เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การจับคู่คะแนนแนวโน้มในการวิจัยทางการศึกษา× | การถ่วงน้ำหนักด้วยคะแนนแนวโน้ม (Propensity Score Weighting - PSW / IPW)× | |
|---|---|---|
| สาขาวิชา | การอนุมานเชิงสาเหตุ | การอนุมานเชิงสาเหตุ |
| ตระกูล | Regression model | Regression model |
| ปีกำเนิด≠ | 1983 (foundational); education adoption widespread from late 1990s | 1983 (propensity score); 2003 (efficient IPW estimator) |
| ผู้ริเริ่ม≠ | Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| ประเภท≠ | Quasi-experimental / matching-based causal inference | Causal inference / reweighting |
| แหล่งต้นตำรับ | 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 ↗ |
| ชื่อเรียกอื่น | PSM in education, educational PSM, PSM for program evaluation in schools, propensity matching education | PSW, inverse probability weighting, IPW, propensity-based weighting |
| ที่เกี่ยวข้อง≠ | 5 | 6 |
| สรุป≠ | 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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