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.
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.
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.
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.
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.
There are four multistage steps that must be followed to conduct multistage sampling:
Now that you know how to conduct multistage sampling design, where do you apply them?
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.
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.
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.
Let us consider these two examples of multistage sampling design.
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:
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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.
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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