In multistage sampling or multistage cluster sampling, a sample is drawn from a population through the use of smaller and smaller groups (units) at each stage of the sampling. In this article, we are going to discuss multistage sampling, its uses, the advantages, and the disadvantages.

What is Multistage Sampling

Multistage sampling is defined as a method of sampling that distributes the population into clusters or groups so as to conduct research. This is a complex form of group sampling, during which the significant groups from the selected population are divided into subgroups at different stages. It is primarily to ensure that it is easier to collect the primary data. 

Hence, this sampling method is used in a national survey to gather data from a large population of people geographically spread across. Multistage sampling is also known as multistage cluster sampling. 

Learn: Purposive Sampling: Definition, Types, Examples

Types of Multistage Sampling

There are two types of multistage sampling and they are multistage cluster sampling and multistage random sampling. 

Let’s look at the two multistage sampling types in detail.

1. Multistage cluster sampling

Multistage cluster sampling is a complex form of cluster sampling because the researcher has to divide the population into clusters or groups at different stages so that the data can be easily collected, managed, and interpreted. 

For example, if a researcher wants to conduct research on the different eating habits in the United States, it is impossible to go from one house to the other to collect this data from everyone. So, the researcher will have to select the states that are of interest to the study.

He/she will select the district needed for the research and then narrow it down by selecting specific streets or blocks to represent the state. The researcher will finally choose specific respondents from the selected blocks to participate in the research. 

From this example, we can see that clusters are selected at different progression stages until they have been narrowed down to the sample required by the researcher.

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2. Multistage random sampling

This is the second type of multistage sampling. The multistage random sampling technique is not too different from multistage cluster sampling; however, the samples are selected randomly at each stage by the researcher. 

The clusters are not created by the researcher, but the samples are narrowed down by applying random sampling.

For example, a researcher wants to understand the feeding habits of children under the age of 10 in the United States, and for the purpose of the research, the sample size will be 50 respondents. 

The researcher will first randomly select 5 states out of 20. They will then select 5 districts out of each state randomly. Now, from the 5 selected districts, the researcher will randomly choose 6 households to participate in the research.

Explore: Convenience Sampling: Definition, Applications, Examples

How to Conduct Multistage Sampling

There are four multistage steps that must be followed to conduct multistage sampling:

  1. You must first select a sampling frame based on the population of interest for your research. It is important that you allocate a number to every cluster and choose a small sample from separate groups that are relevant to your research.
  2. Next, proceed to select a sampling frame from sub-groups that are relevant to the study. To achieve this, select your sampling frame from related yet, diverse discrete clusters that you selected in the first stage.
  3. There are some cases where you may need to repeat the second step of selecting sample frames from the sub-groups if the groups in the first stage are not representing the population properly. In this instance, it is best to repeat the second step. 
  4. Make use of some variation of probability sampling to select the members for your sample group from the sub-groups.

Now that you know how to conduct multistage sampling design, where do you apply them?

Read: Snowball Sampling Guide: Types, Examples, Pros & Cons

What are the Applications of Multistage Sampling?

  1. Multistage sampling can be applied to a multistage design where the population is too large and it is practically impossible to research every individual.
  2. Multi-stage sampling can also be used to conduct surveys on the employees of a multinational corporation that are in multiple locations in multiple countries of the world.
  3. Researchers can use multistage sampling to collect the perceptions of various students on a particular study even when they are in different locations or universities and studying different courses.
  4. Researchers also apply multi-stage sampling when they have limited time to conduct a study. Information drawn from the small sample can then be used to draw inferences on the entire population.

It is a good practice to carefully examine the ways you intend to implement the multistage approach because there is no exact definition or approach to multiphase sampling. There is no conventional method or process for mixing the sampling methods. 

This is why you must ensure that the process design retains its randomness and sample size. Also, the process design must be both cost and time-effective. 

Read: What is Stratified Sampling? Definition, Examples, Types

What is the Difference between Stratified Sampling and Multistage Sampling?

