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| 다변량 탐색적 양적 연구× | 탐색적 요인 분석 (EFA)× | |
|---|---|---|
| 분야≠ | 연구설계 | 통계학 |
| 계열≠ | Process / pipeline | Latent structure |
| 기원 연도≠ | 1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward | — |
| 창시자≠ | Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century) | — |
| 유형≠ | Quantitative research design | Latent variable / dimension reduction |
| 원전≠ | 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. DOI ↗ |
| 별칭≠ | multivariate exploratory design, exploratory multivariate analysis, multivariate data exploration, MEQ research | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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