Multistage sampling – A Comprehensive Guide with Examples

In research work, the most important thing is a sample of data which helps in the representation of a large population. For a research study, it is not possible to approach every single person and collect responses. So, in order to deal with this aspect, sampling is used. There is not a single sampling technique, but multiple. Each technique of sampling is supposed to perform a particular task. So, based on your research objective and problem statement, you have to see which type should be selected. One of the most commonly used sampling techniques is named multistage sampling, which demands a particular approach to work on it for data collection. This article will provide you with a comprehensive guide for multistage sampling. 

What is meant by Multistage sampling in Research Work?

Multistage sampling is a sampling method employed in primary data collection. The sampling technique splits the population into clusters and selects a sample from those multiple clusters. It is also called multistage cluster sampling. The sampling method uses multiple smaller units of samples and then draws a larger sample from the clusters. The multistage sampling technique is useful when the researcher has to collect data from a broad range of people spread across a broad area, such as in national-level surveys or research. 

What are the Main Steps Required to Follow in a Multistage sampling?          

The multistage sampling can be completed in four simple steps. Each step has a particular role to play, so you may pay attention to it. Let’s discuss these steps in detail. 

Work on the Cluster Collection 

Depending on the demographic that interests you for your research, you must first choose a sampling frame. A small sample from several groups that are pertinent to your research should be selected, and it’s crucial to assign a number to each cluster.

Go for a Particular Frame of Sampling 

The next step is to choose a sampling frame from the segments that apply to the study. Choose your sampling frame from the similar yet varied distinct clusters you chose in the prior stage to accomplish this.

Ensure Repetition of Steps 

In some circumstances, if the categories in the initial step do not adequately represent the population, you may be required to duplicate the second step of choosing sample frames from the subcategories. The best course of action in this situation is to repeat the second step.

Practice the Probability Sampling Technique 

Use probability sampling to select the sample clusters from the identified subcategories. It is essential to remember that multistage sampling proceeds from higher domains to lower domains. You may need to use variants of probability sampling to select a representative sample, and for that, you must be familiar with random sampling techniques. However, if you experience any difficulty in using the probability sampling technique to select multiple clusters of samples, you can always take service from experts at masters dissertation help.  

What are Different Types of Multistage Sampling Used in Research?

Multistage sampling is divided into multistage random sampling and multistage cluster sampling. Let’s overview both types of sampling methods:

Multistage Random Sampling

Multistage random sampling is the first type of multistage sampling. The multistage random sampling technique is similar to the multistage cluster sampling technique, although the samples are chosen at random by the researcher at each step. Unlike multistage cluster sampling, clusters are not selected by the researcher, but the samples are chosen using a random sampling technique. Random sampling technique means selecting the people at random, and it requires using computer software. The researcher plugs in the population numbers in the software, and the software randomly selects the sample. Stratified random sampling is another way to ensure that the chosen sample is representative of the entire population. Using stratified random sampling, the researcher specifies the percentage of population groups and selects the sample from the divisible pool.

For example, if the researcher wants to conduct research on the university’s student body, the student body must be divided into freshmen, sophomore, junior, and senior students. For each sample of 100, the researcher specifies the percentage of identified categories, and then the computer selects the students randomly from the specified groups. So, multistage cluster sampling replicates the random sampling technique at each stage until it arrives at a reasonable sample.

For example

if the researcher wants to understand the study habits of university students in the United States. The researcher has specified a sample limit of 100 students. The researcher will randomly select 10 states out of 50 states. Afterwards, the researcher will select 10 districts from each state randomly. After selecting the districts, the researcher will select 10 households to participate in the research. 

Multistage Cluster Sampling

Multistage cluster sampling is challenging because it requires the researcher to segment the population into clusters at several points to facilitate the collection, storage, and analysis of the data. For example, if the researcher wants to conduct research on the voting preferences of people in the US, it is impossible to visit every other household and collect data from every household in the United States.

So, the researcher will have to narrow down the scope of interest and concentrate on the specific states that are relevant to the research. Afterwards, the researcher must specify the specific cities, towns and localities and select the specific areas that can furnish the researcher with relevant data. After specifying the area, the researcher will choose specific people from the identified areas to partake in the research process. We can infer from this discussion that the researcher will have to divide the population into clusters at each stage. Until the researcher narrows down the scope to a perfect sample which is representative.

Multistage cluster sampling requires applying logical reasoning skills to select clusters at each stage. And if you feel you cannot do it, you can always take help from expert researchers.

Conclusion

It is the multistage sampling which has made it possible to remove the boundaries of geography. Data collection from any part of the world is no more difficult for any researcher. You can collect data from a large population at the national as well as international levels. The most important step is of clusters helps to use a sample stratum for the representation of inhabitants. The repetition process helps researchers gather as many clusters as possible to ensure that each member has an equal chance of being selected for the research study.