方法对比
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| 多变量因果比较研究× | 判别分析× | |
|---|---|---|
| 领域≠ | 研究设计 | 统计学 |
| 方法族≠ | Process / pipeline | Latent structure |
| 起源年份≠ | Mid-20th century onward; multivariate extension systematized 1970s–1990s | 1936 |
| 提出者≠ | Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian) | Ronald A. Fisher |
| 类型≠ | Quantitative non-experimental comparative design | Supervised classification and dimension reduction |
| 开创性文献≠ | 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 | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| 别名 | multivariate causal-comparative design, MANOVA causal-comparative study, multi-outcome ex post facto research, multivariate ex post facto design | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. | Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error. |
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