Comparer des méthodes

	Analyse de puissance pour les modèles multiniveaux et à effets mixtes	Modèle à effets mixtes
Domaine	Statistique	Statistique
Famille	Hypothesis test	Regression model
Année d'origine	1993	1982
Auteur d'origine	Snijders & Bosker; Hox, Moerbeek & van de Schoot	Laird & Ware
Type	Sample-size planning for hierarchical designs	Mixed effects regression
Source fondatrice	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.
Alias	HLM power analysis, mixed-effects power analysis, clustered design power analysis, Çok Düzeyli / Karma Model Güç Analizi	LME, LMM, mixed model, random effects model
Apparentées	4	4
Résumé	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.