Snowball sampling is often used in qualitative research when a population is hard-to-reach or hidden. This makes it useful for sensitive topics too.
This sampling technique is called “snowball” for this process. Like a snowball rolling downhill, the sample grows larger. Each new referral adds more people to the group.
Non-probability sampling means that researchers, or other participants, choose the sample rather than selecting it at random. Not every population member has an equal chance of being included in the study.
Techniques
Linear Snowball Sampling
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- Linear snowball sampling depends on a straight-line referral sequence, beginning with only one subject. This individual subject will provide one new referral, which is then recruited into the sample group.
- This referral will provide another new referral, and this pattern continues until the ideal sample size is reached.
Exponential Non-Discriminative Snowball Sampling
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- In exponential non-discriminative snowball sampling, the first subject recruited to the sample provides multiple referrals. Each new referral will then provide the researchers with more potential research subjects.
- This geometric chain sampling sequence continues until there are enough participants for the study.
Exponential Discriminative Snowball Sampling
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- This type of snowball sampling is very similar to exponential non-discriminative snowball sampling in that each subject provides multiple referrals.
- However, in this case, only one subject is recruited from each referral. Researchers determine which referral to recruit based on the objectives and goals of the study.
Method
- First, researchers will form an initial sample by drafting any potential subjects from a population (seeds).
- Even if only a couple of subjects are found at first, researchers will ask those subjects to recruit other individuals for the study. They recruit subjects by encouraging them to come forward on their own. Study participants will only provide specific names of recruited individuals if there is no risk of embarrassment or a violation of privacy. Otherwise, study participants do not identify any names of other potential participants.
- Current participants will continue to recruit others until the necessary sample size has been reached.
Ethics
Snowball sampling requires special approval by an Institutional Review Board (IRB), whereby the researchers must provide a valid justification for using this method.
Researchers must also take precautions to protect the privacy of potential subjects, especially if the topic is sensitive or personal, such as studies of networks of drug users or prostitutes.
In addition, each respondent has the opportunity to participate or decline. Current participants in studies using this method do not receive any compensation for providing referrals, and study participants are not required to identify any names of other potential participants.
Example Situations
Snowball sampling helps when participants are hard to find. It works well for hidden populations, such as criminals, drug dealers, or sex workers, who are difficult for researchers to access directly.
Consider a researcher studying undocumented immigrants in one city. Fear of legal repercussions and a lack of formal records make this population especially hard to reach through traditional sampling.
The researcher starts small. They contact a local organization that supports immigrants and connect with a few willing individuals through it.
These initial participants, the seeds, are then asked to refer the researcher to other undocumented immigrants they know. Each new participant refers others in turn. The sample grows through this chain of referrals, much like a snowball rolling downhill.
The method works well because participants tend to know others like themselves. Members of hidden populations are often closely connected. They share interests or belong to the same groups, so they can vouch for the study and reassure others about confidentiality.
Research Examples
- Researching non‐heterosexual women using social networks (Browne, 2005).
- Investigating lifestyles of heroin users (Kaplan, Korf, & Sterk, 1987).
- Identifying Argentinian immigrant entrepreneurs in Spain (Baltar & Brunet, 2012).
- Studying illegal drug users over the age of 40 (Waters, 2015).
- Obtaining samples of populations at risk for HIV (Kendall et al., 2008).
Advantages
Enables access to hidden populations
Snowball sampling enables researchers to conduct studies when finding participants might otherwise be challenging. Concealed individuals, such as drug users or sex workers, are difficult for researchers to access, but snowball sampling helps researchers to connect to these hidden populations.
Avoids risk
Snowball sampling requires the approval of an Institutional Review Board to ensure the study is conducted ethically. In addition, each respondent has the opportunity to participate or to decline participation.
Saves money and time
Since current subjects are used to locate other participants, researchers will invest less money and time in planning and sampling.
Limitations
Difficult to determine sampling error
Snowball sampling is a non-probability sampling method, so researchers cannot calculate the sampling error. Each participant’s chance of being included depends on who referred them, not on a known probability.
This breaks the assumption of independence between cases. Standard formulas for sampling error assume independence. Without it, there is no valid way to estimate how far the sample might depart from the true population value.
Bias is possible
Since current participants select other members for the sample, bias is likely. People tend to recommend others like themselves, from within their own social circle.
This means a snowball sample often ends up more homogeneous than the wider population it is meant to represent. The initial participants shape the whole chain that follows. A network of sociable, well-connected people will snowball very differently from a network of more isolated individuals.
Not always representative of the greater population
Because researchers are not selecting the participants themselves, they have little control over the sample. Researchers will thus have minimal knowledge as to whether the sample is representative of the target population.
Key Terms
- A sample is the participants you select from a target population (the group you are interested in) to make generalizations about. As an entire population tends to be too large to work with, a smaller group of participants must act as a representative sample.
- Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics (e.g. gender, ethnicity, socioeconomic level). In an attempt to select a representative sample and avoid sampling bias (the over-representation of one category of participant in the sample), psychologists utilize various sampling methods.
- Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.
Key Takeaways
- Chain Referral: Snowball sampling starts with a small group of participants who recruit others they know, so the sample grows through personal referral chains.
- Hidden Populations: It is especially useful for reaching populations with no sampling frame, such as drug users, undocumented migrants, and other hard-to-reach or stigmatised groups.
- Non-Probability: Because participants are not randomly selected, snowball sampling cannot support the same statistical inference as random sampling methods.
- Homogeneity Bias: Referred participants often resemble those who recruited them, since people tend to recommend others from their own social circle.
- No Sampling Error: The referral chain breaks the independence assumption behind standard statistical formulas, so researchers cannot calculate a meaningful sampling error for a snowball sample.
- Qualitative Fit: Because representativeness is not the goal, qualitative researchers judge a snowball sample by saturation: whether new referrals keep surfacing fresh information.
- Origins: The method was given its formal statistical treatment by Goodman (1961) and developed further by Biernacki and Waldorf (1981) as a tool for studying concealed populations.
Sources
Baltar, F., & Brunet, I. (2012). Social research 2.0: Virtual snowball sampling method using Facebook. Internet Research, 22 (1), 57–74. https://doi.org/10.1108/10662241211199960
Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods & Research, 10 (2), 141–163. https://doi.org/10.1177/004912418101000205
Browne, K. (2005). Snowball sampling: Using social networks to research non-heterosexual women. International Journal of Social Research Methodology, 8 (1), 47–60. https://doi.org/10.1080/1364557032000081663
Felix-Medina, M. H., & Thompson, S. K. (2004). Combining link-tracing sampling and cluster sampling to estimate the size of hidden populations. Journal of Official Statistics, 20 (1), 19–38.
Goodman, L. A. (1961). Snowball sampling. The Annals of Mathematical Statistics, 32 (1), 148–170. https://doi.org/10.1214/aoms/1177705148
