方法对比
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| 贝叶斯量表开发× | 探索性因子分析（EFA）× | |
|---|---|---|
| 领域≠ | 心理测量学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1990s–2000s | — |
| 提出者≠ | Harold Jeffreys, expanded into psychometrics by Mislevy and colleagues | — |
| 类型≠ | Bayesian probabilistic scale construction | Latent variable / dimension reduction |
| 开创性文献≠ | De Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Guilford Press. ISBN: 978-1593858698 | 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 ↗ |
| 别名≠ | Bayesian psychometric scale construction, Bayesian measurement modeling, Bayesian item development, BSD | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| 相关≠ | 5 | 4 |
| 摘要≠ | Bayesian scale development applies Bayesian statistical inference to the construction and evaluation of psychometric scales. Rather than relying on single point estimates of item and person parameters, it produces full posterior distributions that quantify uncertainty, incorporate prior knowledge, and support principled decisions about item retention, reliability, and validity in small or complex samples. | 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. |
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