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| Phân tích Chức năng Mục khác biệt Bayes (Bayesian DIF)× | Phân tích nhân tố khẳng định (Confirmatory Factor Analysis - CFA)× | |
|---|---|---|
| Lĩnh vực | Trắc lượng tâm lý | Trắc lượng tâm lý |
| Họ | Latent structure | Latent structure |
| Năm ra đời≠ | 1990s–2000s | 1969 |
| Người khởi xướng≠ | H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s | Karl Gustav Jöreskog |
| Loại≠ | Item bias detection / Bayesian inference | Hypothesis-testing latent variable model |
| Công trình gốc≠ | Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370. DOI ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Tên gọi khác | Bayesian DIF, Bayesian DIF analysis, Bayesian item bias detection, BDIF | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Liên quan≠ | 5 | 4 |
| Tóm tắt≠ | Bayesian differential item functioning analysis detects whether a test item behaves differently across demographic or cultural groups — such as males vs. females — after accounting for the underlying ability or trait being measured. It applies Bayesian IRT estimation to obtain posterior distributions of item parameters separately per group, then evaluates group differences with posterior credibility intervals or Bayes factors rather than classical p-values. | 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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