Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Design experimental cu grup de control, simplu mascat× | Design experimental factorial cu grup de control× | |
|---|---|---|
| Domeniu | Design experimental | Design experimental |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | Mid-20th century (blinding standards consolidated ~1950s–1970s) | 1926–1935 |
| Autorul original≠ | Classical experimental tradition; blinding formalized in 20th-century clinical trial methodology | Ronald A. Fisher |
| Tip≠ | Controlled experimental design | Experimental design |
| Sursa seminală≠ | Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Denumiri alternative | single-masked controlled experiment, single-blind controlled trial, SB-CGD, single-blind parallel-group design | factorial controlled experiment, factorial design with control, factorial RCT with control arm, multi-factor controlled experiment |
| Înrudite | 6 | 6 |
| Rezumat≠ | A single-blind control group experimental design is a controlled experiment in which participants are kept unaware of whether they are receiving the active treatment or a control condition, while researchers and outcome assessors remain unmasked. The design uses a designated control group as the baseline for comparison, allowing causal inference about the treatment effect while limiting participant-driven response biases such as the placebo effect and demand characteristics. | A factorial control group experimental design crosses two or more independent variables (factors) in a fully factorial structure while including at least one condition that serves as a no-treatment or standard-treatment control. This allows researchers to simultaneously estimate the main effect of each factor, their interactions, and the size of those effects relative to a meaningful baseline, maximising both causal precision and experimental efficiency. |
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