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| Online Weighted Sampling× | Vægtet stikprøveudtagning× | |
|---|---|---|
| Fagområde | Surveymetodologi | Surveymetodologi |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | Late 1990s–2000s | 1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator) |
| Ophavsperson≠ | Survey methodology practitioners; systematized via probability-based online panels (e.g., Knowledge Networks, founded late 1990s) | Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework) |
| Type≠ | Probability-adjusted online sampling technique | Probability sampling design |
| Oprindelig kilde≠ | Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley. ISBN: 978-1118456149 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Aliasser | web-based weighted sampling, internet survey weighting, online panel weighting, weighted internet sampling | probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling |
| Relaterede≠ | 4 | 6 |
| Resumé≠ | Online weighted sampling is the practice of recruiting respondents via internet platforms and then applying statistical weights to correct for unequal selection probabilities, coverage gaps, and differential non-response. It enables researchers to draw valid population inferences from web surveys by compensating for the structural biases inherent in online recruitment — including the fact that not all members of a target population have equal internet access or equal likelihood of joining a panel. | Weighted sampling is a probability-based design in which units are selected with unequal probabilities proportional to a known auxiliary measure of size or importance. Sampling weights — the inverse of inclusion probabilities — are applied during analysis so that each sampled unit correctly represents the population units it stands for. The approach underpins large-scale government, health, and social surveys where simple random sampling would be inefficient. |
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