7 Secrets to Mastering Snowball Sampling Unlock the Mystery and Improve Your Results

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 issnowball 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.

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:

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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.

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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

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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.Sometimes, researchers find it difficult to access the relevant variables for their study and build a sample. When this happens, you can explore snowballing sampling instead of stalling your systematic investigation or ending it abruptly. So how does snowball sampling work? 

For a better understanding, you need to cast your mind to winter—remember how a rolling snowball gathers the snow on its path? This is exactly how snowball sampling works. In other words, the researcher depends on a small number of participants to help him identify other potential research subjects. 

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Also known as chain-referral sampling, snowball sampling is a non-probability technique that allows the researcher to discover variables with rare characteristics. Here, the researcher finds a handful of suitable primary data sources and uses them to discover similar variables for the study—think of it as a referral technique that thrives on the principle of like-minded attraction. 

For instance, let’s say you want to collect responses from patients who suffer from a rare type of cancer. In this case, other sampling techniques might prove inadequate for gathering relevant subjects—you cannot just walk into the hospital and request patients’ contact information or medical records. What you can do, however, is put out a call to speak with one or two patients with the condition, and then ask them to refer you to other potential subjects who might be willing to participate in your study. Patients can continue a chain-referral process until there’s enough population to form your sample frame. 

One of the most exciting parts of winter is the snow, and the different games you play with snowballs. You’d notice that when you form a tiny snowball and set it rolling, it continues to pick up more snow along its path until it forms a large ball of snow, ready for the games. This is what happens with snowball sampling. 

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First, the researcher forms a small snowball by identifying a handful of potential participants for the systematic investigation. Then, s/he set the ball rolling by asking existing participants to “pick up” similar variables. As they continue to refer new subjects, the sample population or snowball grows bigger until you have enough data to work with. 

In linear snowball sampling, the researcher depends on a straight-line referral sequence that starts with a single variable. This variable provides information about one other potential research subject, and it goes on until the desired sample population is achieved. 

Exponential non-discriminative snowball sampling runs on a geometric chain sampling sequence. Here, the formation of a sample population starts with finding one suitable participant. Now, this participant goes ahead to refer multiple potential research subjects, and the chain continues until you have enough participants for your study. 

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This is quite similar to exponential non-discriminative snowball sampling. However, in this case, the researcher screens potential variables before accepting them into the sample population. Only variables that meet the screening criteria are allowed to participate in the systematic investigation. 

Snowball sampling is best used in contexts with a specific and relatively small population that is difficult to identify or locate. Because of its non-probability nature, it is one of the best data collection techniques for exploratory or qualitative research. 

There are two important steps you should take when it comes to snowball sampling. First, you need to identify potential subjects that closely fit your research, and can help you achieve your aims and objectives. Typically, you’d find only one or two possible variables in the first instance. 

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Next, ask these initial variables to recruit other potential participants who share the same characteristics as them. To get the most response, you can throw in incentives that encourage participants to refer others. 

1. Accelerated Sampling: With the snowball technique, you can easily find suitable participants for your systematic investigation. By depending on the chain-referral system, you can discover variables that share rare traits that are relevant to your research process. 

2. Cost-effective Sampling: Since you’re not scouting for research participants by yourself, you don’t have to invest lots of money in the sampling process. You only depend on the referrals obtained from your primary data sources. 

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3. Snowball sampling also allows you to collect responses from people who would have hesitated to take part in your research. Since there’s an existing relationship between each variable and their referral, the referrals are quite eager to participate in the data collection process. For example, someone with a rare medical condition would feel more comfortable speaking with a patient with a similar challenge.

2. Snowball sampling removes the researcher from the center of the sampling process. This means that the researcher has little or no control over the sampling method, and relies mainly on the referrals from already-identified participants.

4. Snowball sampling can increase the margin of error of your research results, leading to more variations between the sample results and the population of interest.

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The common line that runs through all applications of snowball sampling is the difficulty in locating suitable variables for the research. This difficulty prompts the researcher to create some sort of data collection bypass for the systematic investigation by depending on referrals for the sample population. 

Identifying the right sample size is important because, if it’s too little, it can lead to inaccurate results. However, if it’s too large, you might end up wasting valuable time and resources. 

2. If the population is infinite or unknown (i.e you have no idea what the population could be), you can use the Cochran Formula. Find out more here

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1. Medical researchers use snowball sampling to collect responses from patients with rare diseases. Suppose you’re researching the symptoms of a condition like a porphyria. People with this disease might be unwilling to speak with a researcher about their illness. However, with snowball sampling, you can use the chain-referral system to draw them out and gather the data you need.

2. Let’s say you’re researching heroin usage in a particular location. On your own, it might be difficult to draw out people who

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