Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Pesquisa Causal-Comparativa Transversal× | Design Ex Post Facto× | |
|---|---|---|
| Área | Delineamento de pesquisa | Delineamento de pesquisa |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1960s onward | 1960s (systematic codification); concept used in social science from early 20th century |
| Autor original≠ | Donald T. Campbell and Julian C. Stanley (quasi-experimental foundations); refined in education research by various methodologists | Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley |
| Tipo≠ | Non-experimental quantitative design | Non-experimental quantitative research design |
| Fonte seminal≠ | Frankfort-Nachmias, C., & Nachmias, D. (2015). Research Methods in the Social Sciences (8th ed.). Worth Publishers. ISBN: 978-1429295154 | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Outros nomes | cross-sectional ex post facto design, single-wave causal-comparative study, cross-sectional group-comparison design, cross-sectional criterion-group study | after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design |
| Relacionados | 3 | 3 |
| Resumo≠ | Cross-sectional causal-comparative research compares two or more pre-existing groups — defined by a characteristic or experience that has already occurred — on one or more outcome variables, with all data collected at a single point in time. Because the presumed cause (group membership) precedes measurement but cannot be manipulated, the design sits between purely descriptive and truly experimental work. It is widely used in education, psychology, and social sciences when randomization is impossible or unethical. | Ex post facto design is a non-experimental quantitative research approach in which the researcher investigates a phenomenon after it has already occurred, examining pre-existing differences between groups to explore potential causal or associative relationships. Because the independent variable cannot be manipulated — it happened in the past — the design relies on careful group selection, retrospective data collection, and statistical controls to approximate causal inference without experimental intervention. |
| ScholarGateConjunto de dados ↗ |
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