Methoden vergelijken

	Poweranalyse voor multiniveau- en gemengde-effectmodellen	Poweranalyse voor ANOVA
Vakgebied	Statistiek	Statistiek
Familie	Hypothesis test	Hypothesis test
Jaar van ontstaan	1993	1988
Grondlegger	Snijders & Bosker; Hox, Moerbeek & van de Schoot	Jacob Cohen
Type	Sample-size planning for hierarchical designs	Sample size determination
Oorspronkelijke bron	Snijders, T.A.B. & Bosker, R.J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). SAGE. ISBN: 978-1849202015	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Aliassen	HLM power analysis, mixed-effects power analysis, clustered design power analysis, Çok Düzeyli / Karma Model Güç Analizi	ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA
Verwant	4	4
Samenvatting	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.	Power analysis for ANOVA is a prospective statistical technique that determines the minimum sample size needed to detect a specified group mean difference with a chosen probability. Formalized by Jacob Cohen in his 1988 monograph, it translates a researcher's effect size expectation — expressed as Cohen's f — along with the desired Type I error rate (alpha) and statistical power (1 − beta) into a concrete per-group sample size recommendation for one-way or factorial ANOVA designs.