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| Laboratoriumsexperiment× | Experimentelles Design mit Kontrollgruppe× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 17th century (natural science); ~1879 onward (behavioral/social science) | 1935 (Fisher); 1963 (Campbell & Stanley codification) |
| Urheber≠ | Francis Bacon, Robert Boyle (early scientific method); formalized in social science by Wilhelm Wundt (1879 psychology lab) and Ronald A. Fisher (20th-century design principles) | Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley |
| Typ≠ | Experimental quantitative design | Experimental research design |
| Wegweisende Quelle≠ | Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560 | Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally. link ↗ |
| Aliasnamen | lab experiment, controlled experiment, true experiment, lab study | controlled experiment, true experimental design, randomized controlled design, treatment-control design |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | A laboratory experiment is a research design in which the investigator systematically manipulates one or more independent variables under tightly controlled conditions, randomly assigns participants to conditions, and measures the effect on dependent variables. By maximizing internal control, the laboratory experiment is the gold standard for establishing cause-and-effect relationships. It is the backbone of experimental psychology, cognitive science, pharmacology, and many social sciences. | Control group experimental design is a fundamental experimental structure in which participants are assigned to at least two groups — a treatment group that receives the intervention and a control group that does not — so that the effect of the intervention can be isolated by comparing outcomes across groups. Randomisation of assignment strengthens causal inference by balancing known and unknown confounders. |
| ScholarGateDatensatz ↗ |
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