方法对比
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| 多元探索性定量研究× | 探索性因子分析（EFA）× | |
|---|---|---|
| 领域≠ | 研究设计 | 统计学 |
| 方法族≠ | Process / pipeline | Latent structure |
| 起源年份≠ | 1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward | — |
| 提出者≠ | Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century) | — |
| 类型≠ | Quantitative research design | Latent variable / dimension reduction |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | multivariate exploratory design, exploratory multivariate analysis, multivariate data exploration, MEQ research | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. |
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