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Snowball Sampling Method: Techniques & Examples

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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 in qualitative research when 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.

This sampling technique is 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.

Linear Snowball Sampling

• 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

• 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

• 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.
• 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.

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 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.

The researcher might start by contacting a local organization that provides services to immigrants. Through this organization, the researcher could connect with a few willing individuals to participate in the study.

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

• Researching non‐heterosexual women using social networks (Browne, 2002).
• 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).
• Assess the prevalence of irritable bowel syndrome in South China and its impact on health-related quality of life (Xiong, 2004).
• Obtaining samples of populations at risk for HIV (Kendall et al., 2008).

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

Since current participants select other members for the sample, bias is likely. The initial participants will strongly impact the rest of the sample. In addition, an individual who is well-known and sociable is more strongly impacted by one who is more introverted.

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.

• 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.

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

Xiong, L. S., Chen, M. H., Chen, H. X., Xu, A. G., Wang, W. A., & Hu, P. J. (2004). A population‐based epidemiologic study of irritable bowel syndrome in South China: stratified randomized study by cluster sampling. Alimentary pharmacology & therapeutics , 19 (11), 1217-1224.

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Snowball Sampling – Method, Types and Examples

Table of Contents

Snowball Sampling

Definition:

Snowball sampling is a non-probability sampling technique in which participants are recruited through referrals from other participants. The idea behind snowball sampling is to start with a small group of participants, often referred to as “seeds,” and then have them refer other people they know who meet the study’s eligibility criteria.

Types of Snowball Sampling

Types of Snowball Sampling are as follows:

• Linear snowball sampling : In linear snowball sampling, each participant is asked to identify only one additional participant, and the process stops once the desired sample size is reached. This method is useful when the population of interest is small, and the researcher wants to ensure that each participant has an equal chance of being selected.
• Exponential non-discriminative snowball sampling : In exponential non-discriminative snowball sampling, each participant is asked to identify multiple individuals, but there is no restriction on the number of individuals they can identify. This method is useful when the population of interest is large, and the researcher wants to increase the sample size quickly.
• Exponential discriminative snowball sampling : In exponential discriminative snowball sampling, participants are asked to identify individuals who meet specific criteria. For example, if the researcher is interested in studying individuals who have a particular medical condition, participants are asked to identify individuals who have that condition. This method is useful when the population of interest is rare or difficult to identify, and the researcher wants to ensure that the sample is representative of that population.
• Network-based snowball sampling : In network-based snowball sampling, participants are selected based on their connections to other individuals. For example, if the researcher is interested in studying drug use among adolescents, they might start with a few individuals who are known to use drugs and ask them to identify other individuals in their social network who also use drugs. This method is useful when the population of interest is connected in some way, such as through social networks or communities.
• Time-location-based snowball sampling: In time-location-based snowball sampling, participants are selected based on their location at a particular time. For example, if the researcher is interested in studying the experiences of homeless individuals, they might start by visiting a particular location where homeless individuals are known to gather and ask them to identify other homeless individuals who might be willing to participate. This method is useful when the population of interest is difficult to reach through other means.
• Maximum variation snowball sampling: In maximum variation snowball sampling, participants are selected to represent a broad range of characteristics or experiences. For example, if the researcher is interested in studying the experiences of individuals with mental illness, they might select participants who have different diagnoses, are at different stages of recovery, and have different levels of support. This method is useful when the researcher wants to capture the diversity of experiences within a particular population.
• Criterion-based snowball sampling: In criterion-based snowball sampling, participants are selected based on certain criteria, such as age, gender, or occupation. For example, if the researcher is interested in studying the experiences of female healthcare workers during the COVID-19 pandemic, they might start by identifying a few female healthcare workers and ask them to identify other female healthcare workers who are also working during the pandemic. This method is useful when the researcher wants to study a specific subgroup within a larger population.
• Volunteer snowball sampling : In volunteer snowball sampling, participants are recruited through existing networks or organizations, such as online forums or community groups. For example, if the researcher is interested in studying the experiences of individuals with a rare medical condition, they might reach out to patient advocacy groups or online support groups to recruit participants. This method is useful when the researcher wants to reach a specific population that is difficult to access through other means.
• Respondent-driven sampling : Respondent-driven sampling (RDS) is a variant of snowball sampling that is often used to study hard-to-reach populations, such as individuals who use drugs or engage in high-risk behaviors. In RDS, participants are given incentives to recruit other participants, and the sample is weighted to account for the biases that can occur in snowball sampling. This method is useful when the researcher wants to obtain a representative sample of a hard-to-reach population.

Snowball Sampling Method

In this sampling method, the researcher starts with a small group of individuals who are already known to have some characteristics of interest, and then asks them to identify others who share those same characteristics. This process of expanding the sample through referrals continues until the desired sample size is reached.

The snowball sampling method is often used when the population of interest is small or hidden, or when there is a lack of comprehensive sampling frames. For example, it can be used to study populations of drug users, homeless individuals, or people engaged in illegal activities. It is also useful when the researcher is studying a rare phenomenon or a group that is difficult to access, such as people with a specific medical condition.

How to Conduct Snowball Sampling

Here are the steps to conduct snowball sampling:

• Identify your initial participants : Identify a small group of participants who fit the criteria for your research. They should be willing and able to refer others to participate in the study.
• Ask for referrals: Ask your initial participants to refer others who may be interested in participating in the study. Encourage them to reach out to their social networks and spread the word.
• Screen the referrals : Screen the referred participants to ensure that they meet the criteria for your study. If they do, invite them to participate.
• Repeat the process : After the referred participants have completed the study, ask them to refer others to participate. Repeat the process until you have reached your desired sample size.
• Analyze the data : Once you have collected data from your participants, analyze it to draw conclusions and insights.

Examples of Snowball Sampling

Here are some examples of how snowball sampling can be used in different research contexts:

• Studying stigmatized groups: Researchers who want to study stigmatized groups, such as people living with HIV/AIDS or members of the LGBTQ+ community, may use snowball sampling to identify participants. In this case, the initial participants may be recruited through outreach programs or community centers, and they may refer others who they know are also part of the community.
• Exploring hidden populations: Researchers who want to study populations that are difficult to access, such as drug users or sex workers, may also use snowball sampling. In this case, the initial participants may be recruited through outreach programs or contacts in the community, and they may refer others who they know are also part of the population.
• Conducting market research: Snowball sampling can also be used in market research to identify potential customers or clients. In this case, the initial participants may be recruited through social media or online forums, and they may refer others who they know are also interested in the product or service being offered.
• Collecting historical data: Snowball sampling can also be used to collect historical data about a particular community or event. For example, researchers may use snowball sampling to identify and interview survivors of a natural disaster, political conflict, or war.

Applications of Snowball Sampling

Snowball sampling can be applied in various research contexts, particularly in studies that aim to explore hard-to-reach populations or phenomena. Here are some common applications of snowball sampling:

• Studying hidden or stigmatized populations : Snowball sampling can be used to recruit participants from populations that may be difficult to reach through traditional sampling methods, such as drug users, sex workers, or refugees. This method can help researchers gain insights into the experiences and perspectives of these populations.
• Exploring social networks: Snowball sampling can be used to explore social networks by asking participants to refer others who they know. This method can help researchers understand how social networks operate and how they influence individuals’ attitudes and behaviors.
• Collecting historical data: Snowball sampling can be used to collect historical data by identifying individuals who have experienced a particular event or phenomenon. This method can help researchers gain insights into the long-term effects of historical events on individuals and communities.
• Conducting market research : Snowball sampling can be used to recruit participants for market research studies. This method can help researchers identify potential customers or clients who are interested in a particular product or service.
• Investigating rare phenomena: Snowball sampling can be used to study rare phenomena or behaviors that occur in specific populations. For example, researchers may use snowball sampling to identify individuals who have experienced a rare medical condition or who engage in a particular type of behavior.

When to use Snowball Sampling

Here are some situations where snowball sampling may be a suitable approach:

• Studying hard-to-reach populations : Snowball sampling can be used to study populations that may be difficult to access through traditional sampling methods, such as refugees, homeless individuals, or people living with HIV/AIDS. This method can help researchers gain insights into the experiences and perspectives of these populations.
• Exploring sensitive topics : Snowball sampling can be used to explore sensitive topics that individuals may not want to discuss with strangers. For example, researchers may use snowball sampling to study experiences of sexual assault or domestic violence.
• Collecting data on rare phenomena : Snowball sampling can be used to study rare phenomena or behaviors that occur in specific populations. For example, researchers may use snowball sampling to identify individuals who have experienced a rare medical condition or who engage in a particular type of behavior.
• Conducting exploratory research: Snowball sampling can be used in exploratory research when the goal is to identify new themes or areas of inquiry. This method can help researchers identify potential participants who can provide insights into the research question.
• Conducting research with limited resources : Snowball sampling can be a cost-effective method for conducting research with limited resources. Since participants are recruited through referrals, researchers may not need to spend resources on advertising or recruiting participants.

