방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 정렬 순위 변환 분산 분석 (ART-ANOVA)× | 전요인 실험 설계× | |
|---|---|---|
| 분야≠ | 통계학 | 실험설계 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 2011 | 1926 |
| 창시자≠ | Wobbrock, Findlater, Gergle & Higgins | R. A. Fisher |
| 유형≠ | Nonparametric factorial hypothesis test | Parametric factorial experiment |
| 원전≠ | Wobbrock, J. O., Findlater, L., Gergle, D., & Higgins, J. J. (2011). The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2011), 143–146. DOI ↗ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| 별칭 | ART-ANOVA, aligned ranks ANOVA, nonparametric factorial ANOVA, Hizalanmış Sıra Dönüşümü ANOVA (ART-ANOVA) | factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k) |
| 관련≠ | 7 | 5 |
| 요약≠ | The Aligned Rank Transform ANOVA (ART-ANOVA) is a nonparametric factorial hypothesis test that detects main effects and interactions in designs with two or more independent variables, without requiring normality. The procedure was formalized by Wobbrock, Findlater, Gergle, and Higgins in their 2011 CHI paper and operates by separately aligning each effect before ranking, so that standard ANOVA machinery can be applied to nonparametric data. | A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured. |
| ScholarGate데이터셋 ↗ |
|
|