Multi-stage sampling is a complex form of cluster sampling which contains two or more stages in sample selection. In simple terms, in multi-stage sampling large clusters of population are divided into smaller clusters in several stages in order to make primary data collection more manageable. It has to be acknowledged that multi-stage sampling is not as effective as true random sampling, however, it addresses certain disadvantages associated with true random sampling such as being overly expensive and time-consuming.
Contrary to its name, multi-stage sampling can be easy to apply in business studies.
The following is an example of implementation of multi-stage sampling method once a state has been chosen as cluster sampling:
- Random number of districts within the state need to be selected as primary clusters.
- Random number of villages within district need to be selected as secondary clusters.
- Ultimately a number of houses need to be selected as sampling unit to be used in the study.
Multi-stage sampling offers the following advantages:
- Simplification of random sampling method
- High level of flexibility
The following points represent disadvantages of this sampling method:
- High level of subjectivity
- Research findings can never be 100% representative of population