In a cluster sample, researchers divide subjects into strata (like cities, for example), randomly select a few strata (draw the names of a few cities from a hat) and sample every subject in those strata (question everyone in that city.) A significant disadvantage is that you may select strata that completely overlook a feature relevant to your study.
Advantages of cluster sampling include that it's inexpensive, fast, and simple. A disadvantage is that it is known to have a high sampling error.
Cluster sampling is a scheme which is used when sampling from the whole population would be too expensive - in terms of time or money. The sample space is divided into clusters, a random sample of clusters is selected and then every member of the population within the selected clusters is studied.For example, suppose you wanted to collect information from schools across a country and had calculated that a 5% sample was required. Rather than criss-crossing the country, you could divide the country into units: for example counties. You then select counties (your clusters) so that they cover 5% of the nation's schools. Visit each chosen county and sample all schools in it. The selection of counties would probably also need to be controlled so that urban and rural areas are properly represented.
Cluster Sampling
It is called one-stage cluster sampling. If random samples are taken within the selected clusters then it is two-stage cluster sampling.
In a cluster sample, researchers divide subjects into strata (like cities, for example), randomly select a few strata (draw the names of a few cities from a hat) and sample every subject in those strata (question everyone in that city.) A significant disadvantage is that you may select strata that completely overlook a feature relevant to your study.
Determining the ideal sample size in cluster sampling involves several factors. Here's a breakdown of the key considerations: Factors Affecting Sample Size: Desired Precision: The level of accuracy you want in your results. Higher precision requires a larger sample size. Intra-Cluster Correlation (ICC): This measures how similar units within a cluster are compared to units from different clusters. A higher ICC means you need a larger sample size to account for the clustering effect. Cluster Size: The average number of units within each cluster. Smaller cluster sizes typically require a larger number of clusters to achieve the same level of precision. Confidence Level: The level of certainty you want in your findings. A higher confidence level (e.g., 95% vs. 90%) typically necessitates a larger sample size. Calculating Sample Size: Unfortunately, there's no one-size-fits-all formula for sample size in cluster sampling. However, there are statistical software programs and online calculators that can help you determine the appropriate sample size based on the factors mentioned above. Here are some resources that can be helpful: Sample Size Calculators: Guides on Cluster Sampling and Sample Size: Additional Tips: Pilot Study: Consider conducting a pilot study on a smaller sample to estimate the ICC and refine your sample size calculations. Software or Statistical Help: If you're not comfortable with statistical calculations, consider using specialized software or consulting a statistician for assistance in determining the optimal sample size for your cluster sampling design.
There are circumstances when it is important and others when it is not. If, for example, you wanted a sample of all schools in the country, it would make more sense to go for cluster sampling. A lot of market research work will require quota sampling. So the supremacy of a random sample is a myth.
Advantages of cluster sampling include that it's inexpensive, fast, and simple. A disadvantage is that it is known to have a high sampling error.
Sample, example.
Cluster sampling is a scheme which is used when sampling from the whole population would be too expensive - in terms of time or money. The sample space is divided into clusters, a random sample of clusters is selected and then every member of the population within the selected clusters is studied.For example, suppose you wanted to collect information from schools across a country and had calculated that a 5% sample was required. Rather than criss-crossing the country, you could divide the country into units: for example counties. You then select counties (your clusters) so that they cover 5% of the nation's schools. Visit each chosen county and sample all schools in it. The selection of counties would probably also need to be controlled so that urban and rural areas are properly represented.
Simple random sampling.
Cluster Sampling
a example of a hendecagon is a hedecagon
sample example
When discussing race, you can use "cluster" to refer to the grouping of individuals with similar racial characteristics. For example, "In this diverse city, neighborhoods often cluster by race, creating pockets of different ethnic communities."
No, a typical soil sample is heterogeneous.