Purpose of Snowball Sampling

The purpose of snowball sampling is to identify and recruit participants for a research study when traditional sampling methods are not feasible or appropriate. Snowball sampling involves asking initial participants to refer others who they know and who meet the criteria for the study, which creates a “snowball” effect as the sample size grows.

The purpose of snowball sampling is to gain insights into populations or phenomena that may be difficult to access through traditional sampling methods. This method is often used to study hard-to-reach or stigmatized populations, such as drug users, workers, or refugees, who may be hesitant to participate in research studies. Snowball sampling can also be used to study rare phenomena or behaviors that occur in specific populations.

Snowball sampling is a useful research tool when the research question requires a non-random sample, and when the population of interest is small or hard to reach. However, researchers must be mindful of the potential biases that can arise from participant referrals, and take steps to minimize them. The purpose of snowball sampling is to identify a diverse range of participants who can provide valuable insights into the research question, while also maintaining the ethical principles of informed consent, confidentiality, and protection from harm.

Characteristics of Snowball Sampling

Here are some characteristics of snowball sampling:

• Non-random sampling: Snowball sampling is a non-random sampling technique, which means that participants are not selected at random from a population. Instead, participants are recruited based on their connection to the initial participants or through referrals.
• Recruitment through referrals : Snowball sampling relies on referrals from initial participants to recruit additional participants for the study. Participants are asked to refer others who they know and who meet the criteria for the study, creating a “snowball” effect as the sample size grows.
• Sampling bias: Snowball sampling can be prone to sampling bias since the sample is not randomly selected from the population of interest. Participants may be more likely to refer others who share similar characteristics or experiences, leading to a non-representative sample.
• Limited generalizability : The findings of studies that use snowball sampling may have limited generalizability to the population of interest, as the sample may not be representative of the population.
• Useful for hard-to-reach populations: Snowball sampling can be a useful technique for recruiting participants from hard-to-reach populations, such as individuals with a rare disease or people who engage in stigmatized behaviors.
• Ethical considerations: Researchers using snowball sampling must take steps to ensure that participants are fully informed about the study, their rights, and the potential risks and benefits of participating. Researchers must also take steps to protect participant confidentiality and minimize any potential harm.

Advantages of Snowball Sampling

Here are some advantages of snowball sampling:

• Access to hard-to-reach populations: Snowball sampling is useful for accessing hard-to-reach populations that may be difficult to recruit through traditional sampling methods. For example, individuals who engage in stigmatized behaviors, such as drug use or sex work, may be more likely to participate in a study if they are referred by someone they trust.
• Cost-effective: Snowball sampling can be a cost-effective method for recruiting participants since it relies on referrals from initial participants rather than costly advertising or recruitment efforts.
• High levels of rapport and trust : Snowball sampling can result in high levels of rapport and trust between the researcher and the participants. Participants may feel more comfortable sharing personal or sensitive information with a researcher who has been referred by someone they know and trust.
• Can generate rich data: Snowball sampling can generate rich data since participants are often highly engaged and willing to share their experiences and perspectives. Participants may also provide detailed information about their social networks, which can be valuable for understanding social dynamics and relationships.
• Flexible : Snowball sampling is a flexible research method that can be adapted to the needs of the study. Researchers can use different strategies for identifying initial participants and can adjust the recruitment process as the study progresses.

Limitations of Snowball Sampling

Here are some limitations of snowball sampling:

• Sampling bias : Snowball sampling is susceptible to sampling bias because participants are not selected at random from the population of interest. Participants may be more likely to refer others who share similar characteristics or experiences, leading to a non-representative sample.
• Limited generalizability : The findings of studies that use snowball sampling may have limited generalizability to the population of interest because the sample may not be representative. Therefore, caution should be taken when generalizing the results of a study that uses snowball sampling to other populations.
• Difficulty in controlling sample size: Snowball sampling can result in unpredictable sample sizes, making it difficult to plan for sample size calculations or statistical power.
• Limited access to initial participants: Snowball sampling relies on the initial participants to identify and refer additional participants. However, if initial participants are difficult to access or unwilling to participate, the recruitment process may stall.
• Ethical considerations: Researchers using snowball sampling must take steps to ensure that participants are fully informed about the study, their rights, and the potential risks and benefits of participating. They must also take steps to protect participant confidentiality and minimize any potential harm.

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Muhammad Hassan

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SNOWBALL VERSUS RESPONDENT-DRIVEN SAMPLING

Douglas d. heckathorn.

* Cornell University

Leo Goodman (2011) provided a useful service with his clarification of the differences among snowball sampling as originally introduced by Coleman (1958–1959) and Goodman (1961) as a means for studying the structure of social networks; snowball sampling as a convenience method for studying hard-to-reach populations ( Biernacki and Waldorf 1981 ); and respondent-driven sampling (RDS), a sampling method with good estimability for studying hard-to-reach populations ( Heckathorn 1997 , 2002 , 2007 ; Salganik and Heckathorn 2004 ; Volz and Heckathorn 2008 ).

This comment offers a clarification of a related set of issues. One is confusion between the latter form of snowball sampling, and RDS. A second is confusion resulting from multiple forms of the RDS estimator that derives from the incremental manner in which the method was developed. This comment summarizes the development of the method, distinguishing among seven forms of the estimator.

1. SNOWBALL SAMPLING

As described in Leo Goodman’s (2011) comment, snowball sampling was developed by Coleman (1958–1959) and Goodman (1961) as a means for studying the structure of social networks. Several years after Coleman’s and Goodman’s development of snowball sampling, what was also termed snowball sampling emerged as a nonprobability approach to sampling design and inference in hard-to-reach, or equivalently, hidden populations. Sampling these populations is difficult because standard statistical sampling methods require a list of population members (i.e., a “sampling frame”) from which the sample can be drawn. Yet for a hidden population, constructing the frame using methods such as household surveys is infeasible when the population is small relative to the general population, geographically dispersed, and when population membership involves stigma or the group has networks that are difficult for outsiders to penetrate ( Sudman and Kalton 1986 ). Groups with these characteristics are relevant to research in many areas, including public health (e.g., drug users), public policy (e.g., illegal immigrants), and arts and culture (e.g., musicians).

This nonprobability form of snowball sampling became a widely employed method in qualitative research on hard-to-reach populations. In a review article, Biernacki and Waldorf (1981) observed that beginning with Becker’s (1963) study of marijuana smokers; snowball sampling had become both a standard technique in qualitative research, and a topic in methodology textbooks. In this literature, “chain-referral sampling” served as the general term for a class of sampling methods including not only snowball sampling, but also link-tracing designs, Klovdahl’s “random walk” approach, and others.

Chain-referral-sampling of a hidden population begins with a convenience sample of initial subjects, because if a random sample could be drawn, the population would not qualify as hidden. These initial subjects serve as “seeds,” through which wave 1 subjects are recruited; wave 1 subjects in turn recruit wave 2 subjects; and the sample subsequently expands wave by wave like a snowball growing in size as it rolls down a hill.

In snowball studies, sample proportions are used to estimate terms such as HIV prevalence. In a typical but disappointingly common pattern, the introduction explains the limits of inference from a convenience sample, then results are presented objectively, and finally the discussion section then analyzes group differences as though these comparisons were based on a probability sample.

This transformation of the snowball method did not go without comment. Spreen (1992 :41) noted that “the historical purpose that Coleman had in mind … of using a snowball design to study social structure changed into a total [sic] different purpose of using some kind of snowball design, namely as an expedient for locating members of a special population.” Thus, the transition from snowball sampling for studying network structure to snowball sampling as a convenience sampling method fit the needs of scholars whose exclusive concern was accessing hidden populations.

2. THE EMERGENCE OF RESPONDENT-DRIVEN SAMPLING

The use of snowball sampling in research on hidden populations created a widespread perception of snowball sampling in particular and chain-referral methods in general as convenience sampling methods. Erickson’s (1979) article on problems of inference from chain data from hidden populations expressed the common wisdom. As she states, the sample begins with a convenience sample with bias of unknown magnitude and unknown direction and this bias is then compounded in unknown ways as the sample expands from wave to wave. Hence, as applied to hidden populations, chain-referral samples are inherently limited to convenience samples.

The judgment that chain-referral sampling is a convenience method was challenged in a series of papers leading to the development of a new method for collecting and analyzing chain-referral data, Respondent-driven sampling (RDS). This method was initially developed as part of a NIH/NIDA-funded HIV prevention project in Connecticut ( Heckathorn 1997 ), and has now emerged as a rapidly growing area of research with contributions from numerous independent groups of researchers (e.g., see Goel and Salganik 2009 ; Gile and Handcock 2010 ).

The method evolved incrementally in a series of papers which expanded and strengthened the method. Because a different form of the method was presented in each paper, the term RDS refers not to a single method, but to a series of methods that have as their common core an effort to convert chain-referral sampling into a sampling method of good estimability. Table1 summarizes this sequential development. It lists the seven estimators that have appeared in the peer-review literature. Designated E#1 to E#7, the table lists the information required to calculate each estimator, its limitations, and the distinctive contribution offered by each.

