Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Multivariate Causale-Vergelijkende Onderzoeksopzet× | Ex Post Facto Ontwerp× | |
|---|---|---|
| Vakgebied | Onderzoeksontwerp | Onderzoeksontwerp |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | Mid-20th century onward; multivariate extension systematized 1970s–1990s | 1960s (systematic codification); concept used in social science from early 20th century |
| Grondlegger≠ | Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian) | Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley |
| Type≠ | Quantitative non-experimental comparative design | Non-experimental quantitative research design |
| Oorspronkelijke bron≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260085594 | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Aliassen | multivariate causal-comparative design, MANOVA causal-comparative study, multi-outcome ex post facto research, multivariate ex post facto design | after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design |
| Verwant≠ | 6 | 3 |
| Samenvatting≠ | Multivariate causal-comparative research is a quantitative, non-experimental design that investigates whether pre-existing group differences (defined by a naturally occurring categorical variable) are associated with differences across multiple outcome variables considered simultaneously. By extending the classic causal-comparative framework to several dependent variables at once, it reduces Type I error inflation and captures the correlated structure of outcomes that univariate comparisons would miss. | 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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