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| Phân tích nhân tố khám phá (EFA)× | Phân tích nhân tố khẳng định (Confirmatory Factor Analysis - CFA)× | Mô hình phương trình cấu trúc (SEM)× | |
|---|---|---|---|
| Lĩnh vực≠ | Thống kê | Trắc lượng tâm lý | Thống kê |
| Họ | Latent structure | Latent structure | Latent structure |
| Năm ra đời≠ | — | 1969 | 1970 |
| Người khởi xướng≠ | — | Karl Gustav Jöreskog | Karl Jöreskog (LISREL framework, 1970s) |
| Loại≠ | Latent variable / dimension reduction | Hypothesis-testing latent variable model | Latent variable / causal modeling |
| Công trình gốc≠ | 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 ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ | Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 |
| Tên gọi khác≠ | common factor analysis, açımlayıcı faktör analizi, factor analysis | CFA, confirmatory FA, measurement model, restricted factor analysis | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling |
| Liên quan≠ | 4 | 4 | 5 |
| Tóm tắt≠ | 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. | 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. | Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences. |
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