Evolution of RDS Population Estimators

The initial paper ( Heckathorn 1997 ) employed a Markov modeling of the peer recruitment process to show that, contrary to the conventional wisdom, bias from the convenience sample of initial subjects was progressively attenuated as the sample expanded wave by wave. This model employed data from peer recruitments to estimate the probability of recruitment across groups. These probabilities were organized into a recruitment matrix, specifying the probability of members of each group (e.g., Hispanics), recruiting members from their own and each other group (e.g., Whites, Blacks, Hispanics, and Others); probabilities that served as the “transition probabilities” of the Markov model. The model showed that as the sample expanded wave by wave, it approached an equilibrium that was independent of the starting point— that is, it was independent of the convenience sample of seeds from which it began. The implication was that this sampling method could potentially become reliable if the number of waves was sufficiently large. That is, any selection of seeds would ultimately produce the same equilibrium sample composition. Hence, it does not matter if the initial sample is nonrandom, if the number of waves reaches a threshold value large enough to eliminate bias from the initial selection of seeds. Furthermore, the analysis showed that bias from the seeds was reduced at a geometric rather than an arithmetic rate, a feature that accelerates the reduction of bias.

Using the Markov equilibrium as the population estimator (E#1), the initial ( Heckathorn 1997 ) RDS paper showed that the sample became self-weighting (see Theorem 3, p. 192) if groups had equal homophily, where homophily was as defined by either the in-group bias estimator in Coleman’s (1958–1959) relational analysis, or equivalently, the in-breeding bias estimator in Rapport’s (1979) biased network theory. Though independently developed, both approaches to defining homophily are based on the idea that structure in a network, i.e., homophily, occurs when affiliation patterns fit depart from random mixing, such that affiliations are formed in ways that reflect respondent characteristics such as SES, age, or religion.

This analysis demonstrated that, under certain conditions, population estimates derived from a chain-referral sample could become not merely reliable but also valid. However, the paper did not show how an unbiased estimate could be derived for cases where homophily was unequal.

These analyses ( Heckathorn 1997 ) also revealed a significant constraint on the method:

When inbreeding terms are very large, reflecting mutual isolation of the system's groups, the approach to equilibrium slows, e.g., when the terms exceed .99, scores of waves of recruitment may be required for equilibrium to be approximated. Therefore, given the practical limitations that constrain the number of waves of recruitment, this means that equilibrium will be reached only when inbreeding is not extreme. The implication is that when the boundaries separating groups are virtually impassable, RDS should be used to draw samples from within such groups, and not across them, even should inbreeding terms prove to be equal.

Such a possibility was illustrated in the paper. The analysis focused on a pair of cities (i.e., cities #2 and #3) with more than 99% recruitment from within each city and its adjoining area. To render the analyses tractable, the sample was divided into two subsamples, one for each city.

A subsequent paper ( Heckathorn 2002 ) introduced new RDS population estimators (E#2 and E#3). Drawing not only on the data from the recruitment matrix but also from self-reported network sizes, the estimators compensated both for differences in homophily across groups, and for differences in the mean degree (i.e., personal network size) across groups. This was accomplished through what was termed the “reciprocity model.” The essential idea was that respondents recruit acquaintances, friends, and relatives, so their relationships tend to be reciprocal. Therefore, the number of ties linking any two groups must be the same in both directions—for example, monogamous marriage is a reciprocal relationship, so for any two groups, X and Y, the number of Xs married to Ys must equal the number of Ys married to Xs. From this elemental network property, proportional group sizes can be calculated based on two types of network information, the proportion of cross-cutting ties between the groups, and the relative sizes of each group’s networks. Drawing the former information from the recruitment matrix, and the latter from the respondents’ self-reported network size, the new estimators were calculated based on these two types of network information (see Heckathorn 2002 ;22). Given that this controlled for the effects of differences in homophily and network size across groups, these estimators became validly applicable across the full range of RDS data sets in which these two network attributes are generally different.

Two versions of the estimator (E#2 and E#3) were introduced because of an issue arising when analyzing three-category and larger groups. Whereas in a dichotomous system, the estimator consisted of a single equation, nondichotomous systems were more complex. For calculating population estimates, using the reciprocity model required solving systems of linear equations that were overdetermined—for example, a four-category system required solving a system of seven linear equations with four unknowns. The problem was that, owing to stochastic variation and other factors, differing estimates would be generated based on which four equations were chosen to calculate the four unknowns. A standard approach to solving such systems is linear least squares, which uses a regression-like logic to reconcile differences among the equations (E#2). An alternative based on data smoothing was also introduced (E#3), and the paper concluded future research would be required to identify the best approach.

Subsequent analyses ( Heckathorn 2007 ) showed that data smoothing (E#3) yielded greater statistical efficiency (i.e., about 20% narrower confidence intervals). The reason is that data smoothing pooled cross-group recruitment data to estimate a reduced number of parameters, so each estimate was based on a greater amount of data.

The paper ( Heckathorn 2002 :27–28) also showed how confidence intervals for RDS population estimates can be generated using a special form of bootstrapping in which subsamples are generated, not through random selection from the sample but from a process that takes into account differences in homophily across groups. That is, the bootstrap subsamples are generated, not through random selection from the sample but rather from generating simulated recruitment chains based on the set of transition probabilities specifying the probabilities of members of each group recruiting members of each other group. It was with the introduction of means for calculating confidence intervals that RDS approaches a probability sampling method. For a more detailed presentation and analysis of this approach, see Salganik (2006) .

This paper also clarified the role of homophily in RDS analysis, as illustrated by Heckathorn (2002 :28) in a figure showing that as homophily increases, standard error increases exponentially. This reinforces the necessity to subdivide samples at high-homophily breakpoints.

An additional paper ( Salganik and Heckathorn 2004 ) introduced a further estimator (E#4), which employed a multiplicity approach to estimate relative group network sizes. It also introduced a proof that this RDS estimator is asymptotically unbiased when the assumptions of the method are met— that is, bias is only on the order of 1/[sample size], so bias is minor in samples of substantial size.

Specification of the assumptions must be satisfied for the 2004 estimator (E#4) to yield asymptotically unbiased population estimates provided a theoretically grounded means for assessing bias in RDS samples, through tests to see if the assumptions were approximated. Six assumptions were required for the proof:

• Respondents know one another as members of the target population, as is typical of groups such as drug users or musicians.
• The network of the target population forms a single component. This assumption is plausible if the network was created through a contact pattern, if it has small-world properties, or if its network sizes fit a power-law distribution.
• Sampling occurs with replacement. Therefore, the sampling fraction must be small enough for a sampling-with-replacement model to be appropriate.
• Respondents can accurately report their personal network size, i.e., the number of those they know who fit the requirements of the study such as drug injectors or jazz musicians (see Wejnert [2009] for an examination of this assumption).
• Respondents recruit randomly from their personal networks. This assumption becomes more plausible when members of the target population have easy and nonthreatening access to the research sites.
• Respondents recruit only a single recruit, so recruitment effectiveness is uniform across groups.

The first five assumptions provide guidance both on when RDS is a suitable method and on suitable research designs. The sixth assumption is frequently counterfactual, because it is common for some groups to recruit more effectively than others, but it was eliminated based on subsequent theoretic development (see the discussion of E#7 below).

A further development of the RDS method occurred in a Volz-Heckathorn (2008) paper that derived two RDS estimators based on network principles. The first of the two, termed RDS II (E#5), permitted analysis of continuous variables. A data smoothed version of this estimator (E#6) was also introduced ( Volz and Heckathorn 2008 :11), which yielded point estimates equivalent to the original Salganik and Heckathorn (E#4) estimator. Hence, two profoundly different logics, the reciprocity model underlying E#4, and the network approach underlying E#6, produced identical point estimates. This paper also went beyond the simulation-based boot-strap approach to variance estimation by proposing an analyticallyderived estimation procedure.

A further refinement of the RDS estimator ( Heckathorn 2007 ) provided a means for controlling for bias from differential recruitment, in which some groups recruit more effectively than others. This was accomplished by dividing the RDS sampling weight into an individual-based component calculated from respondents’ self-reported network sizes, and a group-based component calculated from the recruitment matrix based on each group’s patterns of recruitment of their own and other groups. This eliminated the need for the sixth assumption in the Salganik-Heckathorn (2004) paper, in which each recruiter had only a single recruit. It also reduced constraints on RDS research design, permitting multistage designs to more effectively sample low-density sectors of social networks ( Heckathorn 2007 :188).

As the use of RDS expanded, confusion between snowball sampling and RDS became increasingly common, including both articles employing snowball sampling but calling it RDS, and articles saying that the development of RDS showed that snowball sampling was, after all, a form of probability sampling. In an effort to avoid such confusion, a recent article ( Magnani et al. 2005 : S71) listed the different features of the methods.

