Сравнение методов

	Многомерное исследовательское количественное исследование	Эксплораторный факторный анализ (ЭФА)
Область	Дизайн исследования	Статистика
Семейство	Process / pipeline	Latent structure
Год появления	1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward	—
Автор метода	Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century)	—
Тип	Quantitative research design	Latent variable / dimension reduction
Основополагающий источник	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.
Другие названия	multivariate exploratory design, exploratory multivariate analysis, multivariate data exploration, MEQ research	common factor analysis, açımlayıcı faktör analizi, factor analysis
Связанные	5	4
Сводка	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.