পদ্ধতির তুলনা করুন

	Longitudinal Generalizability Theory	Generalizability Theory (G-Theory)
ক্ষেত্র	মনোমিতি	মনোমিতি
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	1990s–2000s	1963–1972
প্রবর্তক	Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations	Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam
ধরন	Variance components / reliability estimation	Variance-components reliability model
মৌলিক উৎস	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.	Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. Wiley.
অপর নাম	longitudinal G-theory, longitudinal GT, repeated-measures generalizability theory, G-theory for longitudinal designs	G-theory, G-study / D-study framework, variance components reliability
সম্পর্কিত	4	4
সারসংক্ষেপ	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.