasper Yang* and Bryan Shepherd and Thomas Lumley and Pamela Shaw
Companies: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania and Department of Biostatistics, Vanderbilt University School of Medicine and University of Auckland and Kaiser Permanente Washington Health Research Institute
Keywords: multi-wave sampling; Neyman allocation; optimal design; R

Stratified random sampling techniques are often employed to obtain more precise estimates of population characteristics, but efficiently allocating samples to strata is difficult because the optimal design relies on the specification of unknown parameters. Adaptive, multi-wave designs are particularly useful in these cases because estimates for the necessary parameters are obtained iteratively as the expensive variables are collected. We motivate and illustrate the multi-wave sampling design. Unlike simpler sampling schemes, executing multi-wave designs requires careful management of many moving parts over repetitive steps, which can be cumbersome and error-prone. Using real-life epidemiological study examples, we demonstrate an efficient workflow for the design and implementation of multi-wave surveys in R. This workflow is facilitated by the ‘optimall’ package, which offers functions for defining strata, optimum allocation, selecting samples, and organizing the various pieces of a multi-wave survey. Although tailored towards multi-wave sampling under two- or three-phase designs, the R package ‘optimall’ may be useful for any sampling survey.

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

Back to the full JSM 2021 program