Snowball Sampling, PDF

What Is a Snowball Sampling?

Snowball sampling is a non-probability method in which the samples have unique characteristics. Existing subjects offer referrals to recruit the samples necessary for a research study using this sampling technique. This sampling technique comprises a primary data source designating additional possible data sources that can participate in the research investigations. Using the snowball sampling method, a researcher can establish a sample based only on recommendations. Consequently, this technique is also known as the chain-reference sampling technique. Snowball sampling is a common technique for business research. The snowball sampling technique is frequently employed when a population is unknown or rare, and it is challenging to select participants to collect samples for analysis. According to studies, although market research can be expensive, it is sometimes more costly to make erroneous decisions based on inaccurate or insufficient information. Between 25 and 50 percent of the average company’s annual marketing expenditure is allocated to research operations.

Benefits of Snowball Sampling

If you wish to examine a specific population, such as restaurant managers or crime victims, you must recruit volunteers for your study. Calling or mailing letters to random persons in the phone logs would take an interminable amount of time, as only some recipients would satisfy your requirements. The solution is snowball sampling, a method in which a small group of initial participants assists researchers in locating additional volunteers by tapping into their social networks. Snowball sampling is convenient, but it has numerous limits.

Recruiting Participants: The most significant benefit of snowball sampling is that it enables researchers to locate a more substantial number of study participants than possible with other methods. For instance, a researcher wishing to poll company executives might need help convincing CEOs to spend the time to complete the survey form. Nonetheless, if the researcher knows a particular CEO, that executive could reach out to other individuals in her social network who occupy the same position. Researchers are more likely to recruit new volunteers if they employ this strategy, which utilizes existing social relationships.Reaching Difficult-to-Locate Populations: Snowball sampling is beneficial for locating people that satisfy uncommon criteria and are usually difficult to identify. For instance, a researcher interested in surveying survivors of sexual assault may have difficulty recruiting volunteers, given that many survivors do not report their attacks to the police and records are frequently sealed from public view. However, some sexual assault survivors attend support groups, and a researcher in contact with one survivor may be able to reach additional survivors through that participant’s social network. This enables a bigger sample size than would otherwise be possible.Sampling Bias: An issue with snowball sampling is that participants’ social networks are not random. Consequently, the technique may produce biased samples. In the CEO research, for instance, if the first participant recruited works in manufacturing, it is likely that most of her CEO contacts also work in manufacturing. Consequently, the survey may need to include or underrepresent CEOs from other industries. Similarly, a sexual assault survivor’s social network may be limited to a single geographic area, preventing the researcher from generalizing study results to the entire country.Research Ethical Concerns: Snowball sampling might pose substantial ethical challenges, mainly if the research topic is sensitive or confidential. For instance, many survivors of sexual assault are hesitant to discuss their experiences and avoid identifying themselves as survivors. A researcher using snowball sampling can maintain participant anonymity in published articles but cannot preserve participant anonymity between themselves because participants must recruit one another. Some participants in the study may disagree with this use of their social networks, and some potential participants may detest being contacted.Sample hesitant subjects: Some individuals may not volunteer for research studies because they do not wish their identities to be revealed. In this circumstance, snowball sampling is practical since references are sought from people who know each other. Some segments of the target population could be more challenging to reach. For instance, if a researcher wishes to comprehend the challenges experienced by HIV patients, other sampling techniques cannot offer these sensitive samples. In snowball sampling, researchers can closely investigate and filter HIV-infected members of a population and conduct research by speaking with them, ensuring that they comprehend the research aim, and then assessing their responses.Cost effective: Referrals are received from a primary data source, which makes this strategy cost-effective. It is convenient and less expensive than alternative approaches.

Types of Snowball Sampling

Snowball sampling commences with a convenience sample of one or more initial subjects. Following are multiple data collecting points. These “seed” participants are utilized to attract the first wave of participants. Depending on your study aims, you can pick between three distinct snowball sampling methods:

1. Linear Snowball Sampling

One referral per participant is required for linear snowball sampling. In other words, the researcher recruits a single subject, and that subject recruits a single topic. This procedure is repeated until there are sufficient individuals in the sample. When there are few limits on who can be included in a sample, linear snowball sampling is most effective.

