Cluster Sampling Advantages

Before we try to understand the advantages of cluster sampling, let us take a look at the key definitions, which will help us in understanding the concept better.

Sampling: It is a technique in which certain members of a population group are selected so that they can act as representatives for the entire population.

Sampling Unit: The subject on which information is to be collected kept under observation is called sampling unit.

Sampling Fraction: It is a ratio of sampling size and sampling population.

Sampling Frame: It is a list of many sampling units from which any sample is drawn.

Sampling Scheme: The procedure by which sampling unit is drawn from sampling frame.

Cluster Sampling: Those sampling units which are not identified independently but in group are called cluster samples. This method of sampling is called cluster sampling.

Stratified Sampling: The sampling frames are divided into homogeneous subgroups on basis of a particular attribute (like age or occupation); such sampling is called stratified sampling.

Before going on to the advantages, let us take a look at sampling itself. As the above definitions tell us, sampling is a process of selecting certain members of population. This is done so that they can act as representatives of that population. Any population, when its size is too big, it is not feasible to take into account each and every member of such population. For the purpose of observation and research some members are selected so that they can act as representatives of the entire population. The results of observation of any such samples may not be accurate for entire population, but they are considered to be the closest to actual behavior to that population.

Uses of Sampling

• Reducing field time
• Reducing costs
• Increasing accuracy
• Market research, etc

All the other probabilistic sampling methods (like simple random sampling, stratified sampling) require sampling frames of all the sampling units, but cluster sampling does not require that. Once the clusters are selected, they are compiled into frames. Now, various probabilistic researches and observations are performed on these frames and required conclusions are drawn.

Advantages

• Feasibility: Again, as I mentioned before, cluster sampling is such a method of probabilistic sampling that takes into account large populations. Since these groups are so large, deploying any other sampling technique would be very difficult task. It is very feasible when you are dealing with large population.
• Economy: The two major concerns of expenditure when it comes to sampling are traveling and listing. They are greatly reduced when it comes to cluster sampling. For example: Compiling research information about every house hold in city would be a very difficult, whereas compiling information about various blocks of the city will be easier. Here traveling as well as listing efforts will be greatly reduced.
• Reduced Variability: When you are considering the estimates by any other method of probabilistic sampling, reduced variability in results are observed. This may not be an ideal situation every time.

Disadvantages

• Biased Samples: If the group in population that is chosen as a cluster sample has a biased opinion then the entire population is inferred to have the same opinion. This may not be the actual case.
• Sampling Errors: The other probabilistic methods give fewer errors than cluster sampling. For this reason, cluster sampling is discouraged for beginners.

The above article should help the reader for knowing and understanding some of the concepts of sampling. I have a firm belief that this article has benefited the readers by increasing their knowledge on statistical data collection and also acquainted them with some advantages of cluster sampling.