Surveys help you collect audience opinions that help you, and use the information to help you understand their preferences and opinions. But here’s the thing: if you invite people to a survey, chances are high that the people who respond are mostly people who are already interested in the topic of the survey.

Let’s say you’re walking and someone asks if you want to join them for a wine tasting. Aside from the fact that you may have other activities scheduled, or you may just not be in the mood for it. A huge factor that will determine whether you will join this experience is whether or not you’re a wine person.

That’s what happens with volunteer sampling; the majority of your respondents are people who are already interested in your topic. So, the responses may not help you understand why your product/service/candidate doesn’t appeal to others. 

Let’s discuss volunteer sampling and how to prevent it from being biased.

What Is Volunteer Sampling?

Volunteer sampling happens when the majority of people you collect data from are people who choose to be a part of your survey. This is also known as voluntary response or self-selected sampling.

While the operative words here are “choose” and “volunteer,” it doesn’t mean you have to force people to respond; people will only take your survey if they want to. However, it becomes a problem when you only have answers that reflect a particular group of people with similar opinions and exclude people who have different opinions. 

Volunteer Sampling Is Fueled by Convenience

A major reason why volunteer sampling happens is because of convenience. Let’s say you want to know how people feel about the new Google Veo 3 update; the most likely place to get responses is social media. Anyone (age, gender, location, etc) can be on social media and see your survey. 

But the problem here is that while you can reach anyone, you’re most likely to reach only people interested in this particular type of topic. You posted about Google Veo 3, so you’re more likely to get responses from AI enthusiasts and people who use it in their workflow. In summary, the algorithm is most likely to show your post to people who have shown interest in similar topics.

Common Biases in Volunteer Sampling

Volunteer sampling is relatively easy to implement, but it’s highly susceptible to different biases. Here are the most common types of biases that tend to creep into volunteer sampling:

1. Self-Selection Bias

This is the most common side effect of volunteer sampling. Your volunteers are likely to be people who have strong opinions on the topic, whether it’s positive or negative. Meanwhile, people who are not interested or too busy respond, so if their opinions are different from the volunteers, it won’t reflect in the data, and if you base your decisions, e.g, a product launch, on this survey, you may end up creating a product that only a select few of your customers want.

2. Non-Response Bias 

This is a bit similar to self-selection in terms of the results. However, non-response itself happens when a significant portion of your target population doesn’t respond to your survey, and only a select few do, and the people who don’t respond have different opinions from people who respond. For example, if a survey about how bubble tea is only completed by people who take bubble tea from a specific tea shop, it won’t accurately reflect the views of how people like bubble tea in general (because not every shop uses the same recipe).

3. Demographic and Attitude Skew

People with similar interests are likely to answer surveys that interest them, which means your survey demographics will reflect this. For example, Gen Z and Millennials are more likely to take online polls than Gen X and Baby Boomers, which means they might be overrepresented in online polls. Similarly, people with strong political views are more inclined to participate in political surveys than people who aren’t, leading to an overrepresentation of a particular opinion.

How Sampling Bias Affects Poll Accuracy

Here is how sampling bias can negatively impact your results:

• Misjudged Preference

Remember those online polls that predicted a landslide victory for Hilary Clinton, only for the actual election results to be different and for Donald Trump to win? Well, part of the reason that happened is sampling bias: a majority of the people who responded were people who supported Hilary, so their opinion was what echoed in the polls. The problem was that they are not the only ones who voted; others who didn’t participate in polls voted for Trump.

• Lost Trust, Wrong Decisions, Poor Insights

If your poll results are consistently inaccurate, your audience will lose trust in your data, and people will stop participating in your surveys. For example, you run a fashion design business and you collect customer opinions for your summer collection, but only people who like nature-themed outfits took the survey, and you create a nature-themed collection. If this cycle repeats, people with different opinions will get tired of giving you their opinions because you’re going to disregard their opinions anyway.

The end result of them not giving you their opinion is that you have now successfully cut off a portion of your target audience. 

