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| 강건 탐색적 요인 분석× | 확인적 요인 분석 (CFA)× | |
|---|---|---|
| 분야 | 심리측정학 | 심리측정학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 2000–2003 | 1969 |
| 창시자≠ | Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams) | Karl Gustav Jöreskog |
| 유형≠ | Latent variable / dimension reduction (robust) | Hypothesis-testing latent variable model |
| 원전≠ | Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58. DOI ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| 별칭 | robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation | CFA, confirmatory FA, measurement model, restricted factor analysis |
| 관련 | 4 | 4 |
| 요약≠ | Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings. | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. |
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