Methoden vergleichen

	Multivariate explorative quantitative Forschung	Exploratorische Faktorenanalyse (EFA)
Fachgebiet	Forschungsdesign	Statistik
Familie	Process / pipeline	Latent structure
Entstehungsjahr	1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward	—
Urheber	Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century)	—
Typ	Quantitative research design	Latent variable / dimension reduction
Wegweisende Quelle	Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
Aliasnamen	multivariate exploratory design, exploratory multivariate analysis, multivariate data exploration, MEQ research	common factor analysis, açımlayıcı faktör analizi, factor analysis
Verwandt	5	4
Zusammenfassung	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.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.