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| Hồi quy Bayes× | Phân tích nhân tố khẳng định (CFA)× | Mô hình đường cong tăng trưởng tiềm ẩn (LGC)× | |
|---|---|---|---|
| Lĩnh vực≠ | Bayes | Thống kê | Thống kê |
| Họ≠ | Bayesian methods | Latent structure | Latent structure |
| Năm ra đời≠ | — | 1969 | 1990 |
| Người khởi xướng≠ | — | Karl Jöreskog | Meredith & Tisak |
| Loại≠ | Bayesian linear model | Confirmatory latent variable model | Latent variable / longitudinal growth model |
| Công trình gốc≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122. DOI ↗ |
| Tên gọi khác≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli |
| Liên quan≠ | 2 | 4 | 5 |
| Tóm tắt≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships. | The latent growth curve model is a structural equation modelling approach introduced by Meredith and Tisak (1990) for analysing change over time. It treats each individual's starting point (intercept) and rate of change (slope) as latent variables, simultaneously estimating the average trajectory across the sample and the extent to which individuals differ in their own trajectories. |
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