Compară metode

	Analiza puterii statistice pentru modele multinivel și cu efecte mixte	Model cu efecte mixte
Domeniu	Statistică	Statistică
Familie	Hypothesis test	Regression model
Anul apariției	1993	1982
Autorul original	Snijders & Bosker; Hox, Moerbeek & van de Schoot	Laird & Ware
Tip	Sample-size planning for hierarchical designs	Mixed effects regression
Sursa seminală	Snijders, T.A.B. & Bosker, R.J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). SAGE. ISBN: 978-1849202015	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
Denumiri alternative	HLM power analysis, mixed-effects power analysis, clustered design power analysis, Çok Düzeyli / Karma Model Güç Analizi	LME, LMM, mixed model, random effects model
Înrudite	4	4
Rezumat	Multilevel power analysis is a sample-size planning procedure designed for hierarchical, clustered, or longitudinal study designs in which observations are nested within higher-level units such as students within schools or patients within clinics. Formalized in the multilevel modeling literature by Snijders and Bosker (1993, expanded 2012) and Hox, Moerbeek, and van de Schoot (2017), it accounts for the intraclass correlation (ICC) and the design effect that arises when data are clustered, ensuring that both the number of clusters and the cluster size are adequate to detect a target effect.	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.