Módszerek összehasonlítása

	Multivariáns kauzális-komparatív kutatás	Diszkriminancia-analízis
Tudományterület	Kutatástervezés	Statisztika
Módszercsalád	Process / pipeline	Latent structure
Keletkezés éve	Mid-20th century onward; multivariate extension systematized 1970s–1990s	1936
Megalkotó	Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian)	Ronald A. Fisher
Típus	Quantitative non-experimental comparative design	Supervised classification and dimension reduction
Alapmű	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.
Alternatív nevek	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
Kapcsolódó	6	4
Összefoglaló	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.