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طراحی رگرسیون ناپیوسته در پژوهش‌های آموزشی×تطابق امتیاز تمایل (Propensity Score Matching)×
حوزهاستنتاج علّیآمار پژوهش
خانوادهRegression modelProcess / pipeline
سال پیدایش1960 (origination); 1999-2010 (education economics canon)1983
پدیدآورThistlethwaite & Campbell (1960); popularized in education economics by Angrist & Lavy (1999), Lee & Lemieux (2010)Paul Rosenbaum and Donald Rubin
نوعQuasi-experimental causal inferenceMethod
منبع بنیادینLee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281-355. 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 ↗
نام‌های دیگرRDD in education, education RD design, sharp RDD education, score-cutoff designPSM, propensity score weighting, covariate balance
مرتبط53
خلاصهRegression discontinuity design (RDD) in education research exploits a score-based eligibility cutoff — such as a test score threshold, GPA requirement, or age cutoff — to estimate the causal effect of a program, intervention, or policy on student or school outcomes. Units just below and just above the cutoff are treated as near-randomly assigned, enabling credible causal inference without a randomized trial.Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.
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ScholarGateمقایسهٔ روش‌ها: Regression discontinuity design in education research · Propensity Score Matching. بازیابی‌شده در 2026-06-20 از https://scholargate.app/fa/compare