Snowball Sampling: Techniques, Applications, and Examples

What’s research? It is the systematic process of investigating, analyzing, and interpreting information. 

Why? To discover new facts, validate existing theories, or solve specific problems. If you are familiar with the term, you might have heard about qualitative and quantitative research. 

Now, if you delve a little deeper, you’ll come across sampling and its two types: probability and non-probability sampling. (No, we’re not done yet!) Go further into the non-probability sample, and you’ll land on snowball sampling!

In this blog, we will zoom in on snowball sampling method, exploring its types, applications, and everything in between. Let's get started!

What is Snowball Sampling?

Snowball sampling is a research method used to study a population that is hard to reach. 

Imagine you’re collecting snowflakes. It starts with one, and you let it roll downhill. On the way, you pick up more snow. Snowball sampling works a bit like that, but in research. It provides a methodological snowball effect where the sample size gradually expands.

So, How Does it Work? 

• Start Small: Begin with a single participant, often someone with unique access or knowledge about the group being studied.
• Expand Through Referrals: After interviewing the initial participant, ask them to refer others who meet the study criteria.
• Chain Effect: Each new participant refers to more individuals. This creates a ‘snowball effect’ that enlarges the sample size over time.

It is useful when researching a sensitive topic and the participants are reluctant to participate.

Now, are we clear with the basics?

(Note: For those research gurus out there, we were all novices once, right?) 

Let’s get to the interesting part and dive into the intricacies of this sampling method, shall we?

Types Of Snowball Sampling Methods

There is so much more than meets the eye regarding snowball sampling. It is also called “network,” “chain referral,” “respondent-driven,” and “seeded” sampling. Each of these techniques (if I may) has unique characteristics and perspectives. Have a look at them.

1. Chain Referral Sampling

In this, participants refer to others sequentially. This creates a chain-like structure (thus the name!). It is simple and straightforward. This is one reason why it is comparatively easy to implement. 

Where is it used? 

It is effective when you try to study communities that have a clear social structure. Moreover, it encourages participants to participate in the referral process actively. Also, you can use it in instances where building a network is your aim. e.g., exploratory research

What makes it effective is that it allows you to race the referral path. This helps you understand social connections better. In fact, what makes it interesting, I believe, is the natural flow it offers. It resembles real-life interactions.

Remember that it is only suitable when the participants are comfortable referring others.

Moving on to the next method, RDS.

2. Respondent-Driven Sampling (RDS)

RDS combines elements of snowball and probability sampling. (And, you attain a perfect balance)

It enhances data accuracy. How? Participants provide information about their social networks.

Sampling biases are corrected by using statistical techniques. This also improves sample representativeness and enhances reliability by minimizing selection biases.

It provides a systematic method for determining participant incentives.

Chiefly, the method enables researchers to calculate sample weights. This ensures data analysis. It creates a sense of trust in the participants. Wouldn’t you love to be promised confidentiality and privacy?

3. Network Sampling

Just as the name suggests, network sampling refers to a method that works on the foundation of building connections.

It focuses on entire social networks. Mainly, it studies interactions and relationships.

So, what does this study offer? A holistic view of community structures and social dynamics. This also helps you get a bird’s eye view of the top influencers within the community.

It also gives an idea of the interconnectedness of the participants.

Go for this method if you’re planning to study information flow. Moreover, it gives you command over the patterns within networks.

Now, where is it used? It is often used in sociology and anthropology studies to analyze social relationships.

4. Seeded Snowball Sampling

Before we start on this one, you need to understand what a seed is in snowball sampling.

So, a seed is like the initial domino in a row. You get what I mean? Let me explain.

Snowball sampling begins with one or a set of participants. A seed is the initial participant(s) of that lot. It is the one who begins the chain. Got it?

Now, let’s get back to where we started.

Seeded snowball sampling allows you to control the initial participants. This helps in maintaining specific characteristics or expertise.

