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| 종단 일반화 가능성 이론× | 다수준 모형× | |
|---|---|---|
| 분야≠ | 심리측정학 | 연구 통계 |
| 계열≠ | Latent structure | Process / pipeline |
| 기원 연도≠ | 1990s–2000s | 1992 |
| 창시자≠ | Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations | Anthony Bryk and Stephen Raudenbush |
| 유형≠ | Variance components / reliability estimation | Method |
| 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 ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 별칭 | longitudinal G-theory, longitudinal GT, repeated-measures generalizability theory, G-theory for longitudinal designs | HLM, mixed-effects models, random effects models, MLM |
| 관련≠ | 4 | 3 |
| 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. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
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