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| 정렬 순위 변환 분산 분석 (ART-ANOVA)× | 이원 분산 분석 (Two-Way ANOVA)× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 2011 | 1925 |
| 창시자≠ | Wobbrock, Findlater, Gergle & Higgins | Ronald A. Fisher |
| 유형≠ | Nonparametric factorial hypothesis test | Parametric factorial mean comparison |
| 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 ↗ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 |
| 별칭≠ | ART-ANOVA, aligned ranks ANOVA, nonparametric factorial ANOVA, Hizalanmış Sıra Dönüşümü ANOVA (ART-ANOVA) | factorial ANOVA, two-factor ANOVA, İki Yönlü ANOVA |
| 관련≠ | 7 | 6 |
| 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. | Two-Way ANOVA is a parametric hypothesis test that simultaneously examines the main effects of two independent categorical factors and their interaction effect on a single continuous dependent variable. The technique was developed within the broader framework of the analysis of variance established by Ronald A. Fisher in 1925 and remains the standard approach whenever an experiment or survey includes exactly two between-subjects factors. |
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