Method

1. Draft a participation program (likely to be subject to change, but indicative).
2. Approach stakeholders and ask for contacts.
3. Gain contacts and ask them to participate.
4. Community issues groups may emerge that can be included in the participation program.
5. Continue the snowballing with contacts to gain more stakeholders if necessary.
6. Ensure a diversity of contacts by widening the profile of persons involved in the snowballing exercise.

Requirement

The participants are likely to know others who share the characteristics that make them eligible for inclusion in the study.^[9]

Applicable situation

Snowball sampling is quite suitable to use when members of a population are hidden and difficult to locate (e.g. samples of the homeless or users of illegal drugs) and these members are closely connected (e.g. organized crime, sharing similar interests, involvement in the same groups that are relevant to the project at hand).^[9]

Application field

This list is incomplete; you can help by adding missing items. (June 2016)

Advantages

1. Locate hidden populations: It is possible for the surveyors to include people in the survey that they would not have known but, through the use of social network.
2. Locating people of a specific population: There are no lists or other obvious sources for locating members of the population (e.g. the homeless, users of illegal drugs). The investigators use previous contact and communication with subjects then, the investigators are able to gain access and cooperation from new subjects. The key in gaining access and documenting the cooperation of subjects is trust. This is achieved that investigators act in good faith and establish good working relationship with the subjects.
3. Methodology: As subjects are used to locate the hidden population, the researcher invests less money and time in sampling. Snowball sampling method does not require complex planning and the staffing required is considerably smaller in comparison to other sampling methods.^[13]

Snowball sampling can be used in both alternative and complementary research methodologies. As an alternative methodology, when other research methods can not be employed, due to challenging circumstancing and when random sampling is not possible. As complementary methodology with other research methods to boost the quality and efficiency of research conduct and to minimize the sampling bias like quota sampling.^[11][14]

Disadvantages

1. Community bias: The first participants will have a strong impact on the sample. Snowball sampling is inexact and can produce varied and inaccurate results. The method is heavily reliant on the skill of the individual conducting the actual sampling, and that individual's ability to vertically network and find an appropriate sample. To be successful it requires previous contacts within the target areas, and the ability to keep the information flow going throughout the target group.^[15]
2. Non-random: Snowball sampling contravenes many of the assumptions supporting conventional notions of random selection and representativeness.^[16] However, social systems are beyond researchers' ability to recruit randomly. Snowball sampling is inevitable in social systems.
3. Unknown sampling population size: There is no way to know the total size of the overall population.^[9]
4. Anchoring: Another disadvantage of snowball sampling is the lack of definite knowledge as to whether or not the sample is an accurate reading of the target population. By targeting only a few select people, it is not always indicative of the actual trends within the result group. Identifying the appropriate person to conduct the sampling, as well as locating the correct targets is a time-consuming process such that the benefits only slightly outweigh the costs.
5. Lack of control over sampling method: As the subjects locate the hidden population, the research has very little control over the sampling method, which becomes mainly dependent on the original and subsequent subjects, who may add to the known sampling pool using a method outside of the researcher's control.

Compensations

The best defense against weaknesses is to begin with a set of initial informants that are as diverse as possible.^[9] Efforts to improve the main disadvantage of snowball sampling resulted in the respondent-driven sampling (RDS) method.^[17] RDS augments the referral method by weighting the sample in order to compensate for the initial non-random selection, which may lead to the reduction of errors occurring in sampling by the referral method.^[13]

Advantages

• In hard-to-reach and hard-to-involve populations online sampling can better detect individuals of researcher's interest and allows to expand geographical scope of the studies^[4]
• Brings the possibility to increase representativeness of the results^[4]
• Virtual sampling can increase the number of responses in comparison with traditional snowball sampling. According to Baltar (2012) who used Facebook to search for participants for his study and conduct the research, it was possible to reduce the time necessary for building trust between the participant and researcher. Participants were more likely to share their personal information because the researcher was also sharing personal information on his/her Facebook profile. Increased level of confidence contributed to higher response rate^[4]
• Less costly relative to traditional snowball sampling technique^[4]

Disadvantages

• Even though the virtual sampling method can increase representativeness of the results, sample selection is biased towards the characteristics of online population such as gender, age, education level, socioeconomic level, etc.^[4]
• Target population might not always have access to the Internet^[4]

Example used in research

Virtual snowball sampling technique was used in order to find participants for the study of a minority group – Argentinian entrepreneurs living in Spain. About 60 percent of this population has double nationality – both Spanish and Argentinian. Spanish national statistics classifies them as European citizens only and there is no information about the place of birth tied to the profiles of entrepreneurs in Spain either. Therefore, referring to national statistics only, made it impossible to build a sample frame for this research. The use of virtual networks in this example of hard to reach population, increased the number of participating subjects and as a consequence, improved the representativeness of results of the study.^[4]

Respondent-driven sampling

A new approach to the study of hidden populations. It is effectively used to avoid bias in snowball sampling. Respondent-driven sampling involves both a field sampling technique and custom estimation procedures that correct for the presence of homophily on attributes in the population. The respondent-driven sampling method employs a dual system of structured incentives to overcome some of the deficiencies of such samples. Like other chain-referral methods, RDS assumes that those best able to access members of hidden populations are their own peers.^[21]

Peer Esteem Snowballing (PEST)

Peer Esteem Snowballing is a variation of snowball sampling, useful for investigating small populations of expert opinion. Its proponents^[22] argue that it has a number of advantages relative to other snowballing techniques:

1. reduces the selection bias inherent in initial seed samples for a snowball by advocating for a nominations phase that objectively identifies contact seeds for the first wave;
2. by analysing network data it provides an estimate of the population size, unbiased by any researcher defined population boundary;
3. by reporting the estimate of the sample size vis a vis the population, it provides a measure of relative significance (optimal sampling data can be reported in this context);
4. through a network analysis of referrals it allows for identifying clusters of experts that may be instrumental in explain variations in their response profile;
5. allows for a referrals nominations strategy that, in certain cases, could improve response rates, while the nominations strategy acts as an ultimate validation of expertise for informants and therefore improves content validity.