Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vienas akles pirms- un pēc-testa eksperimentālais dizains× | Kontrolgrupas eksperimentālais dizains× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1963 (systematic codification); blinding in use from early 20th century | 1935 (Fisher); 1963 (Campbell & Stanley codification) |
| Autors≠ | Campbell & Stanley (codified); blinding practice has earlier roots in clinical research | Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley |
| Tips≠ | Controlled experimental design with partial blinding | Experimental research design |
| Pirmavots | Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally. link ↗ | Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally. link ↗ |
| Citi nosaukumi | single-masked pretest-posttest design, participant-blind pretest-posttest, single-blind before-after design, SB-PP design | controlled experiment, true experimental design, randomized controlled design, treatment-control design |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | The single-blind pretest-posttest experimental design combines two protective strategies: measuring outcomes both before and after treatment to quantify change, and keeping participants unaware of which condition they are in. This pairing controls for preexisting group differences and expectancy-driven response bias, making it a practical middle ground between fully open-label and double-blind trials in behavioral and health research. | 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. |
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