Comparer des méthodes

	Recherche quantitative exploratoire multivariée	Analyse de regroupement
Domaine	Conception de la recherche	Statistique
Famille	Process / pipeline	Latent structure
Année d'origine	1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward	1939–1967
Auteur d'origine	Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century)	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
Type	Quantitative research design	Unsupervised classification / grouping
Source fondatrice	Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
Alias	multivariate exploratory design, exploratory multivariate analysis, multivariate data exploration, MEQ research	clustering, unsupervised classification, data clustering, numerical taxonomy
Apparentées	5	5
Résumé	Multivariate exploratory quantitative research is a design in which researchers simultaneously examine multiple quantitative variables without imposing a predetermined structural model, using techniques such as exploratory factor analysis, cluster analysis, or principal component analysis to detect latent patterns, natural groupings, or underlying dimensions in the data. The goal is discovery and pattern recognition rather than hypothesis confirmation.	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.