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| Phân tích Cronbach's Alpha (Độ tin cậy)× | Phân tích nhân tố khám phá (EFA)× | Phân tích thành phần chính× | |
|---|---|---|---|
| Lĩnh vực≠ | Thống kê | Thống kê | Học máy |
| Họ≠ | Latent structure | Latent structure | Machine learning |
| Năm ra đời≠ | 1951 | — | 2002 |
| Người khởi xướng≠ | Lee J. Cronbach | — | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Loại≠ | Reliability / internal consistency coefficient | Latent variable / dimension reduction | Unsupervised dimensionality reduction |
| Công trình gốc≠ | Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. DOI ↗ | 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 ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Tên gọi khác≠ | coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha) | common factor analysis, açımlayıcı faktör analizi, factor analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Liên quan≠ | 4 | 4 | 3 |
| Tóm tắt≠ | Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research. | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
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