Multistage sampling Statistics Basic Sampling Techniques

Statistics

1. STAT – 512 Basic Sampling Techniques Multistage sampling Submitted by, Dharsika R L, MFSc 1 st year.

2. Introduction Multistage sampling, also called multistage cluster sampling , is exactly what it sounds like – sampling in stages. It is a more complex form of cluster sampling, in which smaller groups are successively selected from large populations to form the sample population. A combination of stratified sampling or cluster sampling and simple random sampling is usually used.

3. Due to this multi-step nature, the sampling method is sometimes referred to as phase sampling. Multistage sampling is often considered an extended version of cluster sampling. This method is useful for collecting data from large, dispersed populations. In multistage sampling, you divide the population into clusters and select some clusters at the first stage. At each subsequent stage, you further divide up those selected clusters into smaller clusters, and repeat the process until you get to the last step. At the last step, you only select some members of each cluster for your sample.

4. Steps involved in Multistage sampling In multistage sampling, you always go from higher-level to lower-level clusters at each stage. The clusters are often referred to as sampling units. At the first stage, you divide up the population into clusters and select some of them: these are your primary sampling units (PSUs). At the second stage, you divide up your PSUs into further clusters, and select some of them as your secondary sampling units (SSUs). You can end at the second stage, or continue this process with as many stages as you need. In the last stage, you’ll get to your final sample of ultimate sampling units (USUs).

5. Example

6. Types of multistage sampling Multistage random sampling The concept of multistage random sampling technique is similar to multistage cluster sampling. But in this case, the researcher chooses the samples randomly at each stage. Here, the researcher does not create clusters, but narrows down the convenience sample by applying random sampling. Multistage cluster sampling Multistage cluster sampling is a complex type of cluster sampling. The researcher divides the population into groups at various stages for better data collection, management, and interpretation. These groups are called clusters. 

7. Advantages You don’t need to start with a sampling frame of your target population. Compared to a simple random sample, it’s relatively inexpensive and effective when you have a large or geographically dispersed population. It’s flexible—you can vary sampling methods between stages based on what’s appropriate or feasible.

8. Disadvantages Multistage sampling has a high level of subjectivity in its process. 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. Multistage sampling also requires information from many group levels; otherwise, the procedure will not be successful.

9. 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.