RDS has now been employed in more than 120 studies in dozens of countries ( Malekinejad et al. 2008 ). The literature is now expanding in several directions, including sensitivity analyses to assess the effects of violations of its assumptions ( Gile and Handcock 2010 ), development of new estimators which may prove more reliable and valid than current estimators, applying the method to a greater range of groups ( Ramirez-Valles et al. 2005 ), and addressing the ethical issues that arise when studying stigmatized and vulnerable populations ( Semaan et al. 2009 ).

Acknowledgments

This research was supported by National Institutes of Health/NINR award 1R21NR10961. I thank Michael W. Spiller, Christopher J. Cameron, and Vladimir Barash for helpful comments and advice.

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How Snowball Sampling Used in Psychology Research

An effective method for recruiting study participants

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Esa Hiltula/iStock/Getty Images

• When to Use It

Is Snowball Sampling Qualitative or Quantitative?

• How It Works
• Pros and Cons
• Snowball Sampling Steps
• Role in Modern Research

Snowball sampling is a recruitment technique in which current research participants are enlisted to help recruit other potential study participants. This involves tapping into each participant's social network to find more subjects for a study. It allows researchers to find subjects who belong to a specific population who might not otherwise volunteer or seek out study participation.

As the name suggests, snowball sampling starts small and slowly "snowballs" into a larger sample. It is sometimes referred to as chain sampling, referral sampling, respondent-driven sampling, or chain-referral sampling.

At a Glance

Snowball sampling is a non-probability method allowing researchers to tap into hard-to-reach populations. Often used in qualitative designs, it allows researchers to recruit participants through referrals. This can be beneficial because it helps connect researchers with individuals they might not otherwise reach, but it can also contribute to sample bias and make it difficult to generalize the results to a larger population.

When to Use Snowball Sampling in Psychology Research

In most cases, researchers want to draw a sample that is both random and representative. Random selection ensures that each member of a group has an equal chance of being chosen, while representativeness ensures that the sample is an accurate reflection of the population as a whole.

While ideal, getting a random, representative sample isn't always possible. In such cases, researchers might turn to another method such as snowball sampling.

There are a number of situations where snowball sampling might be appropriate. These include:

• When researchers are working with populations that are difficult to reach, including marginalized or hidden groups, such as drug users or sex workers
• When research is in the exploratory stage, and scientists are still trying to learn more about an emerging phenomenon
• When researchers are working to generate a hypothesis before they conduct more comprehensive studies
• When recruiting through social networks makes the most sense in terms of cost and available resources
• When researchers are studying communities that are highly connected via shared characteristics of interest

Snowball sampling is commonly used in qualitative research. It uses a non-probability sampling method and is often used in studies where researchers are trying to explore different psychological phenomena and gain insights. Sample sizes may be smaller in this type of research, but often results in contextually-rich data. This can help researchers understand the nuances of what they are studying in a specific population.

How Snowball Sampling Works

Snowball sampling starts by finding a few individuals who meet the necessary criteria for a research sample. These individuals are sometimes known as the "seeds." The researcher then asks each participant to provide the names of additional people who meet those criteria.

The seed participants are interviewed and provided with a reward for their participation. They may then be given "coupons" that they can give to other eligible individuals. Each coupon contains information that allows recruiters to trace its origins. Potential participants can then redeem these coupons by enrolling in the study.

Each individual approached for participation is also asked to provide information on potential candidates. This process is continued until enough subjects have been located.

Pros and Cons of Snowball Sampling

Snowball sampling can have some pros and cons. Before using this approach, researchers should carefully weigh the potential advantages against the possible disadvantages and be transparent about any resulting limitations of the findings.

Advantages of Snowball Sampling

Snowball sampling can be particularly important when researchers are dealing with an uncommon or rare phenomenon. Traditional recruitment methods might simply not be able to locate a sufficient sample size .

It can also be helpful when participants are difficult to locate. This can include situations where people might be reticent about volunteering information about themselves or identifying themselves publicly. Because snowball sampling relies on recruiting people via trusted individuals, people may be more willing to participate.

Because snowball sampling provides essential information about the structure of social networks and connections, it can also be a helpful way of looking at the dynamics of the group itself.

Limitations of Snowball Sampling

The problem with snowball sampling is that it can contribute to bias . The opinions and characteristics of the initial members of the sample influence all of the subsequent subjects who are chosen to become part of the study.

This can make it more difficult for researchers to determine who might be missing from their sample and the factors contributing to that exclusion. Some variables might make it less likely for certain people to be referred, which can bias the study outcomes.

Another problem with snowball sampling is that it is difficult to know the size of the total overall population. It's also challenging to determine whether the sample accurately represents the population. If the sample only reflects a few people in the group, it might not be indicative of what is actually going on within the larger group.

Research suggests this sampling method can be a cost-effective way to collect data. However, researchers also caution that it can introduce bias, which means that caution must be used when interpreting the results of studies relying on snowball sampling.

Examples of Snowball Sampling

To understand how snowball sampling can be used in psychology research, looking at a few different examples can be helpful.

LGBTQIA+ Youth

Imagine a study where researchers want to investigate the experiences of LGBTQIA+ youth who live in rural areas. Because this population might be more difficult to reach due to discrimination , researchers might start by recruiting participants through local LGBTQIA+ organizations. Once they have an initial sample, the researchers can ask the current participants to introduce them to other people who are also LGBTQIA+.

Mental Health of Specific Populations

Consider a situation where researchers want to study the mental health of people in a particular profession, such as first responders who work in high-stress settings. The researchers might start by recruiting participants through professional organizations and then ask participants to refer them to colleagues who might also be interested in taking part.

Online Communities

Researchers might interested in learning more about phenomena that affect people who belong to specific online communities. They might reach initial participants by contacting them through online forums or websites and then ask if these participants are willing to share contact information for other members of the community.

Steps to Conduct Snowball Sampling

To conduct a snowball sample, researchers often use the following steps:

• Create a research question and define the objectives of the study.
• Identify the initial participants based on specific pre-determined criteria.
• Obtain informed consent that clearly explains the purpose, benefits, and potential risks of participating in the research.
• Collect data from the initial participants using surveys , interviews, observations , or other techniques.
• Ask participants to refer you to other potential participants and obtain contact information if possible.
• Contact the potential participants who have been referred to you. Explain the study and invite them to participate.
• Repeat the same process with each subsequent participant. 
• Continue the process until a sufficient sample has been obtained.

The Role of Snowball Sampling in Modern Research

While snowball sampling has its limitations, it plays an important role in modern psychology research . In particular, it can help researchers make contact with vulnerable or marginalized populations who are often overlooked and left out of more traditional sampling methods.

This technique can help researchers connect with the members of communities who may be hesitant to participate due to discrimination or the stigma associated with their condition.

It can also be a way for researchers to investigate phenomena that may be newly emerging and that might not yet be detectable using other sampling techniques.

Given the importance of social networks in today's highly connected work, snowball sampling also gives researchers a unique opportunity to examine how individuals connect to their communities. Researchers can use the information they collect to re-trace connections, providing valuable insights into how relationships and social dynamics affect the phenomena they study.

Snowball sampling is one method that psychology researchers may use to recruit study participants. While it has a greater risk of bias than drawing a random , representative sample , it does have some essential benefits. In particular, it can be a cost-effective way for researchers to find participants who belong to hidden or hard-to-reach populations. Despite the limitations of snowball sampling, it can play an important role in helping scientists learn more about emerging phenomena and populations that face stigma and marginalization.

Crawford FW, Wu J, Heimer R. Hidden population size estimation from respondent-driven sampling: a network approach . J Am Stat Assoc . 2018;113(522):755-766. doi:10.1080/01621459.2017.1285775

Raina SK. Establishing association . Indian J Med Res . 2015;141(1):127. doi:10.4103/0971-5916.154519

Kirchherr J, Charles K. Enhancing the sample diversity of snowball samples: Recommendations from a research project on anti-dam movements in Southeast Asia . PLoS One . 2018;13(8):e0201710. doi:10.1371/journal.pone.0201710

Martínez-Mesa J, González-Chica DA, Duquia RP, Bonamigo RR, Bastos JL. Sampling: how to select participants in my research study ?  An Bras Dermatol . 2016;91(3):326-330. doi:10.1590/abd1806-4841.20165254

Badowski G, Somera LP, Simsiman B, et al. The efficacy of respondent-driven sampling for the health assessment of minority populations . Cancer Epidemiol . 2017;50(Pt B):214-220. doi:10.1016/j.canep.2017.07.006

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Statistics Made Easy

Snowball Sampling: Definition + Examples

When researchers are interested in studying a particular population, they often recruit members of the population to be in a study using some type of sampling method .

One such method is  snowball sampling , a method in which researchers recruit initial subjects to be in a study and then ask those initial subjects to recruit additional subjects to be in the study.

Snowball sampling is also sometimes referred to as chain-referral sampling.

Using this approach, the sample size “snowballs” bigger and bigger as each additional subject recruits more subjects.

