Sampling Low-Incidence Populations Under Anticipated Nonresponse

Music City Center 

Survey sampling theory on optimal allocation typically assumes 100% response rates. This has led sample designers to resort to ad hoc practices for accommodating anticipated nonresponse, such as computing classic allocations under complete response and then adjusting for anticipated sample loss. In a previous paper (2024), we showed that standard practices may perform quite poorly in some situations. For instance, in an application with a large degree of differential nonresponse, our proposed allocation increased the effective sample size by 25% relative to standard practices.

Here, we extend our previous paper, which assumed that all members of the frame are eligible population members, to situations where eligibility is not known upfront. For instance, it can be challenging to survey low-incidence populations, where population membership is not known in the frame, although auxiliary data are often available for constructing strata with different concentrations (eligibility rates) of the target population. We provide new theory on optimal allocation for low-incidence populations under anticipated nonresponse. We treat eligibility through an analogy to domain estimation, but in contrast with previous theory on sampling for rare populations, nonresponse is included in our formulation. We provide theoretical results and will compare our allocation with existing approaches through an application.

Sampling

Sample design

Sample allocation

Nonresponse

Rare populations