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| 베이지안 탐색적 요인 분석 (Bayesian Exploratory Factor Analysis, BEFA)× | 탐색적 요인 분석 (EFA)× | |
|---|---|---|
| 분야≠ | 심리측정학 | 통계학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 2004 (Bayesian formulation); factor analysis roots: 1904 | — |
| 창시자≠ | Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904) | — |
| 유형≠ | Probabilistic latent variable model | Latent variable / dimension reduction |
| 원전≠ | Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67. link ↗ | 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≠ | Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 관련 | 4 | 4 |
| 요약≠ | Bayesian exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factors as an unknown to be inferred from data. | 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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