Salīdzināt metodes

	Daudzlīmeņu testu-retesta uzticamība	Daudzlīmeņu modelēšana
Nozare	Psihometrija	Pētniecības statistika
Saime	Latent structure	Process / pipeline
Izcelsmes gads	1979 (ICC foundation); multilevel extension: 1990s–2000s	1992
Autors	Shrout & Fleiss (ICC foundation); multilevel extension by Goldstein, Snijders, and others	Anthony Bryk and Stephen Raudenbush
Tips	Reliability estimation under hierarchical data	Method
Pirmavots	Shrout, P. E. & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Citi nosaukumi	hierarchical test-retest reliability, multilevel ICC reliability, nested test-retest reliability, ML-TRT reliability	HLM, mixed-effects models, random effects models, MLM
Saistītās	5	3
Kopsavilkums	Multilevel test-retest reliability estimates how consistently a measurement instrument produces the same scores across repeated administrations when observations are nested within higher-level units — such as patients within clinics or students within classrooms. It partitions total score variance across levels using intraclass correlation coefficients derived from multilevel models.	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.