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| Analiza przerwanych szeregów czasowych (ITS)× | Badania podłużne× | |
|---|---|---|
| Dziedzina≠ | Wnioskowanie przyczynowe | Projektowanie badań |
| Rodzina≠ | Regression model | Process / pipeline |
| Rok powstania≠ | 2002 | Late 19th–early 20th century; methodologically codified through the 20th century |
| Twórca≠ | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Typ≠ | Quasi-experimental segmented regression | Quantitative (or mixed) observational research design |
| Źródło pierwotne≠ | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Inne nazwy≠ | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. | Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time. |
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