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| 다변량 인과-비교 연구× | 다변량 분산 분석 (MANOVA)× | |
|---|---|---|
| 분야≠ | 연구설계 | 통계학 |
| 계열≠ | Process / pipeline | Hypothesis test |
| 기원 연도≠ | Mid-20th century onward; multivariate extension systematized 1970s–1990s | 1932 |
| 창시자≠ | Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian) | Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.) |
| 유형≠ | Quantitative non-experimental comparative design | Parametric multivariate mean comparison |
| 원전≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260085594 | Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574 |
| 별칭≠ | multivariate causal-comparative design, MANOVA causal-comparative study, multi-outcome ex post facto research, multivariate ex post facto design | Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA) |
| 관련≠ | 6 | 5 |
| 요약≠ | Multivariate causal-comparative research is a quantitative, non-experimental design that investigates whether pre-existing group differences (defined by a naturally occurring categorical variable) are associated with differences across multiple outcome variables considered simultaneously. By extending the classic causal-comparative framework to several dependent variables at once, it reduces Type I error inflation and captures the correlated structure of outcomes that univariate comparisons would miss. | MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root — were developed between the 1930s and 1950s, with Wilks' Lambda formalised by Samuel Stanley Wilks in 1932. |
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