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| Campionamento Pesato Online× | Campionamento per quote× | |
|---|---|---|
| Campo | Metodologia delle indagini | Metodologia delle indagini |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | Late 1990s–2000s | 1930s |
| Ideatore≠ | Survey methodology practitioners; systematized via probability-based online panels (e.g., Knowledge Networks, founded late 1990s) | Developed in market research and opinion polling, notably applied by George Gallup in the 1930s |
| Tipo≠ | Probability-adjusted online sampling technique | Non-probability sampling design |
| Fonte seminale≠ | 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 | Moser, C. A., & Kalton, G. (1972). Survey Methods in Social Investigation (2nd ed.). Heinemann. ISBN: 978-0435827496 |
| Alias≠ | web-based weighted sampling, internet survey weighting, online panel weighting, weighted internet sampling | quota-controlled sampling, quota selection, non-probability quota sampling |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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. | Quota sampling is a non-probability technique in which the researcher pre-specifies how many units to recruit from each subgroup (quota cell) defined by one or more control variables such as age, gender, or occupation. Interviewers or data collectors then use their own judgment to find and enroll participants until each cell is filled. The method guarantees the sample mirrors the population on the control variables but does not provide the randomness needed for classical statistical inference. |
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