Snowball sampling is a sampling technique where we use the existing people in our sample to recruit more people in order to increase the sample size. This is like a snowball that on rolling down the slope collects more snow and keeps growing bigger and bigger. This explains why the method is called snowball sampling.
How is a sample chosen using the snowball sampling method?
To conduct snowball sampling the researcher asks the people currently in the sample to suggest more names to add to the sample.
For example, if a researcher wanted to conduct a study of members of a particular reading circle he would first find one member and then ask that member to suggest more names. This is because that member knows his fellow members of the reading circle, whose identities the researcher does not know about.
This is why snowball sampling is also called cross-referral sampling because it involves one sample unit referring the researcher towards other sample units. This is done until the sample size is large enough for the researcher’s satisfaction.
Advantages of Snowball Sampling:
- This method of sampling can be used to study populations that are rare and hard to find.
- The researcher can use his judgment to choose the people to include in the sample depending on whether he feels they would be useful for the study.
Disadvantages of Snowball Sampling:
- Since this is a non-probabilistic sampling method, the bias of the researcher might affect the study because the choice of sample units depends on the researcher. It is better to use probabilistic sampling methods such as stratified random sampling and cluster sampling to eliminate researcher bias.
- The sample size for the study cannot be fixed in advance as it depends on whether the people already in the sample can recommend more people or not.
- This method is time-consuming since it involves asking every person in the sample for referrals which takes a lot of time.