Cited by 636Statistical Methods in Clinical TrialsStatistical Methods and Bayesian InferenceOptimal Experimental Design Methods

Statistical analysis and optimal design for cluster randomized trials.

Raudenbush, Stephen W.·1997·Psychological Methods

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Crossref · 10.1037/1082-989x.2.2.173

Cites

22 of 22 references verified0 citation errors · 0 not checkable

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  • In randomized new-versus-usual-practice trials, sites are allocated at random to either the new active implementation or the usual practice condition; group-level allocation makes this a cluster-randomized design.

    “Here the sites (practices, communities, organizations) are assigned randomly to the new active implementation or usual practice condition. As random assignment is at the group rather than individual level, this forms a cluster-randomized design (75, 95).”

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Authors
Raudenbush, S. W.
Affiliations
Michigan State University (US)
Corresponding author
Raudenbush, Stephen W.
Venue
Psychological Methods
Published
1997-06
Volume
2
Issue
2
Pages
173-185
ISSN
  • 1082-989X (ISSN-L)
  • 1939-1463
OpenAlex
W2069517904
Cited by
636
References
22
Citations by year
2026: 13 · 2025: 16 · 2024: 23 · 2023: 27 · 2022: 26 · 2021: 30 · 2020: 29 · 2019: 28 · 2018: 34 · 2017: 31 · 2016: 30 · 2015: 30 · 2014: 29 · 2013: 35 · 2012: 25
FWCI
4.21
Type
journal article
Language
en
Open access
closed
Locations
Psychological Methods (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Keywords
optimal design, statistical analysis, cluster size, allocation ratio, variance components

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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