In stratified sampling, all groups are samples but it is different in the case of multistage sampling as only a subset of the groups or clusters is sampled. Also, only sub-samples are drawn in the second stage from the clusters selected in the first stage so that the total groups can be well estimated.

In stratified sampling, the population is divided into strata, unlike multistage sampling where a list is drawn from the entire population especially when the population is large and has to be separated.

While the population is divided into smaller groups in stratified sampling, it is divided into smaller stages in a step-by-step manner in multi-stage sampling.

The strata are created based on shared values or characteristics in stratified sampling while the population is selected into stages randomly in multi-stage sampling.

Read: Cluster Sampling Guide: Types, Methods, Examples & Uses

What is the Difference between Multistage and Multistage Sampling

Multiphase sampling and multistage sampling are sometimes used interchangeably. However, there are still a few things that distinguish the two.

In multi-stage sampling, there are different stages and different types while in multiphase sampling, the observation happens differently with each sample unit being related to the same type of group.

What are the Advantages of Multistage Sampling

  1. Multistage sampling helps researchers to implement cluster or random sampling after the groups have been determined.
  2. Multistage sampling enables the researcher to distribute the population into groups without restrictions.
  3. It is also very useful in the collection of primary data especially from the population that is geographically dispersed.
  4. It allows for continuity. Therefore the researcher can create clusters and sub-clusters until the required size or the needed group is selected.
  5. Multistage sampling is also a flexible method. It allows the researcher to carefully make the selection of the sample group.
  6. Multistage sampling is cost and time-effective. This is because the large population is usually broken down into smaller clusters before conducting the study.
  7. Another advantage of multistage sampling is how the researcher can conveniently find the most appropriate survey sample.
  8. Multistage sampling allows the researcher to mindfully select the audience for the research thereby taking away the issue of uncertainty that comes with random sampling.
  9. Multistage sampling also does not require a full list of all the members in the population of interest, therefore, reducing the research preparation cost.

What are the Disadvantages of Multistage Sampling

  1. Multistage sampling has a high level of subjectivity in its process.
  2. Another disadvantage of multistage sampling is that it is not totally an accurate representation of the population. This is because there is never a 100% population representation in research studies.
  3. Multistage sampling also requires information from many group levels; otherwise, the procedure will not be successful. 

Multistage Sampling Examples

Let us consider these two examples of multistage sampling design.  

Example 1:

Let us assume that the sample location of interest to the researcher and the study is the United States. We can further assume that the goal of the research is to assess the digital or online spending trends of people living in the United States using a digital questionnaire. 

The researcher may decide to curate a sample group consisting of 200 households using these methods:

  • Firstly, the researcher will use simple random sampling to select the number of states. (Other probability sampling methods can be used). So, the researcher can select 10 states in the United States for example.
  • The next thing would be to use a systematic sampling method or any other type of probability sampling method to select 5 districts within each of the selected states.
  • The following step would be to make use of a systematic sampling method or simple random sampling method to pick four households from each of the districts. If this is done, the researcher will end up with 200 houses that can be input into the sample group to participate in the research.
Learn About: Sampling Bias: Definition, Types + [Examples]

Example 2:

A research survey was conducted by a firm in the United States. The research group divided the country into counties and selected some of the counties randomly as a cluster sample. This selection serves as the first stage in multistage sampling.

After the first selection, the selected counties were divided into towns. From the chosen towns, the researcher randomly selected areas, and then each of the areas was further divided into small households. 

The households to be used for the study were selected randomly and they formed the sample population for the research study.

After completing these steps, the researcher can apply multi-stage sampling. What it does is help to select representatives for a vast population but in stages.

Conclusion

Multistage sampling is a complex form of cluster sampling, however, it is useful when your research population is large. It will help you to eliminate the impracticality of making use of a large sample size. It also limits the risk of bias as the cluster sample is selected randomly.



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