Data collection and estimation with complex populations |
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| Coordinator 1 | Dr Mariel Leonard (DIW-Berlin) |
| Coordinator 2 | Dr Carina Cornesse (DIW-Berlin) |
Research shows that survey samples should be constructed using probability sampling approaches to allow valid inference to the intended target population. However, for many populations of interest high-quality probability sampling frames do not exist. This is particularly true for complex populations, including both elite and marginalized or hidden populations.
In the absence of sampling frames, researchers are faced with the choice to discard their research questions or to try to draw inferences from nonprobability and other less conventional samples. For the latter, both model-based and design-based solutions have been proposed in recent years. This session focuses on data collection and analysis techniques designed to result in samples that approximate probability samples as well as robust or accurate estimates. In using or combining such approaches, researchers usually face trade-offs. For example, increasing diversity of an existing probability sample with approaches designed to survey marginalized groups may increase diversity of the sample but also the complexity of analyzing the data.
The session scope covers but is not limited to research on studying and increasing representation of hard-to-reach and hard-to-survey populations, such as highly mobile, elusive, or stigmatized populations. We are particularly interested in practical experiences and lessons learned on topics such as:
- Respondent-driven sampling (RDS) and other network sampling techniques
- Time-space or location sampling (e.g. for travelers or homeless people)
- Quasi-experimental research designs (including blended approaches to combining probability and nonprobability approaches)
- Trade-offs between maximizing sample diversity and/or conceptual coverage vs. population coverage