This sampling method is often used when researchers wish to study a population where the subjects are particularly hard to identify or reach. Examples include:

Individuals with rare diseases . If researchers are conducting a study of individuals with rare diseases, it may be difficult to find these individuals. However, if they can find just a few initial individuals to be in the study then they can ask them to recruit further individuals they may know through a private support group or through some other means.

Homeless individuals.  It may be difficult to obtain a list of homeless individuals in a city. However, researchers could find a few homeless individuals and then ask them to recruit more individuals they know who are homeless to be involved in the study. 

Ex-convicts.  If researchers are interested in conducting a study of ex-convicts, it could be difficult to find a large sample of people who would be willing to come forward to be in the study. But if researchers can find just a few ex-convicts to be in the study, they could ask each of them to recruit additional people they may know who are also ex-convicts.

The reason snowball sampling is so effective is because it’s often difficult for researchers to recruit individuals who don’t want to be identified for a particular reason. However, it’s much easier to recruit these individuals if they’re being recruited by people who are in similar circumstances as them and can reassure them that their privacy will be maintained in the study.

A researcher might have a difficult time recruiting someone with a rare disease to be involved in a study, but if that person is being recruited by someone who has the exact same disease, they’re far more likely to oblige.

Technical Notes:   Snowball sampling is an exampling of a non-probability sampling method , which means that not every member in a particular population has an equal probability of being selected for a study.   After all, using this method, the only way that an individual could become part of a study is if they were recruited directly by a researcher to be an initial subject or if they were recruited by a subject that was already in the study.   The opposite of a non-probability sampling method would be a probability-based sampling method, in which each member of a population has an equal probability of being selected for a study. The most obvious example of this would be a simple random sample .

Advantages of Snowball  Sampling

There are some advantages to using snowball sampling, including:

• Researchers can reach subjects in a particular population that would otherwise be difficult or impossible to reach.
• Snowball sampling is low-cost and easy to implement.
• Snowball sampling doesn’t require a research team to hire recruiters for the study since the initial subjects act as the recruiters who bring in additional subjects.

Disadvantages of Snowball Sampling

There are also several disadvantages to using snowball sampling, including:

• The sample for the study is not guaranteed to be a sample that is representative of the larger population .
• Sampling bias is likely to occur. Because initial subjects recruit additional subjects, it’s likely that many of the subjects will share similar traits or characteristics that might be unrepresentative of the larger population under study.
• Because the sample is likely to be biased, it can be hard to draw conclusions about the larger population with any confidence. For this reason, snowball sampling is often used as part of exploratory analysis – when researchers are simply interested in gaining a better understanding of a certain population and potentially uncovering information that they weren’t aware of.

The Ethics of Snowball Sampling

Because snowball sampling is often used to recruit individuals who don’t want to be identified or known, the topic of the research is usually sensitive and personal.

For this reason, researchers must be extra careful to protect the private information of the individuals in the study so that their contact details and information isn’t leaked.

The researchers should inform existing subjects and potential future subjects that all of their private information will be kept safe.

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Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

3 Replies to “Snowball Sampling: Definition + Examples”

Zack you are the greatest. Thank you so much,

Many thanks Zach – very useful information.

Hi.Am conducting a research on godfatherism and political leadership.And am using APC as my case study.But they are not giving me any chance to distribute my questionnaire.My question is that can I use snowball sampling technique to distribute the questionnaire

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Snowball sampling

Snowball sampling (also known as chain-referral sampling) is a non-probability (non-random) sampling method used when characteristics to be possessed by samples are rare and difficult to find. For example, if you are studying the level of customer satisfaction among elite Nirvana Bali Golf Club in Bali, you will find it increasingly difficult to find primary data sources unless a member is willing to provide you with contacts of other members.

This sampling method involves primary data sources nominating another potential primary data sources to be used in the research. In other words, snowball sampling method is based on referrals from initial subjects to generate additional subjects. Therefore, when applying this sampling method members of the sample group are recruited via chain referral.

Also, snowball sampling is the most popular in business studies focusing on a specific company that involve primary data collection from employees of that company. Once you have contact details of one employee she/he can help you to recruit other employees to the study by providing contact details.

There are following three patterns of snowball sampling:

1. Linear snowball sampling . Formation of a sample group starts with only one subject and the subject provides only one referral. The referral is recruited into the sample group and he/she also provides only one new referral. This pattern is continued until the sample group is fully formed.

2. Exponential non-discriminative snowball sampling . The first subject recruited to the sample group provides multiple referrals. Each new referral is explored until primary data from sufficient amount of samples are collected.

3. Exponential discriminative snowball sampling . Subjects give multiple referrals, however, only one new subject is recruited among them. The choice of a new subject is guided by the aim and objectives of the study.

Application of Snowball Sampling: an Example

Application of snowball sampling involves the following stages:

• Establish a contact with one or two initial cases from the sampling frame. This stage is usually the most difficult one.
• Request the initial cases to identify more cases
• Ask new cases to identify further cases (and so on)
• a) Your pre-specified sample size has been completed;
• b) There are no further cases left;
• c) Pursuing further cases will make the project unmanageable due to the large size.

If using questionnaire as primary data collection method, you can effectively apply snowball sampling with the use of emails. Specifically, body of the email requesting sample group members to participate in the survey can include a sentence along the following lines:

I would be very grateful if you could provide me with e-mail addresses of   other employees in your department/managers who are known to practice democratic leadership style/other people who have bought the same product/etc.   who could also participate in this survey.

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

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of sampling methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy , research approach , research design , methods of data collection and data analysis are explained in this e-book in simple words.

John Dudovskiy

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• What Is Snowball Sampling? | Definition & Examples

What Is Snowball Sampling? | Definition & Examples

Published on 17 August 2022 by Kassiani Nikolopoulou . Revised on 30 September 2022.

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.

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).

Table of contents

When to use snowball sampling, types of snowball sampling, advantages and disadvantages of snowball sampling, frequently asked questions about 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.

Prevent plagiarism, run a free check.

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

Exponential non-discriminative snowball sampling, exponential discriminative 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.

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 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.

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.

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.

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.

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)

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 .

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 .

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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Different Types of Sampling Techniques in Qualitative Research

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Key Takeaways:

• Sampling techniques in qualitative research include purposive, convenience, snowball, and theoretical sampling.
• Choosing the right sampling technique significantly impacts the accuracy and reliability of the research results.
• It’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique for your qualitative research.

Qualitative research seeks to understand social phenomena from the perspective of those experiencing them. It involves collecting non-numerical data such as interviews, observations, and written documents to gain insights into human experiences, attitudes, and behaviors. While qualitative research can provide rich and nuanced insights, the accuracy and generalizability of findings depend on the quality of the sampling process. Sampling techniques are a critical component of qualitative research as it involves selecting a group of participants who can provide valuable insights into the research questions.

This article explores different types of sampling techniques in qualitative research. First, we’ll provide a comprehensive overview of four standard sampling techniques in qualitative research. and then compare and contrast these techniques to provide guidance on choosing the most appropriate method for a particular study. Additionally, you’ll find best practices for sampling and learn about ethical considerations researchers need to consider in selecting a sample. Overall, this article aims to help researchers conduct effective and high-quality sampling in qualitative research.

In this Article:

• Purposive Sampling
• Convenience Sampling
• Snowball Sampling
• Theoretical Sampling

Factors to Consider When Choosing a Sampling Technique

Practical approaches to sampling: recommended practices, final thoughts, get expert guidance on your sample needs.

Want expert input on the best sampling technique for your qualitative research project? Book a consultation for trusted advice.

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4 Types of Sampling Techniques and Their Applications

Sampling is a crucial aspect of qualitative research as it determines the representativeness and credibility of the data collected. Several sampling techniques are used in qualitative research, each with strengths and weaknesses. In this section, let’s explore four standard sampling techniques in qualitative research: purposive sampling, convenience sampling, snowball sampling, and theoretical sampling. We’ll break down the definition of each technique, when to use it, and its advantages and disadvantages.

1. Purposive Sampling

Purposive sampling, or judgmental sampling, is a non-probability sampling technique in qualitative research that’s commonly used. In purposive sampling, researchers intentionally select participants with specific characteristics or unique experiences related to the research question. The goal is to identify and recruit participants who can provide rich and diverse data to enhance the research findings.

Purposive sampling is used when researchers seek to identify individuals or groups with particular knowledge, skills, or experiences relevant to the research question. For instance, in a study examining the experiences of cancer patients undergoing chemotherapy, purposive sampling may be used to recruit participants who have undergone chemotherapy in the past year. Researchers can better understand the phenomenon under investigation by selecting individuals with relevant backgrounds.

Purposive Sampling: Strengths and Weaknesses

Purposive sampling is a powerful tool for researchers seeking to select participants who can provide valuable insight into their research question. This method is advantageous when studying groups with technical characteristics or experiences where a random selection of participants may yield different results.

