Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Persampelan Berwajaran Dalam Talian× | Penimbang Skor Kecenderungan (PSW / IPW)× | |
|---|---|---|
| Bidang≠ | Metodologi Tinjauan | Inferens Kausal |
| Keluarga≠ | Process / pipeline | Regression model |
| Tahun asal≠ | Late 1990s–2000s | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Pengasas≠ | 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) |
| Jenis≠ | Probability-adjusted online sampling technique | Causal inference / reweighting |
| Sumber perintis≠ | 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 ↗ |
| Alias | web-based weighted sampling, internet survey weighting, online panel weighting, weighted internet sampling | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Berkaitan≠ | 4 | 6 |
| Ringkasan≠ | 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). |
| ScholarGateSet data ↗ |
|
|