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| 多群信頼性分析× | 多群同時確認的因子分析(MG-CFA)× | |
|---|---|---|
| 分野 | 心理測定学 | 心理測定学 |
| 系統 | Latent structure | Latent structure |
| 提唱年≠ | 1990s–2000s | 1971 |
| 提唱者≠ | Classical test theory traditions; synthesized in modern practice by Vandenberg & Lance (2000) and Sijtsma (2009) | Karl Jöreskog |
| 種類≠ | Reliability estimation and comparison | Measurement model / invariance test |
| 原典 | 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 ↗ | 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 ↗ |
| 別名 | reliability comparison across groups, group-specific reliability estimation, multi-sample reliability analysis, cross-group internal consistency | MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA |
| 関連≠ | 4 | 6 |
| 概要≠ | 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. | Multi-group confirmatory factor analysis tests whether a measurement model holds equivalently across two or more groups — such as cultures, genders, or time points. By imposing increasingly stringent equality constraints and comparing model fit, it determines whether comparisons of latent mean scores are justified. |
| ScholarGateデータセット ↗ |
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