One of the main advantages of purposive sampling is the ability to improve the quality and accuracy of data collected by selecting participants most relevant to the research question. This approach also enables researchers to collect data from diverse participants with unique perspectives and experiences related to the research question.

However, researchers should also be aware of potential bias when using purposive sampling. The researcher’s judgment may influence the selection of participants, resulting in a biased sample that does not accurately represent the broader population. Another disadvantage is that purposive sampling may not be representative of the more general population, which limits the generalizability of the findings. To guarantee the accuracy and dependability of data obtained through purposive sampling, researchers must provide a clear and transparent justification of their selection criteria and sampling approach. This entails outlining the specific characteristics or experiences required for participants to be included in the study and explaining the rationale behind these criteria. This level of transparency not only helps readers to evaluate the validity of the findings, but also enhances the replicability of the research.

2. Convenience Sampling  

When time and resources are limited, researchers may opt for convenience sampling as a quick and cost-effective way to recruit participants. In this non-probability sampling technique, participants are selected based on their accessibility and willingness to participate rather than their suitability for the research question. Qualitative research often uses this approach to generate various perspectives and experiences.

During the COVID-19 pandemic, convenience sampling was a valuable method for researchers to collect data quickly and efficiently from participants who were easily accessible and willing to participate. For example, in a study examining the experiences of university students during the pandemic, convenience sampling allowed researchers to recruit students who were available and willing to share their experiences quickly. While the pandemic may be over, convenience sampling during this time highlights its value in urgent situations where time and resources are limited.

Convenience Sampling: Strengths and Weaknesses

Convenience sampling offers several advantages to researchers, including its ease of implementation and cost-effectiveness. This technique allows researchers to quickly and efficiently recruit participants without spending time and resources identifying and contacting potential participants. Furthermore, convenience sampling can result in a diverse pool of participants, as individuals from various backgrounds and experiences may be more likely to participate.

While convenience sampling has the advantage of being efficient, researchers need to acknowledge its limitations. One of the primary drawbacks of convenience sampling is that it is susceptible to selection bias. Participants who are more easily accessible may not be representative of the broader population, which can limit the generalizability of the findings. Furthermore, convenience sampling may lead to issues with the reliability of the results, as it may not be possible to replicate the study using the same sample or a similar one.

To mitigate these limitations, researchers should carefully define the population of interest and ensure the sample is drawn from that population. For instance, if a study is investigating the experiences of individuals with a particular medical condition, researchers can recruit participants from specialized clinics or support groups for that condition. Researchers can also use statistical techniques such as stratified sampling or weighting to adjust for potential biases in the sample.

3. Snowball Sampling

Snowball sampling, also called referral sampling, is a unique approach researchers use to recruit participants in qualitative research. The technique involves identifying a few initial participants who meet the eligibility criteria and asking them to refer others they know who also fit the requirements. The sample size grows as referrals are added, creating a chain-like structure.

Snowball sampling enables researchers to reach out to individuals who may be hard to locate through traditional sampling methods, such as members of marginalized or hidden communities. For instance, in a study examining the experiences of undocumented immigrants, snowball sampling may be used to identify and recruit participants through referrals from other undocumented immigrants.

Snowball Sampling: Strengths and Weaknesses

Snowball sampling can produce in-depth and detailed data from participants with common characteristics or experiences. Since referrals are made within a network of individuals who share similarities, researchers can gain deep insights into a specific group’s attitudes, behaviors, and perspectives.

4. Theoretical Sampling

Theoretical sampling is a sophisticated and strategic technique that can help researchers develop more in-depth and nuanced theories from their data. Instead of selecting participants based on convenience or accessibility, researchers using theoretical sampling choose participants based on their potential to contribute to the emerging themes and concepts in the data. This approach allows researchers to refine their research question and theory based on the data they collect rather than forcing their data to fit a preconceived idea.

Theoretical sampling is used when researchers conduct grounded theory research and have developed an initial theory or conceptual framework. In a study examining cancer survivors’ experiences, for example, theoretical sampling may be used to identify and recruit participants who can provide new insights into the coping strategies of survivors.

Theoretical Sampling: Strengths and Weaknesses

One of the significant advantages of theoretical sampling is that it allows researchers to refine their research question and theory based on emerging data. This means the research can be highly targeted and focused, leading to a deeper understanding of the phenomenon being studied. Additionally, theoretical sampling can generate rich and in-depth data, as participants are selected based on their potential to provide new insights into the research question.

Participants are selected based on their perceived ability to offer new perspectives on the research question. This means specific perspectives or experiences may be overrepresented in the sample, leading to an incomplete understanding of the phenomenon being studied. Additionally, theoretical sampling can be time-consuming and resource-intensive, as researchers must continuously analyze the data and recruit new participants.

To mitigate the potential for bias, researchers can take several steps. One way to reduce bias is to use a diverse team of researchers to analyze the data and make participant selection decisions. Having multiple perspectives and backgrounds can help prevent researchers from unconsciously selecting participants who fit their preconceived notions or biases.

Another solution would be to use reflexive sampling. Reflexive sampling involves selecting participants aware of the research process and provides insights into how their biases and experiences may influence their perspectives. By including participants who are reflexive about their subjectivity, researchers can generate more nuanced and self-aware findings.

Choosing the proper sampling technique in qualitative research is one of the most critical decisions a researcher makes when conducting a study. The preferred method can significantly impact the accuracy and reliability of the research results.

For instance, purposive sampling provides a more targeted and specific sample, which helps to answer research questions related to that particular population or phenomenon. However, this approach may also introduce bias by limiting the diversity of the sample.

Conversely, convenience sampling may offer a more diverse sample regarding demographics and backgrounds but may also introduce bias by selecting more willing or available participants.

Snowball sampling may help study hard-to-reach populations, but it can also limit the sample’s diversity as participants are selected based on their connections to existing participants.

Theoretical sampling may offer an opportunity to refine the research question and theory based on emerging data, but it can also be time-consuming and resource-intensive.

Additionally, the choice of sampling technique can impact the generalizability of the research findings. Therefore, it’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique. By doing so, researchers can select the most appropriate method for their research question and ensure the validity and reliability of their findings.

Tips for Selecting Participants

When selecting participants for a qualitative research study, it is crucial to consider the research question and the purpose of the study. In addition, researchers should identify the specific characteristics or criteria they seek in their sample and select participants accordingly.

One helpful tip for selecting participants is to use a pre-screening process to ensure potential participants meet the criteria for inclusion in the study. Another technique is using multiple recruitment methods to ensure the sample is diverse and representative of the studied population.

Ensuring Diversity in Samples

Diversity in the sample is important to ensure the study’s findings apply to a wide range of individuals and situations. One way to ensure diversity is to use stratified sampling, which involves dividing the population into subgroups and selecting participants from each subset. This helps establish that the sample is representative of the larger population.

Maintaining Ethical Considerations

When selecting participants for a qualitative research study, it is essential to ensure ethical considerations are taken into account. Researchers must ensure participants are fully informed about the study and provide their voluntary consent to participate. They must also ensure participants understand their rights and that their confidentiality and privacy will be protected.

A qualitative research study’s success hinges on its sampling technique’s effectiveness. The choice of sampling technique must be guided by the research question, the population being studied, and the purpose of the study. Whether purposive, convenience, snowball, or theoretical sampling, the primary goal is to ensure the validity and reliability of the study’s findings.

By thoughtfully weighing the pros and cons of each sampling technique in qualitative research, researchers can make informed decisions that lead to more reliable and accurate results. In conclusion, carefully selecting a sampling technique is integral to the success of a qualitative research study, and a thorough understanding of the available options can make all the difference in achieving high-quality research outcomes.

If you’re interested in improving your research and sampling methods, Sago offers a variety of solutions. Our qualitative research platforms, such as QualBoard and QualMeeting, can assist you in conducting research studies with precision and efficiency. Our robust global panel and recruitment options help you reach the right people. We also offer qualitative and quantitative research services to meet your research needs. Contact us today to learn more about how we can help improve your research outcomes.

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Snowball Sampling: A Purposeful Method of Sampling in Qualitative Research

Background and Objectives Snowball sampling is applied when samples with the target characteristics are not easily accessible. This research describes snowball sampling as a purposeful method of data collection in qualitative research. Methods This paper is a descriptive review of previous research papers. Data were gathered using English keywords, including “review,” “declaration,” “snowball,” and “chain referral,” as well as Persian keywords that are equivalents of the following: “purposeful sampling,” “snowball,” “qualitative research,” and “descriptive review.” The databases included Google Scholar, Scopus, Irandoc, ProQuest, Science Direct, SID, MagIran, Medline, and Cochrane. The search was limited to Persian and English articles written between 2005 and 2013. Results The preliminary search yielded 433 articles from PubMed, 88 articles from Scopus, 1 article from SID, and 18 articles from MagIran. Among 125 articles, methodological and non-research articles were omitted. Finally, 11 relevant articles, which met the criteria, were selected for review. Conclusions Different methods of snowball sampling can be applied to facilitate scientific research, provide community-based data, and hold health educational programs. Snowball sampling can be effectively used to analyze vulnerable groups or individuals under special care. In fact, it allows researchers to access susceptible populations. Thus, it is suggested to consider snowball sampling strategies while working with the attendees of educational programs or samples of research studies.

