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| 탐색적 요인 분석 (EFA)× | 다수준 모형× | |
|---|---|---|
| 분야≠ | 통계학 | 연구 통계 |
| 계열≠ | Latent structure | Process / pipeline |
| 기원 연도≠ | — | 1992 |
| 창시자≠ | — | Anthony Bryk and Stephen Raudenbush |
| 유형≠ | Latent variable / dimension reduction | Method |
| 원전≠ | 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 ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 별칭≠ | common factor analysis, açımlayıcı faktör analizi, factor analysis | HLM, mixed-effects models, random effects models, MLM |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
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