Snowball Sampling Technique

What is Snowball Sampling Technique? Complete Guide

The snowball sampling technique, sometimes referred to as chain sampling or network sampling, starts with one or more research participants. Following then, it proceeds based on recommendations from those individuals. This procedure is repeated until the required sample is obtained or a saturation point is reached.

Several universities employ snowball sampling, and the University of Sheffield uses it as university of Sheffield assignment help, his aids students who are simultaneously enrolled in classes and working jobs.

You may also hire someone to complete your difficult snowball sampling paper and pay someone to do your assignment uk, so it's not only restricted to that. Yet, you must be also familiar with this sampling technique to do that. With the help of this article, we will clarify this idea for you.

What is snowball sampling and example?

A non-probability sampling method where the samples have uncommon characteristics is known as the snowball sampling technique or chain-referral sampling. This sampling method uses recommendations from current participants to find the sample populations needed for a study.

To further elaborate on snowball sampling, the following are the snowball sampling examples that will help you comprehend the concept.

What is an example of a snowball sample?

Snowball sampling examples include;

  1. For instance, it would be quite challenging to get primary data sources if you are researching the degree of customer satisfaction among the members of a significant country club unless a club member agrees to speak with you directly and gives you the contact information of the other club members.
  2. Suppose you wish to gather feedback from people who have a rare kind of cancer. You cannot just stroll into a hospital and ask for patients' contact information or medical data. In this situation, traditional sampling strategies can be insufficient for obtaining relevant topics. What you can do, though, is issue a call to action to meet with one or two individuals who have the disease, and then request that they recommend you to more possible research participants who would be interested in taking part. Patients can continue to be referred in a chain until your sample frame is large enough.
  3. No official list of members names is available: This sampling approach can be applied to a population in which demographic data are not readily available. For instance, a list of privileged club members or the homeless, whose personal information is difficult to get.
  4. Locating individuals can be challenging: Finding individuals with uncommon disorders might be challenging. Finding the main data source might be difficult if a researcher is doing a comparable research topic. After they have identified the person, they frequently have information on more people who look like them.
  5. Individuals who don't want to be identified: These people come into this group if a researcher is doing a study that entails gathering information/data from sex workers, sexual assault victims, or those who don't want to divulge their sexual orientations.
  6. Confidentiality of identity: Individuals who are members of cults, religious zealots, or hackers typically fall under this category because of their secrecy regarding their identities. These people will need to be located and their information collected through snowball sampling.

It could be appropriate for participants to give researchers the names and contact information of persons who might be interested in participating if the research topic is not sensitive or personal. Snowball sampling may be appropriate if the issue is delicate or private, but caution should be exercised to protect the privacy of the potential subjects. Studies monitoring networks of drug users call for utmost caution when using data gleaned from one subject about another.

Where is snowball sampling used?

The qualitative research technique known as "snowball sampling" is frequently used, especially when examining difficult-to-reach groups. Volunteers are invited to help find additional potential subjects.

Examples of this may consist of:

  • Populations that are modest in size compared to the broader population
  • Small populations in comparison to the overall population
  • Populations spread out geographically
  • Populations with a negative social reputation or a certain common interest

Advantages of snowball sampling

The followings are the advantages and disadvantages of snowball sampling below:

  1. Rapid Sampling

The snowball approach makes it simple to identify relevant participants for your systematic inquiry. You can find variables that share uncommon qualities important to your research process by relying on the chain-referral technique.

  1. Cost-effective sampling

You don't need to spend a lot of money on the sampling procedure because you aren't looking for study participants on your own. You solely rely on the recommendations you receive from your main data sources.

  1. Unique outcomes

Snowball sampling enables you to gather replies from participants who would have been reluctant to participate in your study. Each variable already has a link with its referral, thus the referrals are quite keen to take part in the data-gathering process. Someone with a unique medical condition, for instance, would feel more at ease conversing with a patient facing a comparable difficulty.

  1. Less personnel

Compared to other sampling techniques, the snowball sampling method involves less personnel and preparation.

  1. Identify uncommon traits

You may use snowball sampling to identify further uncommon traits of the variables in your sample group.

Disadvantages of snowball sampling

The following are the disadvantages of snowball sampling below:

  1. It might be difficult to spot sample flaws or draw conclusions about the sampling community when using snowball sampling.
  2. The researcher is removed from the center of the sampling procedure through snowball sampling. This indicates that the researcher depends heavily on recommendations from individuals who have already been identified and has little to no control over the sampling technique.
  3. Because the population of interest is not well represented, it may cause sampling bias in your study.
  4. By increasing the margin of error for your study findings, snowball sampling method might cause greater discrepancies between the sample results and the population of interest.

