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| 多群体普遍性理论× | 多群体信度分析× | |
|---|---|---|
| 领域 | 心理测量学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1963–2001 | 1990s–2000s |
| 提出者≠ | Lee J. Cronbach and colleagues (Cronbach, Gleser, Nanda, Rajaratnam), extended to multi-group contexts by Brennan and others | Classical test theory traditions; synthesized in modern practice by Vandenberg & Lance (2000) and Sijtsma (2009) |
| 类型≠ | Variance component / reliability generalization | Reliability estimation and comparison |
| 开创性文献≠ | Brennan, R. L. (2001). Generalizability Theory. Springer. ISBN: 978-0387952826 | Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. DOI ↗ |
| 别名 | MG G-theory, multi-group G-theory, generalizability theory across groups, cross-group G-study | reliability comparison across groups, group-specific reliability estimation, multi-sample reliability analysis, cross-group internal consistency |
| 相关≠ | 6 | 4 |
| 摘要≠ | Multi-group generalizability theory (MG G-theory) extends classical generalizability theory to estimate and compare variance components — attributable to persons, items, raters, occasions, and their interactions — simultaneously across two or more defined groups. It reveals whether a measurement procedure is equally reliable and generalizable for every group studied, supporting fair and equitable score interpretation. | Multi-group reliability analysis estimates internal consistency or stability coefficients separately within each group and then formally compares them to determine whether a scale functions with equal precision across populations. It is a foundational step in cross-group measurement research, typically carried out alongside or prior to measurement invariance testing. |
| ScholarGate数据集 ↗ |
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