Statistical Power for Randomized Controlled Trials with Clusters of Varying Size

Autor/inn/en	Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P.
Titel	Statistical Power for Randomized Controlled Trials with Clusters of Varying Size
Quelle	In: Journal of Experimental Education, 90 (2022) 3, S.673-692 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kush, Joseph M.) ORCID (Konold, Timothy R.) ORCID (Bradshaw, Catherine P.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2021.1873089
Schlagwörter	Multivariate Analysis; Randomized Controlled Trials; Monte Carlo Methods; Sample Size; Effect Size + Suchen Sie Ihr Suchwort? Multivariate Analyse; Monte-Carlo-Methode
Abstract	In two-level designs, the total sample is a function of both the number of Level 2 clusters and the average number of Level 1 units per cluster. Traditional multilevel power calculations rely on either the arithmetic average or the harmonic mean when estimating the average number of Level 1 units across clusters of unbalanced size. The current study compares these two approaches with simulation-based power estimates in cluster randomized controlled trial designs with unbalanced cluster size. Results from the Monte Carlo study demonstrated that the largest differences in simulated and calculated power occurred in study designs with large variability in the number of Level 1 units sampled. We discuss implications of these findings for the design of cluster randomized trials. [For the corresponding grantee submission, see ED612237.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01