Proven Strategies to Reduce Bias in Volunteer Sampling

Eliminating bias in volunteer samples is not the easiest thing to pull off, but you can significantly mitigate it if you follow these strategies:

• Use screening questions and filters: Before allowing respondents to take the survey, ask preliminary questions to ensure respondents fit your target demographic. This includes asking them their age, location, education level, and even behavioral questions like, “How many times in a week do you watch Apple TV?”
• Randomize participant selection: Instead of taking participants on a first-come basis, select randomly from the list of potential respondents. This increases your chances of selecting both people who are very passionate about the survey and people who just responded because it’s a survey.
• Avoid leading questions: Phrase your questions neutrally to avoid leading respondents towards a particular answer. For example, instead of asking questions like, “Don’t you like this amazing rice recipe?” Phrase your question neutrally and say, “On a scale of 1-10, how do you feel about the taste of this new rice recipe?”
• Limit repeat or “over-eager” participants: Implement measures to prevent people from submitting multiple responses, which can disproportionately sway your results.
• Control the timing and distribution of your form: Distribute your survey at different times and through multiple channels to reach a broader audience, rather than just hitting one niche group. Varying the time means people with different schedules can comfortably answer your questions. It also means people in different time zones can answer your questions. Also, using different channels like emails, social media channels, and even one-on-one interviews means you cover the different ways people consume content and give their opinions.

How to Use Formplus to Minimize Sampling Bias

Modern form-building platforms offer features that can be invaluable in reducing bias:

• Conditional Logic: Use logic to present specific questions only to relevant participants based on their previous answers. For example, if the target audience is 25-35 year olds living in Texas. If a respondent chooses 18-24 as their age group, the form automatically ends their survey, preventing people outside of your target audience from populating your survey.
• Quotas and respondent caps: Set limits on the number of responses you’ll accept for certain demographic groups or the total number of respondents. This helps you prevent overrepresentation.
• Anonymous vs. tracked responses: You can also consider giving a disclaimer to your responders on the intro page to give a disclaimer that their responses will be anonymous. This can help you get more accurate responses from respondents who might answer differently to conform to what’s socially acceptable. But this can become a problem because people can use this opportunity to submit duplicate responses so the results go their way, especially for something like elections.
• Analytics Dashboard: The analytics dashboard allows you to see the demographic breakdown of your respondents in real-time and adjust your distribution strategy to achieve a more balanced sample.

Tips to Improve Response Rates Without Compromising Accuracy

The problem with trying to boost your response rate is that you may end up with exactly what you’re trying to avoid: bias. Here’s a safe way to go about it without tampering with accuracy:

• Incentives: Small non-monetary incentives (e.g., a discount code) can increase participation. However, try to give large incentives like cash prizes for surveys, it’s likely to attract participants primarily motivated by the reward. These types of participants can create multiple accounts or even use bots to submit as many responses as possible to get the reward.
• Keeping the form short and relevant: Long surveys lead to drop-offs; over 75% of survey participants drop out of surveys if they feel the survey is too long.  Keep your questions straightforward and only ask what’s necessary. Also, if your survey must be long, break it into multiple pages and add a progress bar that helps respondents visually gauge their progress and encourage them to finish.
• Mobile-friendly and accessible design: Ensure your survey is easy to complete on any device and accessible to people living with disabilities. The majority of people access websites using their phones, so if your survey isn’t mobile-friendly, you’re missing out on a significant portion of your target audience.

Best Practices for Analyzing Data from Volunteer Samples

Even with precautions, volunteer sampling may still come with some bias. Here are some best practices to help you mitigate it:

• Weighting responses: If you know the actual demographic distribution of your target population, you can apply statistical weights to your survey data to make it more representative. For example, if women are underrepresented in your sample, you can give their responses more weight to balance their representation.
• Segmenting your data: Analyze your data by different demographic groups or response patterns. This can help you identify the specific questions that bring in bias and the particular demographic with the bias.
• Reporting margins of error transparently: When presenting your results, always keep in mind that volunteer sampling is likely to introduce bias. So, provide an estimated margin of error to compensate for overrepresented opinions and set realistic expectations.

Conclusion

Yes, volunteer sampling is convenient because you receive responses from people who are eager to answer your questions; however, it also comes with some potential biases that you need to be aware of. The goal isn’t just to collect data, but to collect accurate data. You need to choose survey methods and tools that allow you to collect responses from a diverse audience using multiple channels and different features to help you randomize your sampling.
With Formplus, you get to create forms that help you collect responses across different demographics, analyze your responses to find patterns, and distribute them using multiple channels.

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