• If you wish to focus on specific subgroups within a larger community, this method is for you.
• The best part is that it provides a structured approach to building a participant network.
• We all know how important it is to get the perfect beginning. Seeded snowball sampling ensures a consistent starting point for the sampling process.
• As we discussed earlier, it helps balance inclusivity and specificity in participant selection.
• Basically, you have control over the direction of the sampling process. You become the helm and the seeded sampling, the ship. (Poetic much?)

3 Common Examples of Snowball Sampling

Here are three common examples of using snowball sampling.

Talking to Undocumented Immigrants

A researcher wants to learn about the lives of undocumented immigrants in a city. These people can be hard to find because they might be worried about legal issues or want to keep their identities secret.

How it Works:

• The researcher starts by talking to a local organization that helps immigrants.
• They find a few immigrants who are willing to share their stories (the "seeds").
• After talking to these first people, the researcher asks if they know any other undocumented immigrants who might want to participate.

Each new person then recommends more people, like a snowball getting bigger as it rolls down a hill.

Learning About Rare Diseases

Researchers often have trouble studying people with rare diseases because there are not many of them.

How it Works:

• The researcher starts with a small group of people who have been diagnosed with the rare disease.
• They ask these people if they know anyone else with the same condition.
• Each person recommends others they know, and this chain continues until the researcher has enough participants.

This method works well because people with rare diseases often connect with others in support groups or online. So it's easier for researchers to find them.

Understanding Marginalized Groups

Sociologists sometimes use snowball sampling to study groups that are not part of the mainstream. This include people who use drugs or don't have homes.

How it Works:

• The researcher finds a few people in the community who are willing to talk about their experiences.
• These people are asked to recommend others in their group who might also want to participate.
• As each new person recommends more people, the number of participants grows. Therefore, the researcher can learn about the community from different perspectives.

This approach is helpful because it allows researchers to reach groups that are often hidden from traditional research methods due to stigma or privacy concerns.

User Bases of Snowball Sampling 

Now comes the question: who uses snowball sampling?

It isn’t restricted to specific fields. What makes it unique is its adaptable nature. Snowball sampling has a diverse user base. Let’s take a look at a few of them.

1. Human Rights Organizations

Human rights organizations rely on snowball sampling. It helps to gather firsthand narratives of human rights violations. This enables them to advocate for the right cause. This method strengthens their policy influence by providing concrete data. Apart from that, it supports legal actions.

Snowball sampling also educates the public about community challenges. Additionally, the data collected empowers affected communities by giving them a platform to share their stories.

Moreover, it supports the efforts of long-term change by exposing recurring human rights issues.

2. Public Health Research

Snowballing helps in understanding disease transmission behaviors. This enables the growth of targeted development strategies.

It reveals disparities in healthcare access. How? By highlighting areas needing intervention and investment.

It supports epidemiological studies. This collection helps in future health education initiatives. Plus, it helps in providing data for vaccine research. This method is beneficial when you are trying to study less-known diseases.

3. Market Research

Market research is one of the most important aspects of a company’s growth. Sampling here helps to provide insights into consumer behavior. It also helps in identifying distinct consumer segments for targeted marketing.

What intrigues me is its ability to analyze competitors effortlessly. Moreover, it predicts market trends. This helps you make informed decisions. That’s a catch, right?

4. Social Science

As I mentioned before, snowball sampling helps to break down social dynamics within communities. It helps you study social movements, identities, and community development efforts.

The best part?  It supports identity studies. It helps you delve into the depth of gender, ethnicity, and social identity within communities.

How to Do Snowball Sampling | 6 Simple Steps

Here are a few things you should do:

Step 1 - ​​Start with a Seed

Choose someone you know who is part of the group you want to study. This person is called the "seed."

Step 2 - ​​Interview the Seed

Have a conversation with the seed. Ask them about their experiences and get them to introduce you to other people who might be good for your study.

