Optimal Sample Allocation for Three-Level Multisite Cluster-Randomized Trials

Autor/inn/en	Shen, Zuchao; Kelcey, Benjamin
Titel	Optimal Sample Allocation for Three-Level Multisite Cluster-Randomized Trials
Quelle	In: Journal of Research on Educational Effectiveness, 15 (2022) 1, S.130-150 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Shen, Zuchao)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2021.1953200
Schlagwörter	Sampling; Research Design; Randomized Controlled Trials; Statistical Analysis; Sample Size; Hierarchical Linear Modeling + Suchen Sie Ihr Suchwort? Forschungsdesign; Statistische Analyse
Abstract	Optimal sampling frameworks attempt to identify the most efficient sampling plans to achieve an adequate statistical power. Although such calculations are theoretical in nature, they are critical to the judicious and wise use of funding because they serve as important starting points that guide practical discussions around sampling tradeoffs and requirements. Conventional optimal sampling frameworks, however, often identify sub-optimal designs because they typically presume the costs of sampling units are equal across treatment conditions. In this study, we develop a more flexible framework that allows costs to differ by treatment conditions and derive the optimal sample size formulas for three-level multisite cluster-randomized trials. We find that the proposed optimal sampling schemes are driven by the differences in costs between treatment conditions, cross-level sampling cost ratios and cross-level variance decomposition ratios. We illustrate the utility of the proposed framework by comparing it to a conventional framework and find that the proposed framework frequently identifies more efficient designs. The proposed optimal sampling framework has been implemented in the "R" package "odr." (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01