What Is Snowball Sampling? | Definition & Examples

Snowball sampling is a non-probability sampling method where new units are recruited by other units to form part of the sample. Snowball sampling can be a useful way to conduct research about people with specific traits who might otherwise be difficult to identify (e.g., people with a rare disease).

Also known as chain sampling or network sampling, snowball sampling begins with one or more study participants. It then continues on the basis of referrals from those participants. This process continues until you reach the desired sample, or a saturation point.

Example: Snowball sampling
You are studying changes over time in romantic relationships. You are relatively new to the city, so you are finding it difficult to find participants yourself. You first look for interviewees within your own circle of friends and acquaintances.

A number of criteria are used for the selection:

  • The couple must have been together for a period of at least five years.
  • The couple must live together now.
  • The couple must live within a certain geographic area.
  • The couple must have examples of changes or challenges they have experienced together (e.g., long-distance, illness or loss of a loved one).

After the first interview with a couple from your own circle, you ask them if they know of another couple who may be interested in taking part in the research. Once you have interviewed the second couple, you ask again for a referral. You continue until you have conducted interviews with 30 couples, which is your desired sample size.

When to use snowball sampling

Snowball sampling is a widely employed method in qualitative research, specifically when studying hard-to-reach populations.

These may include:

  • Populations that are small relative to the general population
  • Geographically dispersed populations
  • Populations possessing a social stigma or particular shared characteristic of interest

In all these cases, accessing members of the population can be difficult for non-members, as there is no sampling frame available.

Research in the fields of public health (e.g., drug users), public policy (e.g., undocumented immigrants), or niche genres (e.g., buskers) often uses snowball sampling.

This sampling method is also used to study sensitive topics, or topics that people may prefer not to discuss publicly. This is usually due to a perceived risk associated with self-disclosure. Snowball sampling allows you to access these populations while considering ethical issues, such as protecting their privacy and ensuring confidentiality.

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Types of snowball sampling

Snowball sampling begins with a convenience sample of one or more initial participants. Multiple data collection points (or waves) follow. These initial participants, called ‘seeds’, are used to recruit the first wave’s participants.

Wave 1 participants recruit wave 2 participants, and the sample expands, wave by wave, like a snowball growing in size as it rolls down a hill.

Depending on your research objectives, there are three different types of snowball sampling methods to choose from:

Linear snowball sampling

Linear snowball sampling relies on one referral per participant. In other words, the researcher recruits only one participant, and this participant, in turn, recruits only one participant. This process goes on until you have included enough participants in the sample.

Linear snowball sampling works best when there are few restrictions (called inclusion and exclusion criteria) as to who is included in the sample.

Example: Linear snowball sampling
Suppose you are researching people who own a tiny house. You approach someone in your circle of acquaintances who has recently bought one, and ask them about their motivation for this purchase.

As you finish up the interview, you ask them if they can refer someone else who also owns a tiny house. They happen to know someone, and pass the contact details to you. You interview them as well. Towards the end of the interview, you ask them to introduce you to one more person.

If more than two names are mentioned, it is a good idea to ask the interviewee how well they know those people, and then interview the person who is least known to them.

In this way, you can avoid remaining within the same social circles and increase the chance of including a variety of respondents in your sample.

Exponential non-discriminative snowball sampling

In exponential non-discriminative snowball sampling, the first participant provides multiple referrals. In other words, the researcher recruits the first participant, and this participant in turn recruits several others. The researcher includes all referrals in the sample. This type of snowball sampling is best used when you want to reach a larger sample.

Example: Exponential non-discriminative snowball sampling
Let’s revisit the example about the motivations of tiny house owners. As you finish up the first interview, you ask the participant for referrals.

They mention that they know of two people who also bought tiny houses a few months ago in the same area as they did. You arrange interviews with both and ask them to introduce you to other owners.

Exponential discriminative snowball sampling

In this method, participants give multiple referrals. However, the researcher screens those referrals, choosing only those who meet specific criteria to participate in the sample. The key difference between this and exponential non-discriminative snowball sampling is that not all referrals are included in the sample.

Exponential discriminative snowball sampling is most used when screening participants according to specific criteria is vital to your research goals.

Example: Exponential discriminative snowball sampling
In researching the motivations of tiny house owners, you decide you only want to focus on those who bought one in the past three years.

As you inquire with your acquaintances, you find someone who bought a tiny house a year ago. At the end of the interview, you ask them if they know of other owners. You do not specify that the purchase has to be in the past three years.

As it happens, they do know of two more people who bought tiny houses in the same area as they did. You contact both, and find out that one bought the house four years ago and the other eight months ago. Since the one who bought the house four years ago does not meet your criteria, you only interview the other.

You repeat the same process, asking for referrals and screening them until you have enough participants.

Advantages and disadvantages of snowball sampling

Like all research methods, snowball sampling has distinct advantages and disadvantages. It is important to be aware of these in order to determine whether it’s the best approach for your research design.

  • Advantages of snowball sampling

Depending on your research goals, there are advantages to using snowball sampling.

  • Snowball sampling helps you research populations that you would not be able to access otherwise. Members of stigmatised groups (e.g., people experiencing homelessness) may hesitate to participate in a research study due to fear of exposure. Snowball sampling helps in this situation, as participants refer others whom they know and trust to the researcher.
  • Since snowball sampling involves individuals recruiting other individuals, it is low-cost and easy to recruit a sample in this way.
  • Unlike probability sampling, where you draw your sample following specific rules and some form of random selection, snowball sampling is flexible. All you need is to identify someone who is willing to participate and introduce you to others.
  • Disadvantages of convenience sampling

Snowball sampling has disadvantages, too, and is not a good fit for every research design.

  • As the sample is not chosen through random selection, it is not representative of the population being studied. This means that you cannot make statistical inferences about the entire population.
  • The researcher has little or no control over the sampling process and relies mainly on referrals from already-identified participants. Since people refer others whom they know (and share traits with), this sampling method can have a potential sampling bias or selection bias
  • Relying on referrals may lead to difficulty reaching your sample. People may not want to cooperate with you, hesitate to reveal their identities, or mistrust researchers in general.

Frequently asked questions about snowball sampling

When would it be appropriate to use a snowball sampling technique?

Snowball sampling is best used in the following cases:

  • If there is no sampling frame available (e.g., people with a rare disease)
  • If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
  • If the research focuses on a sensitive topic (e.g., extra-marital affairs)
Is snowball sampling biased?

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias.

Is snowball sampling quantitative or qualitative?

Snowball sampling is a non-probability sampling method, where there is not an equal chance for every member of the population to be included in the sample.

This means that you cannot use inferential statistics and make generalisations often the goal of quantitative research. As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research.

Is snowball sampling random?

Snowball sampling is a non-probability sampling method. Unlike probability sampling (which involves some form of random selection), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

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Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students. She specialises in writing about research methods and research bias.