Salīdzināt metodes

	Longitudinal Generalizability Theory	Daudzlīmeņu modelēšana
Nozare	Psihometrija	Pētniecības statistika
Saime	Latent structure	Process / pipeline
Izcelsmes gads	1990s–2000s	1992
Autors	Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations	Anthony Bryk and Stephen Raudenbush
Tips	Variance components / reliability estimation	Method
Pirmavots	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.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Citi nosaukumi	longitudinal G-theory, longitudinal GT, repeated-measures generalizability theory, G-theory for longitudinal designs	HLM, mixed-effects models, random effects models, MLM
Saistītās	4	3
Kopsavilkums	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.