ERIC Number: EJ1170584
Record Type: Journal
Publication Date: 2018-Apr
Abstractor: As Provided
Using Cluster Bootstrapping to Analyze Nested Data with a Few Clusters
Huang, Francis L.
Educational and Psychological Measurement, v78 n2 p297-318 Apr 2018
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.
Descriptors: 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
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Publication Type: Journal Articles; Reports - Research
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