Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Longitudinaal causaal-vergelijkend onderzoek× | Ex Post Facto Ontwerp× | |
|---|---|---|
| Vakgebied | Onderzoeksontwerp | Onderzoeksontwerp |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1970s–1980s (as an established combined design in educational and social research) | 1960s (systematic codification); concept used in social science from early 20th century |
| Grondlegger≠ | Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979) | Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley |
| Type | Non-experimental quantitative research design | Non-experimental quantitative research design |
| Oorspronkelijke bron≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill. ISBN: 978-0073525532 | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Aliassen | longitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study | after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design |
| Verwant≠ | 4 | 3 |
| Samenvatting≠ | Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally. | 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. |
| ScholarGateGegevensset ↗ |
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