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bjectives: To determining attitudes and practice regarding breast cancer early detection techniques (breast self-examination (BSE), clinical breast examination (CBE) and mammography) among Iranian woman. Methods: International (PubMed, ISI, and Google Scholar) and national (SID and Magiran) databases were reviewed up to September 2017 to identify articles related to the attitudes and practices of Iranian women concerning breast cancer screening behavior with reference to BSE , CBE and mammography. The screening steps, analysis of quality of the studies and extraction of the papers were performed by two reviewers. Results: Of the 532 studies included initially, 21 performed on 10,521 people were considered eligible. Subjects with a positive attitude toward BSE in various studies were 13.5% to 94.0% with an average of 47.6%. Positive attitudes to CBE and mammography were found in 21.0% and 26.4%, respectively. Participant performance of BSE ranged from 2.6% to 84.7%, with an average of 21.9%. The respective figures for CBE and mammography were 15.8% and 16.7%. Conclusion: Considering the poor performance and low rates for positive attitudes, it is suggested that educational programs should be conducted across the country.

Iranian journal of nursing and midwifery research

Nasim Bahrami , Parvin Mirmiran

Research shows that the age at menarche, as an essential element in the reproductive health of women, had been decreasing in the 19(th) and 20(th) centuries, and shows a huge variation across different countries. There are numerous studies performed in Iran reporting a range of age at menarche. Thus, this meta-analysis aimed to determine the overall mean age at menarche of the girls in Iran. All relevant studies were reviewed using sensitive and standard keywords in the databases from 1950 to 2013. Two raters verified a total of 1088 articles based on the inclusion criteria of this study. Forty-seven studies were selected for this meta-analysis. Cochran test was used for samples' homogeneity (Tau-square). The mean age at menarche of the girls in Iran with 95% confidence interval (CI) from the random effects was reported. The homogeneity assumption for the 47 reviewed studies was attained (Tau-square = 0.00). The mean (95% CI) menarche age of Iranian girls from the random effects...

Purpose Quality of life is the most important psychological factor affecting breast cancer patients. This study aimed to examine the health related quality of life of breast cancer patients in Iran. Methods International (PubMed, Web of science, Scopus and Google scholar) and national (SID, Magiran) databases were searched for related studies to September 2017. The quality of the articles was evaluated using the Hoy tool. Results Out of 232 initial studies, 18 studies performed on 2263 people were included in the final stage of the study. Based on the EORTC-QLQ-C30 and random effect method, the pooled mean score of quality of life in 1073 people was 57.88 (95% CI 48.26–67.41, I 2 = 97.90%) and the pooled mean score of quality of life based on WHOQOL-BREF in 357 people was 66.79 (95% CI 45.96–87.62, I 2 = 99.50%). Conclusion According to the results of the study, a moderate level of quality of life in women with breast cancer was indicated. Therefore, the use of multidimensional approaches can improve their quality of life. Keywords Quality of life · Breast Cancer · Iran · Systematic review

Global Journal of Health Science

mahmood moosazadeh

Reza Ghanei , Mohammad Farajzadeh , Kourosh Sayehmiri

Background: Many nurses experience job stress in their workplace. Given the wide range of differences in the statistics about job stress among nurses, the question that arises is what is the general prevalence of job stress among Iranian nurses? Objective: The present study aimed to evaluate the prevalence of job stress among Iranian nurses through meta‑analysis. Persian and English databases including SID, MagIran, IranMedex, Google Scholar, Sciencedirect, and PubMed were searched by using the keywords such as " job stress, occupational stress, work‑related stress, job related stress " and their combinations and 30 articles were finally selected. All the observational research articles that had information regarding the prevalence of job‑related stress, sample size, and job stress instruments were entered into the meta‑analysis. The form used to extract information included variables such as the first author's name, publication year, the place where the study had been carried out, type of the study, sample size, data collection instruments, and the most important findings. Results: The general prevalence of job stress was estimated to be 69% (confidence interval [CI] 95%: 0.58–0.79) based on the report of 30 papers with sample size of 4630. By region, type of hospital and the type of study, the highest prevalence of nurses' job stress was 90% (CI 95%: 0.85–0.96) in region one (Provinces of Alborz, Tehran, Qazvin, Mazandaran, Semnan, Golestan, and Qom), 70% (CI 95%: 0.60–0.80) in public and private hospitals, and 79% (CI 95%: 0.58–1.01) in studies where the type of study had not been mentioned. Conclusion: Given the high prevalence of job stress among nurses, developing programs to reduce nurses' job‑related stress seems to be essential. and limited power to make decisions would create considerable job stress. [9] Although job stress appears in all professions, jobs dealing with people are associated with serious stress. Nursing is one of these jobs and nurses suffer from high

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Background and purpose: Aromatherapy, a CAM therapy, is a natural way of treating the mind, body and soul of individuals. The purpose of this study was to systematically review the literature to determine the effect of aromatherapy on hemodialysis complications. Methods: In this systematic review, international (PubMed, Google Scholar, Web of Science, CINHAL, EMBASE and Scopus) and national databases (SID and Magiran) were searched from inception of the databases to 30 De-cember 2017. Results: The results showed that aromatherapy reduced some of the complications of hemodialysis, including anxiety , fatigue, pruritus, pain of arteriovenous fistula puncture, sleep quality, depression, stress and headache. In one case, it improved the quality of life of hemodialysis patients. Conclusion: Considering the complications and heavy costs of managing complications in patients undergoing he-modialysis, it appears that aromatherapy can be used as an inexpensive, fast-acting and effective treatment to reduce complications in hemodialysis patients.

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Background: Domestic violence during pregnancy is a public health crisis, because it affects both mother ‎and fetus simultaneously, resulting in irreversible consequences for mothers and their ‎newborns. This study was performed to determine the prevalence of sexual violence during ‎pregnancy in the world and Iran as meta-analysis.‎ Methods: This study is a meta-analysis on the prevalence of sexual violence during pregnancy ‎in the world and Iran that was conducted on Persian and English published articles up to ‎‎2015. To this end, through searching the information by key words and their compounds at SID, Medlib, Irandoc, Google scholar, Pubmid, ‎ISI, Iranmedex, Scopus and Magiran, , all related articles ‎were extracted independently by two trained researchers. The results of studies analyzed using ‎the STATA and Spss16 software.‎ Results: In the initial searching of 167 articles, 33 articles related to Iran, 40 articles related to ‎other parts of the world and totally 73 articles met inclusion criteria for study. The prevalence ‎of sexual violence during pregnancy were estimated in the world 17% (CI95%:15% -18%) and ‎in Iran 28% (CI95%: 23% -32%).The prevalence of sexual violence during pregnancy in Iran is ‎‎11 percent more than the world.‎ Conclusion: According to the present meta-analysis results, the prevalence of sexual violence ‎during pregnancy in Iran is high. Given that sexual violence during pregnancy causes damage to ‎the mother and infant, it is recommended that the relevant authorities with the implementation ‎of intervention and educational programs reduce the prevalence of sexual violence during ‎pregnancy.‎

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10.2 Sampling in qualitative research

Learning objectives.

• Define nonprobability sampling and describe instances when a researcher might choose this sampling technique
• Describe the different types of nonprobability samples

Qualitative researchers typically make sampling choices that enable them to achieve a deep understanding of the phenomenon they are studying. In this section, we’ll examine the techniques that qualitative researchers typically employ when sampling as well as the various types of samples that qualitative researchers are most likely to use in their work.

Nonprobability sampling

Nonprobability sampling refers to sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown. Since we don’t know the likelihood of selection, we don’t know whether a nonprobability sample is truly representative of a larger population. That’s okay because generalizing to a larger population is not the goal with nonprobability samples or qualitative research. That said, this does not mean that nonprobability samples are drawn arbitrarily or without any specific purpose in mind (that would mean committing one of the errors of informal inquiry discussed in Chapter 1). Later, we’ll look more closely at the process of selecting research elements when drawing a nonprobability sample. First, let’s consider why a researcher might choose to use a nonprobability sample.

When are nonprobability samples ideal? One instance might be when we’re starting a big research project. For example, if we are conducting survey research, we may want to administer a draft of our survey to a few people who resemble the folks we’re interested in studying so they can help work out potential kinks. We might also use a nonprobability sample if we’re conducting a pilot study or exploratory research, as it would be a quick way to gather some initial data and help us get a feel of the lay of the land before conducting a more extensive study. From these examples, we can see that nonprobability samples are useful for setting up, framing, or beginning any type of research, but it isn’t just early stage research that relies on and benefits from nonprobability sampling techniques. Researchers also use nonprobability samples in full-blown research projects. These projects are usually qualitative in nature, where the researcher’s goal is in-depth, idiographic understanding rather than more general, nomothetic understanding.

