Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Évaluation longitudinale de programme× | Analyse de séries chronologiques interrompues (ITS)× | |
|---|---|---|
| Domaine≠ | Méthodes de terrain | Inférence causale |
| Famille≠ | Process / pipeline | Regression model |
| Année d'origine≠ | 1960s–1970s (program evaluation); longitudinal designs formalized 1970s–1980s | 2002 |
| Auteur d'origine≠ | Peter Rossi, Michael Scriven, Donald Campbell (program evaluation tradition) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Type≠ | Applied evaluation research design | Quasi-experimental segmented regression |
| Source fondatrice≠ | Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage Publications. ISBN: 978-0761908944 | 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 ↗ |
| Alias≠ | LPE, longitudinal evaluation, long-term program evaluation, prospective program evaluation | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Apparentées≠ | 3 | 5 |
| Résumé≠ | Longitudinal program evaluation is an applied research design that tracks the outcomes and processes of a program or intervention across multiple time points — from pre-implementation baseline through medium- and long-term follow-up. Unlike single-point evaluations, it captures how program effects emerge, fade, or evolve over time, enabling evaluators and funders to judge sustained impact, cost-effectiveness, and unintended consequences that would be invisible in a snapshot assessment. | 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. |
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