Cluster sampling is a sampling method used when studying large populations spread across a wide area. It’s particularly useful when a list of all the members of the population isn’t available, or when collecting data from the entire population is logistically challenging or costly.
In cluster sampling, the population is divided into separate groups, known as clusters. These clusters may often be geographically defined, such as towns, neighborhoods, or blocks, although clusters can also be defined in other ways. Each cluster should ideally be a miniature representation of the population, meaning that it should contain members that are diverse and represent the variability of the whole population.
Here are the general steps involved in cluster sampling:
1. **Dividing the Population into Clusters**: The first step in cluster sampling is to divide the population into clusters.
2. **Selecting Clusters**: Then, a random sample of clusters is chosen using a simple random or systematic sampling method. All the units (people, households, etc.) within the selected clusters are included in the sample. This is known as one-stage cluster sampling.
3. **Sampling within Clusters**: Alternatively, after selecting the clusters, the researcher might conduct a further random sample within those clusters to select specific units for the study. This is known as two-stage cluster sampling.
For example, if a researcher wants to survey the residents of a large city, they might divide the city into neighborhoods (the clusters) and then randomly select a number of neighborhoods to include in the study. Then, every household in the selected neighborhoods could be surveyed (one-stage), or a random sample of households could be surveyed within each selected neighborhood (two-stage).
Cluster sampling can be more practical and cost-effective than other methods when dealing with large, dispersed populations. However, it often suffers from higher sampling error compared to methods like simple random sampling or stratified sampling, because there is generally more variability between clusters than there is within clusters. This method requires a larger sample size to achieve the same level of precision.