Types of snowball sampling

A sample size of one or more initial participants serves as the starting point for snowball sampling. Following are several data gathering points (or waves). These early volunteers, referred to as "seeds," are utilized to enlist the participation of the first wave.

Like a snowball that becomes bigger as it rolls down a hill, wave 1 participants refer to wave 2 participants, and so on.

There are three main types of snowball sampling techniques to pick from, depending on your research goals:

  1. Linear snowball sampling
  2. Exponential non-discriminative snowball sampling
  3. Exponential discriminative snowball sampling

Linear Snowball Sampling

In this method, a sample group is created by starting with a single subject who then refers just one more subject to another, and so on. This practice is followed until the sample has a sufficient number of participants.

Linear snowball sampling advantages

  1. Finding appropriate variables for the study doesn't require the researcher to spend a lot of time and money.
  2. You may pace your study this way to ensure that you have exactly the right number of participants at the end of the day.

Linear snowball sampling disadvantages

  1. It takes quite a while.
  2. If any of the current variables are unable to offer a reference, the chain may abruptly end.

Non-discriminatory Exponential Snowball Sampling:

In this method, the first subject is selected, and he or she subsequently gives several referrals. After there are enough subjects for the sample, each subsequent referral then offers additional information for referral, and so on.

Non-discriminatory exponential snowball sampling advantages:

  1. Using this sampling technique, you can get the perfect study population more quickly.
  2. For your sample population, you have additional alternatives to consider.

Non-discriminatory exponential snowball sampling disadvantages:

  1. You could occasionally have variables that aren't a perfect fit for your study.

Snowball sampling with exponential discrimination

In this method, each subject provides several recommendations, but only one subject is selected from each referral. The type of research study will determine what fresh topic is chosen.

Exponential discriminative snowball sampling advantages:

  1. It enables you to pick the factors that match your study the best.
  2. Snowball sampling with exponential discrimination increases the dependability of your study findings.

Exponential discriminative snowball sampling disadvantages

  1. That takes a lot of time.
  2. Existing participants may feel discouraged from suggesting new research participants.

How to perform snowball sampling

The greatest situations for snowball sampling are those in which there is a distinct, tiny population that is hard to discover or identify. It is one of the greatest data-gathering methods for exploratory or qualitative research because of its non-probability character.

When it comes to snowball sampling, there are two crucial stages you should follow. You must first narrow down the probable subjects for your study so they closely match your goals and objectives. In the first case, you often only identify one or two potential variables.

Ask those who provided the first factors to identify more possible participants who match their characteristics. You might provide participants incentives to promote referrals to receive the most response.

What is purposive and snowball sampling?

Purposive sampling

In purposive sampling, the researcher employs their judgment to choose appropriate study participants based on their understanding of the context of the systematic inquiry.

Snowball sampling

In contrast, the researcher uses existing study participants to help find more possible subjects when using snowball sampling.

Snowball sampling: Uses and Applications

The challenge of finding appropriate variables for the study is a theme that unites all uses of snowball sampling. Due to this challenge, the researcher decides to rely on referrals for the sample population to develop some form of data gathering bypass for the systematic examination.

Some justifications for using snowball sampling in research include:

  • When you are unable to reach the majority of your target audience;
  • When it's challenging to contact your target audience while knowing where they are,
  • When participants in your target population experience social stigma and are less motivated to participate in your research

How do you calculate the snowball sampling sample size?

Finding the appropriate snowball sampling in research is crucial since a small sample size might produce unreliable results. But if it's too big, you can wind up squandering time and money.

The sample size for snowball sampling often depends greatly on whether the population is known or not.

  1. To determine the precise sample size, you can utilize online calculators like if the population is finite or known.
  2. You can apply the Cochran Formula if the population is unlimited or unknown (that is if you are unsure of what the population might be).

Do snowball samples contain bias?

Referrals are a key component of snowball sampling. In this case, the researcher chooses one or more initial participants, who then choose the following individuals.

Participants may be acquainted with one another or share comparable traits. As a result, sampling bias results from the fact that not every person in the population has an equal probability of being picked for the sample.

The Snowball Sampling Formula: What Is It?

In research, there is no set formula for snowball sampling. Each of the current variables would have to give one potential study participant if you used the linear sampling approach. Existing variables can identify all potential participants using the exponential approaches.


For high-level research, many company executives employ snowball sampling since it makes it easier to obtain responses from a niche group. Results from snowball sampling may be readily generalized to a larger audience and are extremely relevant to the study environment. Companies that have a tagline ‘do my assignment for me cheap UK’ provide students the authentic and required results in their thesis projects.

We would suggest students prioritize and finalize their sample size before requesting writers on various platforms saying, can someone write my assignment because this sampling technique works best when you're working with a small population. It will also help the academic writer to conclude papers. If your audience is greater, choose probability sampling techniques that ensure that many subgroups are represented in your sample.

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