Cluster sampling is a probability sampling method in which the population is divided into separate groups or clusters, and a simple random sample of clusters is selected from the population. Unlike stratified sampling, where a sample is taken from every stratum, cluster sampling involves selecting only some of the clusters for the sample.
In cluster sampling, there can be variations within each cluster, and the clusters may not be as internally homogeneous as the strata in stratified sampling. Cluster sampling can be implemented in two ways: single-stage and multistage. In single-stage cluster sampling, the entire selected clusters are sampled, while in multistage cluster sampling, random samples are taken within the chosen clusters in one or more stages.
Compared to stratified sampling, cluster sampling tends to increase sampling error for the same sample size. In other words, cluster sampling requires a larger sample size to achieve the same accuracy standards.
However, cluster sampling offers cost savings, particularly when travel costs between clusters are high. By reducing the cost per respondent, cluster sampling may result in a lower overall cost for the study.
Ideally, within stratified sampling, the variation within each stratum should be small (homogeneous), while within cluster sampling, the variation within clusters should be large. However, in practice, controlling the variation within clusters is often beyond our control.
The process of cluster sampling involves dividing the population into clusters (e.g., towns or cities), grouping the clusters into strata, and taking a cluster sample from each stratum. This approach ensures that the selected clusters are representative of different strata within the population.
To see how cluster sampling works, consider the example of the urban India household panel which used to be the largest consumer panel in the world. Set-up by Hindustan Lever, the panel was configured by splitting all Indian cities and towns (clusters) into groups based on size and geographical location. A selection of about 20 clusters was made covering small, medium, and large urban centres across north, south, east, and west of India. The selected towns/cities were then further divided into blocks, and these blocks were stratified based on variables such as household income and household size. The panel was formed by randomly selecting homes from the chosen urban blocks to adequately represent all strata. This multi-stage sampling process involved selecting cities, then blocks, and finally homes.
A similar approach, involving clustering and stratification, is followed when conducting national surveys of individuals or households in countries across the globe.
In summary, cluster sampling is a probability sampling method that involves dividing the population into clusters, selecting a random sample of clusters, and sampling within those clusters. While cluster sampling requires larger sample sizes than stratified sampling to achieve the same accuracy standards, it offers cost savings and can be an effective approach, especially when there are geographical or logistical constraints in data collection.
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