2. Exponential Non-Discriminative Snowball Sampling

One referral per participant is required for linear snowball sampling. In other words, the researcher recruits a single subject, and that subject recruits a single topic. This procedure is repeated until there are sufficient individuals in the sample. When there are few limits on who can be included in a sample, linear snowball sampling is most effective.

3. Exponential Discriminative Snowball Sampling

Participants in this strategy provide many referrals. However, the researcher checks these referrals and selects only those who match specified inclusion requirements. Not all referrals are included in the sample, which is the primary difference between this and exponential non-discriminatory snowball sampling. Exponential discriminative snowball sampling is often employed when selecting participants based on specified criteria and is crucial to achieving research objectives.

How to Use Snowball Sampling in Research

To assess whether snowball sampling is the appropriate strategy, it is vital to analyze the constraints that may arise while employing these techniques. Although particular phases can vary based on the research topic and sampling approach, many snowball sampling techniques follow a similar pattern. Consider the following steps when implementing snowball sampling in your research.

1. Identify Possible Population Members

Finding a sample subject is the first step in efficiently employing snowball sampling. One or two sources may come from this phase to start your research. It’s crucial to confirm that these potential participants match the arbitrary criteria of your study, such as age employment application, to establish a representative sample.

2. Contact Potential Subjects

After identifying your sample subject or subjects, you can contact them to determine if they are willing to participate in your research. There are numerous ways to get with topics, including telephone and email memos. If people agree to participate, you can proceed with your sample procedures. If they decline to participate in the study, you may contact your second sample source or seek more sources.

3. Invite Subjects to Participate in Research

When recruiting participants for your study, you should inform them about the research and its intended outcomes. This explanation can assist participants in making informed decisions on participation. Even if people decline to participate, you can still ask for subject referrals to enhance your sample size.

4. Promote Topic Referrals

Once the initial subjects have been recruited for your study, you can encourage them to provide subject referrals. Instead of asking participants to indicate other potential issues, you may encourage them to suggest that others contact you directly. This strategy is beneficial when studying delicate topics, as it allows referred subjects to choose whether or not to contact you.

5. If Utilizing Discriminatory Sampling, Evaluate Referrals

Analyze the referral information you obtain if you are employing discriminative sampling methods. This analysis ensures that the most relevant participants for your research may be identified. You can use this analysis to discover respondents who meet the research objectives for your issue, such as those who complete a particular age or occupation criterion.

6. Repeat Until the Required Sample Size Is Reached

After contacting your initial referrals, you can request more referral information from them. You may repeat these procedures until the required population is reached or all referrals have been contacted. You may also cease collecting volunteers when you have amassed a large enough sample size to offer valuable data.


Is snowball sampling biased?

Snowball sampling is dependent on recommendations. Here, the researcher recruits the initial participant or volunteers, who then recruit the subsequent subjects. Participants possess comparable qualities and are acquainted. This results in sampling bias, as only some people in the population have an equal probability of being included in the sample.

Is snowball sampling qualitative or quantitative?

Snowball sampling is a non-probability sampling technique in which only some population members are equally likely to be included in the sample. This means that you cannot utilize inferential statistics to draw broad conclusions, typically the objective of quantitative research. Consequently, a snowball sample is not representative of the target population and is more suitable for qualitative research.

Is snowball sampling random?

Snowball sampling is a type of nonprobability sampling. In contrast to probability sampling (which requires random selection), the initial study participants are responsible for recruiting additional volunteers. Snowball sampling is not unexpected because only some people in the target population have an equal probability of being drafted into the sample.

Many business leaders use Snowball sampling for high-level research because it helps them get answers from a specialized group. The results of snowball sampling are very relevant to the research situation and easily generalize to a larger group. This sampling method works best when there are few people to look at. If you have a big audience, choose probability sampling methods that ensure different subgroups are represented in your sample.