Snowball Sampling: Definition, Method of Applications, Advantages and Disadvantages


Snowball sampling is a popular business study method. The snowball sampling method is extensively used where a population is unknown and rare and it is tough to choose subjects to assemble them as samples for research.

Snowball Sampling

Snowball sampling or chain-referral sampling is defined as a a non-probability sampling technique in which the samples have traits that are rare to find. This is a sampling technique in which existing subjects provide referrals to recruit samples required for a research study.

For example, if you are studying the level of customer satisfaction among the members of an elite country club, you will find it extremely difficult to collect primary data sources unless a member of the club agrees to have a direct conversation with you and provides the contact details of the other members of the club.

This sampling method involves a primary data source nominating other potential data sources that will be able to participate in the research studies. Snowball sampling method is purely based on referrals and that is how a researcher is able to generate a sample. Therefore this method is also called the chain-referral sampling method.

Snowball sampling is a popular business study method. The snowball sampling method is extensively used where a population is unknown and rare and it is tough to choose subjects to assemble them as samples for research.

This sampling technique can go on and on, just like a snowball increasing in size (in this case the sample size) till the time a researcher has enough data to analyse to draw conclusive results that can help an organization make informed decisions.

Types of Snowball Sampling

Linear Snowball Sampling: The formation of a sample group starts with one individual subject providing information about just one other subject and then the chain continues with only one referral from one subject. This pattern is continued until enough number of subjects are available for the sample.

Exponential Non-Discriminative Snowball Sampling: In this type, the first subject is recruited and then he/she provides multiple referrals. Each new referral then provides with more data for referral and so on, until there is enough number of subjects for the sample.

Exponential Discriminative Snowball Sampling: In this technique, each subject gives multiple referrals, however, only one subject is recruited from each referral. The choice of a new subject depends on the nature of the research study.

Snowball Sampling Applications

Snowball sampling is usually used in cases where there is no pre calculated list of target population details (homeless people), there is immense pain involved in contacting members of the target population (victims of rare diseases) , members of the target population are not inclined towards contributing due to a social stigma attached to them (hate-crime, rape or sexual abuse victims, sexuality, etc.) or the confidentiality of the organization respondents work for (CIA, FBI or terrorist organization).

Thus, this type of sampling is preferred in the following applications:

Medical Practices: There are many less-researched diseases. There may be a restricted number of individuals suffering from diseases such as progeria, porphyria, Alice in Wonderland syndrome etc. Using snowball sampling, researchers can get in touch with these hard to contact sufferers and convince them to participate in the survey research.

Social research: Social research is a field which requires as many participants as possible as it is a process where scientists learn about their target sample. When social research is to be conducted in domains where participants might not necessarily willing to contribute such as homeless or the less-fortunate people.

Cases of discord: In case of disputes such as an act of terrorism, violation of civil rights and other similar situations, the individuals involved may oppose giving their statements for evidential purposes. The researchers or management can use snowball sampling, to filter out those people from a population who are most likely to have caused the situation or are witness to the event to gather proof around the event.

Snowball Sampling Examples

For some population, snowball sampling is the only way of collecting data and meaningful information. Following are the instances, where snowball sampling can be used:

No official list of names of the members: This sampling technique can be used for a population, where there is no easily available data like their demographic information. For example, homeless or list of members of an elite club, whose personal details cannot be obtained easily.

Difficulty to locate people: People with rare diseases are quite difficult to locate. However, if a researcher is carrying out a research study similar in nature, finding the primary data source can be a challenge. Once he/she is identified, they usually have information about more such similar individuals.

People who are not willing to be identified: If a researcher is carrying out a study which involves collecting information/data from sex workers or victims of sexual assault or individuals who don’t want to disclose their sexual orientations, these individuals will fall under this category.

Secretiveness about their identity: People who belong to a cult or are religious extremists or hackers usually fall under this category. A researcher will have to use snowball sampling to identify these individuals and extract information from them.

Advantages of Snowball Sampling

  • The ability to recruit hidden populations
  • The possibility to collect primary data in a cost-effective manner
  • Studies with snowball sampling can be completed in a short duration of time
  • A very little planning is required to start primary data collection process

Disadvantages of Snowball Sampling

  • Oversampling a particular network of peers can lead to bias
  • Respondents may be hesitant to provide names of peers and asking them to do so may raise ethical concerns
  • There is no guarantee about the representativeness of samples. It is not possible to determine the actual pattern of distribution of population.
  • It is not possible to determine the sampling error and make statistical inferences from the sample to the population due to the absence of random selection of samples.

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