Types of nonprobability samples

There are several types of nonprobability samples that researchers use. These include purposive samples, snowball samples, quota samples, and convenience samples. While the latter two strategies may be used by quantitative researchers from time to time, they are more typically employed in qualitative research. They are both nonprobability methods, so we include them in this section of the chapter.

To draw a purposive sample , a researcher selects participants from their sampling frame because they have characteristics that the researcher desires. A researcher begins with specific characteristics in mind that they wish to examine and then they seek out research participants who cover that full range of characteristics. For example, if you are studying mental health supports on your campus, you want to be sure to include not only students, but also mental health practitioners and student affairs administrators. You might also select students who currently use mental health supports, those who dropped out of supports, and those who are waiting to receive supports. The ‘purposive’ part of purposive sampling comes from intentionally selecting specific participants because you know they have characteristics that you need in your sample, like being an administrator or dropping out of mental health supports.

Note that these are different than inclusion criteria, which are more general requirements a person must possess to be a part of your sample. For example, one of the inclusion criteria for a study of your campus’ mental health supports might be that participants had to have visited the mental health center in the past year. Differently, purposive sampling assumes that you know individuals’ characteristics and recruit them based on these criteria. For example, I might recruit Jane for my study because they stopped seeking supports this month, or I might recruit JD because they have worked at the center for many years.

Also, it is important to recognize that purposive sampling requires the researcher to have information about the participants prior to recruitment. In other words, you need to know their perspectives or experiences before you know whether you want them in your sample. While many of my students claim they are using purposive sampling by “recruiting people from the health center,” or something along those lines, purposive sampling involves recruiting specific people based on the characteristics and perspectives they bring to your sample. To solidify this concept, let’s imagine we are recruiting a focus group. In this case, a purposive sample might gather clinicians, current patients, administrators, staff, and former patients so they can talk as a group. Purposive sampling would seek out people that have each of those attributes.

Quota sampling takes purposive sampling one step further by identifying categories that are important to the study and for which there is likely to be some variation. In this nonprobability sampling method, subgroups are created based on each category, the researcher decides how many people to include from each subgroup, and then collects data from that number for each subgroup. Let’s consider a study of student satisfaction with on-campus housing. Perhaps there are two types of housing on your campus: apartments that include full kitchens and dorm rooms where residents do not cook for themselves and instead eat in a dorm cafeteria. As a researcher, you might wish to understand how satisfaction varies across these two types of housing arrangements. Perhaps you have the time and resources to interview 20 campus residents, so you decide to interview 10 from each housing type. In addition, it is possible that your review of literature on the topic suggests that campus housing experiences vary by gender. If that is that case, perhaps you’ll decide on four important subgroups: men who live in apartments, women who live in apartments, men who live in dorm rooms, and women who live in dorm rooms. Your quota sample would include five people from each of the four subgroups.

In 1936, up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods. The Literary Digest, the leading polling entity at the time, predicted that Alfred Landon would beat Franklin Roosevelt in the presidential election by a landslide, but Gallup’s polling disagreed. Gallup successfully predicted Roosevelt’s win and subsequent elections based on quota samples, but in 1948, Gallup incorrectly predicted that Dewey would beat Truman in the US presidential election. [1] Among other problems, Gallup’s quota categories did not represent those who actually voted (Neuman, 2007). [2] This underscores the point that one should avoid attempting to make statistical generalizations from data collected using quota sampling methods. [3] While quota sampling offers the strength of helping the researcher account for potentially relevant variation across study elements, it would be a mistake to think of this strategy as yielding statistically representative findings. For that, you need probability sampling, which we will discuss in the next section.

Qualitative researchers can also use snowball sampling techniques to identify study participants. In snowball sampling , a researcher identifies one or two people they would like to include in their study but then relies on those initial participants to help identify additional study participants. Thus, the researcher’s sample builds and becomes larger as the study continues, much as a snowball builds and becomes larger as it rolls through the snow. Snowball sampling is an especially useful strategy when a researcher wishes to study a stigmatized group or behavior. For example, a researcher interested in studying how people with genital herpes cope with their medical condition would be unlikely to find many participants by posting an ad in the newspaper or by announcing the study at a social gathering. Instead, the researcher might know someone with the condition, interview that person, and ask the person to refer others they may know with the genital herpes to contact you to participate in the study. Having a previous participant vouch for the researcher may help new potential participants feel more comfortable about being included in the study.

Snowball sampling is sometimes referred to as chain referral sampling. One research participant refers another, and that person refers another, and that person refers another—thus a chain of potential participants is identified. Aside from being a useful strategy for stigmatized groups, snowball sampling is also useful when the interest group may be difficult to find or the group may be relatively rare. This was the case for Steven Kogan and colleagues (Kogan, Wejnert, Chen, Brody, & Slater, 2011) [4] who wished to study the sexual behaviors of non-college-bound African American young adults who lived in high-poverty rural areas. The researchers initially relied on their own networks to identify study participants, but members of the study’s target population were not easy to find. Access to the networks of initial study participants was very important for identifying additional participants in their situation. Initial participants were given coupons to pass on to others they knew who qualified for the study. Participants were given an added incentive for referring eligible study participants; they received $50 for participating in the study and an additional $20 for each person they recruited who also participated in the study. Using this strategy, Kogan and colleagues succeeded in recruiting 292 study participants.

Finally, convenience sampling is another nonprobability sampling strategy that is employed by both qualitative and quantitative researchers. To draw a convenience sample, a researcher simply collects data from people or other relevant elements that they can access conveniently. Also known as availability sampling, convenience sampling is the most useful in exploratory research or student projects where probability sampling is too costly or difficult. If you’ve ever been interviewed by a fellow student for a class project, you have likely been a part of a convenience sample. While convenience samples offer one major benefit—convenience—they do not offer the rigor needed to make conclusions about larger populations. In the next section on probability sampling, we will discuss this concept in greater detail.

Key Takeaways

• Nonprobability samples might be used when researchers are conducting qualitative (or idiographic) research, exploratory research, student projects, or pilot studies.
• There are several types of nonprobability samples including purposive samples, snowball samples, quota samples, and convenience samples.

Convenience sample – researcher gathers data from whatever cases happen to be convenient

Nonprobability sampling – sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown

Purposive sample – when a researcher seeks out participants with specific characteristics

Quota sample – when a researcher selects cases from within several different subgroups

Snowball sample – when a researcher relies on participant referrals to recruit new participants

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network by geralt CC-0

• For more information about the 1948 election and other historically significant dates related to measurement, see the PBS timeline of “The first measured century” at http://www.pbs.org/fmc/timeline/e1948election.htm. ↵
• Neuman, W. L. (2007). Basics of social research: Qualitative and quantitative approaches (2nd ed.). Boston, MA: Pearson. ↵
• If you are interested in the history of polling, I recommend reading Fried, A. (2011). Pathways to polling: Crisis, cooperation, and the making of public opinion professions . New York, NY: Routledge. ↵
• Kogan, S. M., Wejnert, C., Chen, Y., Brody, G. H., & Slater, L. M. (2011). Respondent-driven sampling with hard-to-reach emerging adults: An introduction and case study with rural African Americans. Journal of Adolescent Research , 26 , 30–60. ↵

Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Snowball Sampling

Snowball sampling is a recruitment technique in which research participants are asked to assist researchers in identifying other potential subjects.  The use of currently enrolled research participants to recruit additional research participants (sometimes referred to as “the snowball sampling”) may be approved by the IRB under some circumstances.  However, the protocol must include justification of the use of this method in the context of the study and target population.  The method that minimizes risk would be the preferred choice.  For example, a researcher seeking to study patterns of informal leadership in a community may ask individuals to name others who are influential in a community. 1

If the topic of the research is not sensitive or personal, it may be acceptable for subjects to provide researchers with names and contact information for people who might be interested in participation.  If the topic is sensitive or personal, snowball sampling may be justified, but care should be taken to ensure that the potential subjects' privacy is not violated.   For example, studies of networks of drug users or studies tracking sex partners require extreme caution with information gathered from one subject about another.

The steps taken to minimize the risk of violating an individual’s privacy should be articulated in the recruitment section of the protocol.  Current participants cannot receive incentives or compensation for referrals.

Acceptable alternatives that reach the same potential subjects include:

• The study team member may provide information to subjects and encourage them to pass it on to others who may be interested or eligible.  The information provided to enrolled subjects (fliers, letters of explanation, etc.) must be approved by the IRB.  Interested prospective participants could then contact the project for more info and possible inclusion.

• The study team member may ask subjects to obtain permission from others prior to disclosing their contact information.  In this scenario, the researcher would not directly contact the referred/potential subject without permission from the potential subject and would not have access to any information about a potential subject without permission from that individual.

1 . Examples provided by the National Science Foundation, http://www.nsf.gov/bfa/dias/policy/hsfaqs.jsp#snow

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