Diffeence btn cluster sampling and stratified sampling
Cluster sampling and stratified sampling are two different methods of sampling used in research and statistical analysis.
Cluster sampling involves dividing the population into groups, or clusters, and then randomly selecting entire clusters to be included in the sample. This method is commonly used when it is difficult or impractical to obtain a complete list of the population, such as in large geographical areas or with dispersed populations. Cluster sampling can be more cost-effective and easier to implement than other sampling methods.
Stratified sampling involves dividing the population into subgroups, or strata, based on certain characteristics or variables. Samples are then randomly selected from each stratum in proportion to the population size of that stratum. This method is typically used when there are distinct subgroups within the population that may have different characteristics or when researchers want to ensure representation from each subgroup in the sample.
In summary, the main difference between cluster sampling and stratified sampling is the way in which the population is divided and sampled. Cluster sampling involves randomly selecting entire clusters, while stratified sampling involves dividing the population into subgroups and sampling from each subgroup. Each method has its own advantages and disadvantages, and the choice of sampling method will depend on the research question and the characteristics of the population being studied.