Multistage sampling is a sampling method where the population divides into groups or clusters. It is a special case of cluster sampling, sometimes which known as multistage cluster sampling. In this sampling method, significant clusters of the selected people are divided into sub-groups at various stages to make it simpler for primary data collection.
If you divide the population into groups and then sample individuals within each selected group, then you have two stage sample. If groups contain smaller groups you can do a three stage sample, and so on. These are multistage sample. This sampling procedure is called multistage sampling. Its also a probability sampling.
Example-
In reallife we divide a country in severel cities then blocks then household then we collect data from the people of selected household. this is a four stage sampling.
Cities→→blocks→ households→people
Conducting procedure
There are four steps. They are as following,
- Step one: Choose a sampling frame, considering the population of interest. The researcher allocates a number to every group and selects a small sample of relevant separate groups.
- Step two: Select a sampling frame of relevant separate sub-groups. Do this from related, different discrete groups selected in the previous stage.
- Step three: Repeat the second step if necessary.
- Step four: Using some variation of probability sampling, choose the members of the sample group from the sub-groups.
Advantage of multistage sampling
- Simplification: This probability sampling is more simple than other probability sampling. In this sampling we just divide our study area in various stages and we collect data from the last stage .
- Flexibility: This sampling procedure is more flexible than other sampling. From data collection to data sorting, data cleaning all the process are flexible.
Disadvantages of multistage sampling
- Arbitrariness:
- Lost data
Application of multistage sampling
· construction of sampling frames at each lower stage becomes less costly
· cost efficiency with use of clusters at higher stages of selection
· Flexibility in choice of sampling units and methods of selection at different stages
· Contribution of different stages towards sampling variance may be estimated separately