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Thus, the final sample may consist of any possible combination of individuals from the population meaning that the requirements for a simple random sample are satisfied.
However, this is inappropriate if it is desired to take a simple random sample of individuals from the population; cluster sampling generally involves varying chances of selection for the population members, rather than equal chances as required for a simple random sample.
For a simple random sample, this required 103 respondents from each project area.
However, population data were not available for a simple random sample.
For a simple random sample this would require 224 women per each intervention trial arm (with alpha 0.05 and 80% power), i.e. 448 in total.
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In the design of clustered-based studies, the ICC may be used to calculate the design effect which is the degree to which the sample size must be inflated above that of a simple random sample to account for the loss of information inherent in the clustered design.
For simple random sampling, the bias-adjusted estimator and a model-assisted difference estimator are equivalent, but other model-assisted options include poststratified (i.e., applying a stratified estimator to data obtained from a simple random sample), ratio, and simple regression estimators.
When such a map is the only available sampling frame, methods are well established for drawing a simple random sample of fixed area plots.
Through simulation studies, the efficacies of two methods devised by Dr. K. Iles are considered for drawing a simple random sample of individuals given a map of the population area.
● The methods presented here constitute a globally extensible approach for generating a simple random sample from the global GLAS dataset.
The methods presented here constitute a globally extensible approach for generating a simple random sample from the global GLAS dataset, enabling its use in forest inventory activities.
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