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Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Σταθμισμένη Δειγματοληψία Διαδικτύου× | Στάθμιση Βαθμολογίας Προδιάθεσης (PSW / IPW)× | |
|---|---|---|
| Πεδίο≠ | Μεθοδολογία Επισκοπήσεων | Αιτιακή Συμπερασματολογία |
| Οικογένεια≠ | Process / pipeline | Regression model |
| Έτος προέλευσης≠ | Late 1990s–2000s | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Δημιουργός≠ | Survey methodology practitioners; systematized via probability-based online panels (e.g., Knowledge Networks, founded late 1990s) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Τύπος≠ | Probability-adjusted online sampling technique | Causal inference / reweighting |
| Θεμελιώδης πηγή≠ | 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 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗ |
| Εναλλακτικές ονομασίες | web-based weighted sampling, internet survey weighting, online panel weighting, weighted internet sampling | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Συναφείς≠ | 4 | 6 |
| Σύνοψη≠ | 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. | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). |
| ScholarGateΣύνολο δεδομένων ↗ |
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