What is the difference between Cluster and Stratified Sampling?

Sampling makes the assortment of information faster because it focuses on only a small section of the population. It also ensures the authenticity of the information amassed. The stratified sampling method is where the population is divided into several categories. This method is quite efficient, as it assists researchers in understanding the individual groups within the population. Each stratum can be approached differently, as the information supplied will provide the researchers with a tool to learn which method performs the best. Cluster sampling, on the other hand, is a sampling method where the population is separated into groups that are already clustered in certain areas or time, and a sample is taken from each group. It can be either a two-stage sampling or a multi-stage sampling. It is costly and time-efficient because it does not involve collecting details concerning all elements of the population. This method's disadvantage is that a chosen cluster might be partial and cause the estimates to become inaccurate.

Sampling makes the assortment of information faster because it focuses on only a small section of the population. It also ensures the authenticity of the information amassed. The stratified sampling method is where the population is divided into several categories. This method is quite efficient, as it assists researchers in understanding the individual groups within the population.

Each stratum can be approached differently, as the information supplied will provide the researchers with a tool to learn which method performs the best. Cluster sampling, on the other hand, is a sampling method where the population is separated into groups that are already clustered in certain areas or time, and a sample is taken from each group. It can be either a two-stage sampling or a multi-stage sampling.

It is costly and time-efficient because it does not involve collecting details concerning all elements of the population. This method's disadvantage is that a chosen cluster might be partial and cause the estimates to become inaccurate.

Cluster and Stratified Sampling are two types of sampling methods used by researchers to collect data during research works. In stratified sampling, the first step to take involves the breaking down of a population of people into different categories. After the first step, a sample is then taken from each group. This sampling method has proven to be effective and efficient as researchers can easily conclude on some categories in the population. There is no doubt stratified sampling has a lot of advantages, however, it also has certain disadvantages. For instance, you will have a lot of samples to deal with if you are using stratified sampling. On the other hand, cluster sampling refers to a sampling method that involves the breaking down of a population of people into clusters or groups, after which a sample will be taken from each group. One of the advantages of cluster sampling is that it is time-efficient compared to stratified sampling.

When you say cluster sampling, this means that one cluster can be considered as one unit. This way, sampling different products. Cluster sampling means that there is a population of clusters that are involved in the first stage. When you say stratified sampling, this means that the different samplings will be done on various elements that are available. There are some breaks or clusters that will not exist between the different groups which means that you need to sample the different products as a whole. This will then prompt you to just divide your target population into various groups to make the sampling far easier for you. One example of cluster sampling is when you divide your group into cities.