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| 종단 일반화 가능성 이론× | 일반화가능성 이론 (G-Theory)× | |
|---|---|---|
| 분야 | 심리측정학 | 심리측정학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 1990s–2000s | 1963–1972 |
| 창시자≠ | Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations | Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam |
| 유형≠ | Variance components / reliability estimation | Variance-components reliability model |
| 원전≠ | Webb, N. M., Shavelson, R. J., & Harrigan, E. H. (2007). Generalizability theory: Overview. In C. R. Rao & S. Sinharay (Eds.), Handbook of Statistics, Vol. 26: Psychometrics (pp. 1–43). Elsevier. link ↗ | Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. Wiley. link ↗ |
| 별칭≠ | longitudinal G-theory, longitudinal GT, repeated-measures generalizability theory, G-theory for longitudinal designs | G-theory, G-study / D-study framework, variance components reliability |
| 관련 | 4 | 4 |
| 요약≠ | Longitudinal generalizability theory extends classical G-theory to repeated-measures and longitudinal designs, decomposing score variance across persons, measurement occasions, raters, and items simultaneously. It quantifies how reliably scores can be generalized across time points, evaluators, and conditions — information that is invisible to cross-sectional reliability indices. | Generalizability Theory is a psychometric framework that decomposes observed score variance into multiple sources — persons, items, raters, occasions, and their interactions — using analysis of variance. It replaces the single reliability coefficient of classical test theory with a family of coefficients that tell researchers how well scores generalize across different measurement conditions. |
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