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Related Concept Videos

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Statistical Methods for Analyzing Epidemiological Data01:25

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Updated: Feb 10, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

Francis L Huang1

  • 1University of Missouri, Columbia, MO, USA.

Educational and Psychological Measurement
|May 26, 2018
PubMed
Summary
This summary is machine-generated.

Cluster bootstrapping offers a viable alternative for analyzing clustered data, especially when few groups are involved in cluster randomized trials. This method provides reliable standard errors for group-level treatment effects.

Keywords:
cluster bootstrappingcluster randomized trialsclustered datalow number of clusters

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Area of Science:

  • Statistics
  • Research Methodology
  • Social Sciences

Background:

  • Cluster randomized trials (CRTs) are common in education, psychology, and biomedicine.
  • Recruiting and retaining intact groups in CRTs pose significant challenges, often leading to a low number of clusters.
  • Multilevel models are standard for nested data but can yield biased results with few clusters.

Purpose of the Study:

  • To compare the efficacy of cluster bootstrapping against ordinary least squares regression and multilevel models.
  • To evaluate standard errors derived from cluster bootstrapping in scenarios with a limited number of clusters.
  • To assess the suitability of cluster bootstrapping for analyzing clustered data in educational and psychological research.

Main Methods:

  • A Monte Carlo simulation was employed to compare statistical methods.
  • The simulation varied the number of clusters, average cluster size, and intraclass correlations.
  • Standard errors from cluster bootstrapping were compared to those from OLS and multilevel models.

Main Results:

  • Cluster bootstrapping provides a reliable alternative for analyzing clustered data.
  • This method is particularly useful when treatment effects at the group level are the primary focus.
  • While computationally intensive, cluster bootstrapping offers a valid approach even with few clusters.

Conclusions:

  • Cluster bootstrapping is a recommended procedure for analyzing clustered data in CRTs, especially with a small number of clusters.
  • Researchers can confidently use cluster bootstrapping when group-level treatment effects are of interest.
  • Supplementary R code is available to facilitate the implementation of cluster bootstrapped regressions.