On This Page:ToggleTechniquesMethodEthicsExample SituationsAdvantagesLimitationsKey Terms
On This Page:Toggle
On This Page:
Snowball sampling, also known as chain-referral sampling, is a non-probability sampling method where currently enrolled research participants help recruit future subjects for a study.Snowball sampling is often used inqualitative researchwhen the population is hard-to-reach or hidden. It’s particularly useful when studying sensitive topics or when the members of a population are difficult to locate.
Snowball sampling, also known as chain-referral sampling, is a non-probability sampling method where currently enrolled research participants help recruit future subjects for a study.
Snowball sampling is often used inqualitative researchwhen the population is hard-to-reach or hidden. It’s particularly useful when studying sensitive topics or when the members of a population are difficult to locate.
The process starts with a small group of initial respondents (seeds). These initial respondents then refer the researcher to other potential respondents they know within the target population. Those respondents then refer the researcher to others, and so on. This process continues until the desired sample size is reached.
Thissampling techniqueis called “snowball” because the sample group grows like a rolling snowball.
Non-probability sampling means that researchers, or other participants, choose the sample instead of randomly selecting it, so not all population members have an equal chance of being selected for the study.
TechniquesLinear Snowball SamplingLinear 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 SamplingIn 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 SamplingThis 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.
Techniques
Linear Snowball Sampling
Exponential Non-Discriminative Snowball Sampling
Exponential Discriminative Snowball Sampling
Method
Ethics
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 is used when researchers have difficulty finding participants for their studies. This typically occurs in studies on hidden populations, such as criminals, drug dealers, or sex workers, as these individuals are difficult for researchers to access.
For example, a researcher studying the experiences of undocumented immigrants in a particular city. This population might be difficult to reach through traditional sampling methods due to fear of legal repercussions, lack of formal records, and other barriers.
These initial participants (the “seeds”) would then be asked to refer the researcher to other undocumented immigrants they know who might also be willing to participate.
The new participants would then refer the researcher to others, and so on, creating a “snowball” effect where the number of participants grows as each person refers the researcher to others in their network.
The snowball sampling method is beneficial because current participants are likely to know others who share similar characteristics relevant to the study.
Members of these hidden populations tend to be closely connected as they share interests or are involved in the same groups, and they can inform others about the benefits of the study and reassure them of confidentiality.
Research Examples
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.
Bias is possible
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
Sources
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-STOCKHOLM-,20(1), 19-38.
Henderson, R. H., & Sundaresan, T. (1982). Cluster sampling to assess immunization coverage: a review of experience with a simplified sampling method.Bulletin of the World Health Organization,60(2), 253–260.
Malilay, J., Flanders, W. D., & Brogan, D. (1996). A modified cluster-sampling method for post-disaster rapid assessment of needs.Bulletin of the World Health Organization,74(4), 399–405.
Roesch, F. A. (1993). Adaptive cluster sampling for forest inventories.Forest Science,39(4), 655-669.
Smith, D. R., Conroy, M. J., & Brakhage, D. H. (1995). Efficiency of Adaptive Cluster Sampling for Estimating Density of Wintering Waterfowl.Biometrics,51(2), 777–788. https://doi.org/10.2307/2532964
Steven K. Thompson (1990) Adaptive Cluster Sampling, Journal of the American Statistical Association, 85:412,1050-1059, DOI:10.1080/01621459.1990.10474975
Saul McLeod, PhD
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Olivia Guy-Evans, MSc
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
Julia Simkus
BA (Hons) Psychology, Princeton University
Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master’s Degree in Counseling for Mental Health and Wellness in September 2023. Julia’s research has been published in peer reviewed journals.
