Using Cluster Bootstrapping to Analyze Nested Data with a Few Clusters

Autor/in	Huang, Francis L.
Titel	Using Cluster Bootstrapping to Analyze Nested Data with a Few Clusters
Quelle	In: Educational and Psychological Measurement, 78 (2018) 2, S.297-318 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164416678980
Schlagwörter	Multivariate Analysis; Sampling; Statistical Inference; Data Analysis; Randomized Controlled Trials; Monte Carlo Methods; Correlation; Error of Measurement; Comparative Analysis; Least Squares Statistics; Sample Size; Statistical Analysis + Suchen Sie Ihr Suchwort? Multivariate Analyse; Inferential statistics; Schließende Statistik; Auswertung; Monte-Carlo-Methode; Korrelation; Messfehler; Statistische Analyse
Abstract	Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01