Step 3 - ​​Contact Referrals

After talking to the seed, reach out to the people they introduced you to. Interview them too, and ask them to introduce you to more people. Keep doing this until you have enough people for your study.

Step 4 - ​​Keep Collecting Referrals

Keep talking to new people and getting more referrals until you have enough participants for your study. Each person you talk to will lead you to more potential participants.

Step 5 - Pay Attention to Relationships

As you talk to people, notice how they are connected to each other. Understanding the relationships in the group can help you analyze your data better.

Step 6 - ​​Respect Privacy

Always respect people's privacy. Make sure they understand why you are doing your study and get their permission before including them.

Importance of Snowball Sampling

Snowballing helps in expanding the scope of research. Here are a few points I think we should all consider:

• Reaching Hidden Communities: Snowball sampling helps find people who are usually difficult to locate or talk to.
• Building Trust: People prefer to talk if someone they trust introduces them.
• Rich, Diverse Data: It gathers information from many different people, giving a wide view of the topic.
• Exploring Sensitive Topics: It makes it easier for people to talk about sensitive or private issues.
• Community Insight: Helps understand how communities work and how people are connected.
• Flexible and Cost-Effective: Saves Money! It’s a low-cost way to gather information compared to other methods.

The Best Tool for Snowball Sampling

When you are doing snowball sampling, picking the right tool can make your research much easier. One of the best tools for this is SurveySparrow.

Why Use SurveySparrow for Snowball Sampling?

1. Easy to Use

SurveySparrow has a simple interface that lets you create and share surveys without any hassle. This ease of use is important when you want to engage participants quickly. You can now create surveys faster with SurveySparrow AI. Just add in the prompt, and the tool will take care of the rest.

2. Customizable Surveys

You can design surveys that fit your specific needs, whether you want multiple-choice questions or open-ended responses. This helps you collect detailed information from your participants.

3. Automated Reminders

SurveySparrow allows you to set up automatic reminders for participants who haven’t completed the survey yet. This feature is great for snowball sampling because it encourages initial participants to refer others and keeps the recruitment process going smoothly.

4. Privacy Protection

The platform lets participants respond anonymously. This can help people feel more comfortable sharing their information. This is especially important when working with sensitive groups.

5. Data Analysis Tools

SurveySparrow provides built-in tools powered by AI to help you analyze the data you collect. You can easily create charts and reports to visualize your results as well. Thus, it makes it simpler to understand your findings.

Advantages of Snowball Sampling

Snowballing does not come without advantages. What are they?

• Easy Access: First of all, it helps researchers reach people. Even those who are not easily accessible through traditional methods.
• Trust and Comfort: Participants feel more comfortable talking when introduced to someone they know. This builds trust in the research process.
• Diverse Perspectives: It gathers various opinions and experiences. Further, it provides a broader understanding of the topic.
• Cost-Effective: Snowball sampling is budget-friendly as it relies on existing social networks.
• Flexible: It adapts well to different communities and situations. Also, it makes it fit for various research needs.
• Exploring Sensitive Topics: Lastly, participants find it easier to discuss sensitive issues in a familiar and trusted environment.

Limitations of Snowball Sampling

Now that you understand the pros, let’s look at some cons:

• Limited Selection: Snowball sampling might leave out important individuals.
• Not Random: People are not chosen randomly, leading to potential bias.
• Inaccurate Results: Findings might not represent the entire community accurately.
• Privacy Concerns: Some may not share personal information openly.
• Time-Consuming: It takes a lot of time to find and interview participants.
• Biased Data: Initial biases can grow. This affects the overall research quality.

Wrap Up!

It’s time to conclude! Now, do you understand the idea behind snowball sampling? We had a little of everything for everyone, right? With snowball sampling, we can understand communities better. This is done by listening to people. 

Though it’s essential to be mindful of its biases and potential inaccuracies, snowball sampling helps you gather diverse perspectives. So, embrace the power of connections and explore!

And again, before you go, don’t forget to try SurveySparrow. It’s free!