Really Random? Exploring Bias and Quality in Random Sampling |
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| Coordinator 1 | Dr Lydia Repke (GESIS - Leibniz Institute for the Social Sciences) |
| Coordinator 2 | Dr Barbara Felderer (GESIS - Leibniz Institute for the Social Sciences) |
Random sampling is considered the gold standard in survey research for building population inferences. Extensive research has been conducted to develop sampling strategies that ensure a fully random sample, where the selection probability of each individual in the population is known. However, even random sampling methods can sometimes result in unintended side effects that may bias survey estimates.
For example, the frequently used random route procedure has been shown not to consistently produce equal selection probabilities across households. Similarly, the last/first birthday method for sampling individuals within a household often results in a non-uniform distribution of birth months at the sample level. This can skew survey estimates if the birth months are correlated with the variables of interest.
Random sampling is often carried out by field agencies, leaving survey organizations with little control over the sampling process. Even with strictly random sampling designs, implementation errors may occur, resulting in samples that are not fully random. Currently, there are no well-established methods to assess the quality of (random) samples. However, if the target population is known, indicators can be derived by comparing the net sample to the general population using information such as the distribution of surnames and gender.
We welcome papers on topics including, but not limited to:
1. The relationship between randomization mechanisms and variables of interest,
2. The detection of failed randomization, and
3. The development of quality indicators for successful random sampling.