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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Models for cluster randomized designs using ranked set sampling.

Omer Ozturk1, Olena Kravchuk2, Richard Jarrett2

  • 1Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, Ohio, 43210, USA.

Statistics in Medicine
|April 11, 2023
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This study enhances cluster randomized designs (CRD) by integrating ranked set sampling. This novel approach improves efficiency and precision in cluster sampling for various research applications.

Keywords:
intra-cluster correlationnested designrandom effect modelranked set samplingranking error study

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Area of Science:

  • Biostatistics
  • Survey Sampling
  • Clinical Trial Design

Background:

  • Cluster randomized designs (CRD) are crucial for studies randomizing treatments to clusters, but often lack efficiency compared to completely randomized designs.
  • The reduced efficiency in CRDs stems from randomization applied at the cluster unit level, impacting precision.
  • Integrating advanced sampling techniques can potentially mitigate these efficiency limitations.

Purpose of the Study:

  • To improve the efficiency and precision of cluster randomized designs.
  • To introduce a novel sampling methodology by embedding ranked set sampling within CRDs.
  • To provide guidance on optimal sample size determination for the proposed design.

Main Methods:

  • Embedding a ranked set sampling design into a cluster randomized design framework.
  • Utilizing ranking groups within ranked set sampling as a covariate to reduce error.
  • Developing an optimality result for determining sample sizes at both cluster and sub-sample levels.

Main Results:

  • The proposed ranked set sampling within CRD significantly reduces the expected mean squared cluster error.
  • The integration of ranked set sampling demonstrably increases the precision of the overall sampling design.
  • Ranking groups effectively function as a covariate, enhancing the statistical power of the design.

Conclusions:

  • The novel embedded ranked set sampling design offers a more precise and efficient alternative to traditional CRDs.
  • This methodology is applicable to diverse research fields, including dental studies and educational interventions.
  • The findings provide a robust framework for optimizing sample size in complex cluster randomized studies.