Approximating Probability Samples in the Absence of Sampling Frames 1 |
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| Coordinator 1 | Dr Carina Cornesse (German Institute for Economic Research) |
| Coordinator 2 | Dr Mariel McKone Leonard (DeZIM Institute) |
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 marginalized and hidden populations, including ethnic, religious, and sexual minorities. 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 techniques designed to result in samples that approximate probability samples. We also invite proposals on techniques for approximating probability samples using already collected nonprobability sample data as well as by combining probability and nonprobability sample data for drawing inferences. The session scope covers but is not limited to research on hard-to-reach and hard-to-survey populations. We are particularly interested in methodological research on techniques such as
- Respondent-driven sampling (RDS) & other network sampling techniques
- Quasi-experimental research designs
- Weighting approaches for nonprobability data (especially those that make use of probability sample reference survey data)
- Techniques for combining probability and nonprobability samples (e.g. blended calibration)