方法对比
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| 稳健的验证性因子分析× | 验证性因子分析（CFA）× | |
|---|---|---|
| 领域≠ | 统计学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1984–1994 | 1969 |
| 提出者≠ | Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator) | Karl Gustav Jöreskog |
| 类型≠ | Confirmatory latent variable model with robust estimation | Hypothesis-testing latent variable model |
| 开创性文献≠ | Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage. link ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| 别名 | Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA | CFA, confirmatory FA, measurement model, restricted factor analysis |
| 相关≠ | 6 | 4 |
| 摘要≠ | Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable. | 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. |
| ScholarGate数据集 ↗ |
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