Sampling is the process that helps you identify a representative sample from a large population. A sample in research saves time because it is impossible to study the broad population or the entire universe. However, the sampling method helps you select a sample which is representative of the entire population, which means that the sample has a proportionate number of traits and qualities found in the entire population. Various sampling strategies are used for sampling, and multistage sampling is one of the useful techniques for collecting a sample. This article will provide you a comprehensive overview of multistage sampling.
What is Multistage sampling?
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.
Types of Multistage sampling
Multistage sampling is divided into multistage cluster sampling and multistage random sampling. Let’s overview both types of sampling methods:
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 instance, if the researcher wants to conduct research on the voting preferences of people in the United States, it is impossible to visit every other household and collect the 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. It 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 at Dissertation Writing Services.
Multistage Random Sampling
Multistage random sampling is the second 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 instance, 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 instance, if the researcher wants to understand the study habits of university students in the United States, and 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 partake in the research.
What are the steps of doing multistage sampling?
Step 1: Select a Sampling Frame
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.
Step 2: Proceed to Select Clusters
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.
Step 3: Repetition
In some circumstances, if the categories in the initial step do not adequately represent the population, you may be required to duplicate the second stage of choosing sample frames from the subcategories. The best course of action in this situation is to repeat step 2.
Step 4: Use Probability Sampling
Use probability sampling to select the sample clusters from the identified sub-categories. 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 help from experts at Dissertation Writers UK.
Conclusion
Multistage sampling helps researchers gather data from a large population stretched across a wide geography. Researchers can conduct national or state-level research with the help of this sampling technique. It enables the researchers to select clusters at each stage and thus narrow down the scope of the sample to specific yet representative strata of the entire population. 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 study.
