Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Кластерний рандомізований лабораторний експеримент× | Факторний лабораторний експеримент× | |
|---|---|---|
| Галузь | Планування експерименту | Планування експерименту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1990s (formalized; cluster randomization principles developed in 1970s-1980s) | 1926 (Fisher's factorial principle); laboratory application systematized mid-20th century |
| Автор методу≠ | David M. Murray (group-randomized trial methodology); built on classical cluster sampling in experimental design | Ronald A. Fisher |
| Тип≠ | Controlled laboratory experiment with cluster-level randomization | Experimental research design |
| Основоположне джерело≠ | Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120363 | Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Sage Publications. ISBN: 978-1412974455 |
| Інші назви | cluster-randomized lab experiment, group-randomized laboratory study, cluster RCT laboratory variant, clustered lab trial | factorial lab experiment, laboratory factorial design, factorial controlled experiment, multi-factor lab study |
| Пов'язані≠ | 6 | 2 |
| Підсумок≠ | A cluster randomized laboratory experiment assigns intact groups — such as lab sections, cohorts, or naturally formed teams — rather than individual participants, to experimental conditions. All participants within a cluster receive the same treatment. The design is used when individual randomization would cause contamination between conditions, while retaining the controlled environment of a laboratory setting. | A factorial laboratory experiment is a controlled experimental design in which two or more independent variables (factors) are simultaneously manipulated, each at two or more levels, within a laboratory setting. This design allows researchers to estimate both the individual main effect of each factor and the interaction effects between factors — making it one of the most efficient and informative designs in behavioral, psychological, and natural science research. |
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