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Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Compacting Factor test01:22

Compacting Factor test

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The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
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Power Evaluation of Focused Cluster Tests.

Rc Puett1, Ab Lawson2, Ab Clark3

  • 1South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, USA ; Departments of Epidemiology and Biostatistics and Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, USA.

Environmental and Ecological Statistics
|May 30, 2014
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Summary

Statistical tests for focused disease clusters are often designed for radial patterns. This study evaluated tests for various spatial patterns, finding directional effects crucial for accurate environmental health investigations.

Keywords:
clusterspowersmall area analysisspatial statistics

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Area of Science:

  • Environmental epidemiology
  • Spatial statistics
  • Public health

Background:

  • Focused disease clusters around environmental contaminants are significant public health concerns.
  • Existing statistical tests primarily detect radial clustering patterns.
  • Environmental contaminant dispersion is influenced by factors like meteorology and topography, leading to diverse spatial patterns.

Purpose of the Study:

  • To evaluate the power of different statistical tests in detecting various spatial patterns of focused disease clusters.
  • To assess the impact of environmental contaminant dispersion patterns on the selection of appropriate cluster detection methods.
  • To investigate the influence of extra variation in risk on test power.

Main Methods:

  • Simulated disease cluster data reflecting five distinct spatial patterns (radial, radial with peak, angular, angular with distance decay, angular with peak and distance decay).
  • Evaluated the detection power of Stone's Maximum Likelihood Ratio Test, Tango's Focused Test, Bithell's Linear Risk Score Test, and Lawson-Waller Score Test variations.
  • Assessed the influence of extra variation in risk on the power of these tests.

Main Results:

  • The power of focused-cluster tests varied significantly depending on the spatial pattern of the disease cluster.
  • Tests showed differential performance in detecting radial versus angular dispersion patterns of environmental contaminants.
  • Directional effects in contaminant dispersion were found to be particularly important for accurate cluster detection.

Conclusions:

  • The selection of statistical tests for focused disease cluster investigations should account for diverse environmental contaminant dispersion patterns.
  • Considering directional effects is crucial for improving the accuracy and reliability of environmental health cluster analyses.
  • While extra variation in risk was considered, it did not substantially impact test power in this study.