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| Klusterirandomisoitu A/B-testi× | Faktoriaalinen A/B-testi× | |
|---|---|---|
| Tieteenala | Koesuunnittelu | Koesuunnittelu |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 2010s (digital platforms); cluster RCT roots date to the 1970s–1980s | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s |
| Kehittäjä≠ | Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s) | Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s |
| Tyyppi≠ | Experimental design | Controlled online/field experiment |
| Alkuperäislähde≠ | Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013). Graph cluster randomization: Network exposure to multiple universes. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 329–337. DOI ↗ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 |
| Rinnakkaisnimet | cluster A/B test, group-randomized A/B test, network A/B test, cluster-level split test | factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | A cluster randomized A/B test is an experimental design in which intact groups (clusters) — such as cities, schools, social network communities, or app user segments — are randomly assigned as whole units to either the treatment (A) or control (B) condition, rather than randomizing individual users or subjects. This approach is used when treatment effects would spill over between individuals if individual-level randomization were applied, or when the intervention must be delivered at the group level. | A factorial A/B test is a controlled online experiment that simultaneously manipulates two or more independent factors, each at two or more levels, exposing different user groups to every combination of factor levels. Rooted in Fisher's factorial design and operationalised at scale by tech companies, it enables researchers to estimate both the independent main effect of each factor and the interaction effects between factors — all from a single experimental run. |
| ScholarGateAineisto ↗ |
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