方法对比
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| 探索性因子分析（EFA）× | 分层线性模型 (HLM / 多层模型)× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族≠ | Latent structure | Hypothesis test |
| 起源年份≠ | — | 1986 |
| 提出者≠ | — | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| 类型≠ | Latent variable / dimension reduction | Parametric nested-data regression |
| 开创性文献≠ | 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 ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| 别名≠ | common factor analysis, açımlayıcı faktör analizi, factor analysis | HLM, MLM, multilevel modeling, multilevel analysis |
| 相关 | 4 | 4 |
| 摘要≠ | 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. | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. |
| ScholarGate数据集 ↗ |
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