Difference Between Cluster and Stratified Sampling

Cluster vs Stratified Sampling

Surveys are used in all kinds of research in the fields of marketing, health, and sociology. They are usually done by taking a sample of a population because making a survey on the entire population would be expensive. Aside from this, sampling makes the collection of data faster because it focuses only on a small part of the population. It also ensures the veracity and correctness of the data gathered and its uniformity and similarity.

Before sampling can be done, it is necessary to specify the concerned population, the sampling frame, sampling method, sample size, and the items or events to be measured or sampled. After this, actual sampling and data collecting can then be done. There are several sampling methods that researchers can use, some of which are: simple random sampling, systematic sampling, probability proportional to size sampling, matched random sampling, quota sampling, line sampling, event sampling, stratified sampling, and cluster sampling.

Stratified sampling is a sampling method wherein the population is divided into several strata or categories and a sample is taken from each stratum. This method is very efficient, and it helps researchers get enough hints about specific groups in the population. Each stratum can be approached differently providing researchers with a tool to learn which approach works best. While there are advantages in using stratified sampling, there are also some disadvantages in using it.

One disadvantage is that stratified sampling would require a larger number of samples from the population since the samples are to be divided into several strata. This would mean additional costs to researchers.

Cluster sampling, on the other hand, is a sampling method wherein the population is divided into groups that are already clustered in certain areas or time, and a sample is taken from each group. It can either be a two-stage sampling or multi-stage sampling. It is cost as well as time efficient because it does not entail collecting details about all elements of the population. The downside to this method is that a chosen cluster might be partial and cause the estimates to become inaccurate.

Summary:

1.The stratified sampling method is a sampling method wherein a population is divided into several strata, and a sample is taken from each stratum. Cluster sampling is a sampling method wherein the population is divided into 2.clusters that already exist in a certain area, and a sample is taken from each cluster.
3.Stratified sampling is very efficient and aims at providing precise statistical data while cluster sampling aims at increasing the efficiency of sampling.
4.Stratified sampling takes a longer period of time to accomplish while cluster sampling is time efficient.
5.Stratified sampling requires a larger number of samples since the population is divided into several strata while cluster sampling does not.
6.Cluster sampling is very cost efficient since samples are already specified while stratified sampling can be expensive.
7.Stratified sampling allows researchers to use different approaches for each stratum and see which approach works best while cluster sampling does not.

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