Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Propensity Score Matching in Education Research× | Uzito wa Alama ya Mwelekeo (PSW / IPW)× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1983 (foundational); education adoption widespread from late 1990s | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Mwanzilishi≠ | Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Aina≠ | Quasi-experimental / matching-based causal inference | Causal inference / reweighting |
| Chanzo asilia | 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 ↗ | 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 ↗ |
| Majina mbadala | PSM in education, educational PSM, PSM for program evaluation in schools, propensity matching education | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | Propensity Score Matching (PSM) in education research is a quasi-experimental technique that creates comparable treatment and control groups from observational student, teacher, or school data. By balancing groups on observed background characteristics, it enables credible causal estimates of educational interventions — such as tutoring programs, school choice policies, or teacher professional development — when random assignment is infeasible. | 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). |
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