Data collection is a very important phase of any research. It helps answer the research questions and draw valuable conclusions about the research population. However, you need to apply different methods of data collection to collect representative samples of the population. One of those methods is multistage sampling.
Do you not know about this important sampling method? It is sad to hear that you are completely unaware of the multistage sampling method used commonly in research. However, there is no need to worry because today’s article explains this sampling method and highlights its differences from cluster sampling. The full description of this method, like steps to collect samples using this method and its applications, will be part of this article. So, let’s get started with today’s discussion with the following question:
What is multistage sampling? Explain the difference between cluster and multistage sampling
The word “multistage” explains this sampling method pretty much. It means a sampling is conducted in different stages. The multistage sampling method is a method of sampling that distributes the entire population under study into different groups. As a researcher, you analyse each group separately and move forward step by step. This sampling method is most useful in the cases where the population is spread over a large area, or the geographical boundaries restrict the research process. It is also important to note that this sampling method is complex and challenging to perform.
Coming toward the difference between multistage and cluster sampling, it is important to note that both are the same sampling methods. Cluster sampling is another name for multistage sampling. It is often called a multistage cluster sampling technique. However, some authors are of the opinion that cluster sampling is a subtype of a multistage method of sampling. Whatever the case is, either a type or the same, there are no big differences. Getting dissertation help online is a good solution to deal with all of the challenges.
Types of multistage sampling methods
After reading the information above, you now have a pretty good of this sampling method for the population. However, this little knowledge is not enough. You must also have an idea of the types that are common in academic research. There are two types of this sampling method in research primarily. A brief description of both types is as follows:
1. Multistage cluster sampling
The first type is multistage cluster sampling. This type is regarded as the toughest and most challenging type of multistage method of sampling. It involves dividing the overall population into clusters or groups at different stages of the research. After dividing the population, the researcher collects the data, making the analysis and interpretation easy. An example of this type of sampling can be researching the eating habits of the people of the UK. It is almost impossible for a researcher to go to each door and ask them what they eat in the morning or at night. So, what does the researcher do? He divides the whole of the UK into states and then collects the data of those states. This is what multistage cluster sampling is all about.
2. Multistage random sampling
It is the second type of multistage sampling. This type does not differ much from cluster sampling; the only difference is that the samples are selected randomly instead of systematically. The researcher creates the clusters of the samples and then selects them randomly to collect the data. For example, in the case of the above example, the researcher can randomly choose a state of the UK where he wants to collect the research data first. Hence, these are the two types of multistage methods of sampling.
How to conduct multistage sampling?
Knowing the types and the definition of this sampling method, you must have an initial idea of conducting this method. However, there are some things that you still need to know about collecting data using this method. So, a brief description of all the steps involved in multistage sampling is as follows:
- The first step is about selecting the sampling frame considering the population size. Divide the population into groups or clusters and allocate a specific number to each group which you will use to store the data. The clusters you obtain after the division of the population are your primary sampling units (PSUs).
- The second step involves further grouping of the PSUs. The clusters or the groups formed as a result of this activity are your secondary sampling units (SSUs). Select the SSUs that are representative of your sampling requirements. Not every SSU is useful for your research study.
- Basically, these are the two steps involved in multistage cluster sampling. However, there could be cases in which you may need to group the population or divide it into further clusters. The final groups of clusters are called ultimate sampling units (USUs). After this, collect the data by applying the suited data collection techniques.
Pros and cons of multistage sampling
There is not a single sampling method in the literature that does not have its pros and cons. Hence, the pros and cons of this sampling method are as follows:
Pros
- It helps the researcher study a population that is too large to study in a single go by dividing it into clusters or groups.
- It is a relatively inexpensive sampling method compared to single-stage sampling when the population is geographically dispersed.
- It is a flexible method as you can change the groups and clusters as per your needs.
Cons
- As a researcher, you need a larger sample to apply this sampling method.
- You need clear reasoning for your choice of this method.
- There is always the possibility of selecting samples which do not represent the traits of the actual population.
Conclusion
Conclusively, multistage sampling allows you to study a population that is geographically dispersed or a population that is impossible to research using the single-stage method. It lets you divide the population into several groups or clusters and then study each group separately. So, it is a very useful method of sampling in academic research to study large populations. Read the article and use this method.
