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| Sèrie temporal interrompuda en la recerca educativa× | Emparellament per puntuació de propensió× | |
|---|---|---|
| Camp≠ | Inferència causal | Estadística per a la recerca |
| Família≠ | Regression model | Process / pipeline |
| Any d'origen≠ | 1979-2002 | 1983 |
| Autor original≠ | Shadish, Cook & Campbell (quasi-experimental design); Wagner et al. (segmented regression formalization) | Paul Rosenbaum and Donald Rubin |
| Tipus≠ | Quasi-experimental causal inference | Method |
| Font seminal≠ | Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560 | 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 ↗ |
| Àlies≠ | ITS in education, educational ITS, segmented regression in education, policy interrupted time series | PSM, propensity score weighting, covariate balance |
| Relacionats≠ | 4 | 3 |
| Resum≠ | Interrupted time series (ITS) analysis is a quasi-experimental design that estimates the causal effect of an education policy or intervention by examining whether an outcome trend changes abruptly at the point of implementation. Applied to education, it is used to evaluate reforms, curriculum changes, testing policies, and school interventions using routinely collected longitudinal data without a randomised control group. | 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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