If you received an invitation to take a survey, you would probably be more likely to actually participate if the topic of the survey interested you. That’s the heart of voluntary response sampling. Like all other methods of sampling, voluntary surveys have their pros and cons. It’s one of the easiest ways to sample quickly and get responses, but it can also result in voluntary response bias. Read on to learn more about voluntary response sampling.
What Is a Voluntary Response?
Voluntary response is when someone volunteers to be a part of your sample. When you’re surveying, you have a population or the whole group of people you want to learn about. But most of the time, you can’t survey the entire population, so you select a smaller group, and that’s your sample. Voluntary response sampling is when you select that sample by letting your population volunteer to be in it.
So what is voluntary response bias? Voluntary response bias is referring to how allowing your sample to self-select skews your data, and you don’t actually get results that are representative of your whole population. Voluntary response bias isn’t always inherently bad; it’s not considered the worst of the biases that could arise in your sampling. But it can lead to more extreme results than would actually be true for your population as a whole.
Why Is Voluntary Response Sampling Biased?
When you create a survey, you want to get results that are representative of your population, so you can make the right decisions based on the data. If you’re allowing your sample to self select, you’re not getting data that shows your entire population. You’re only getting data that reflects your sample. That leaves you with results that aren’t generalizable, and generalizing them anyway is where bias becomes a real problem.
Voluntary response also opens your survey up to the possibility of favoring more extreme results than your population actually experiences. Think about it this way: respondents are more likely to volunteer for a survey if they’re passionate about the topic. The passionate responses can skew your results. You’ll have the customers that loved your product the most (or had a terrible experience) responding instead of your average customer. That could lead to bias problems. You could end up making decisions on products and services that are slightly skewed by voluntary response bias.
What Is an Example of Voluntary Response Sampling?
The classic example of voluntary response sampling is the call-in survey. A company runs an ad to call in with thoughts on a product or service, and they use that data to tweak the product or service. The customers who feel most strongly about the product—whether positively or negatively—are the ones who call in. While those respondents can still give great feedback, they are the ones who felt most passionately, which doesn’t necessarily represent the entire population of customers.
Most commonly, companies run across voluntary response bias when they send out surveys to all their customers and then rely on the ones who respond to be the entire sample. Let’s say a company puts out a new line of shoes, and they email every person who bought the shoes with a survey link. The customers who respond are those who most likely have something to say about the shoes, and that could potentially skew the data.
For example, let’s say most of the respondents were those who had a bad experience, and the company decides to modify the entire product based on the survey. Maybe that is the right decision, but maybe the majority of customers were fine with the shoes, but they didn’t respond to the survey. That’s where problems with voluntary responses happen. A random sample is a more reliable and accurate way to learn about a population. By randomly selecting a sample, you get more accurate and more generalizable results that you can more confidently use to make decisions for your company and products.
Advantages and Disadvantages of Voluntary Response Sampling
Voluntary response sampling has some very obvious disadvantages. Using voluntary responses can allow bias to creep in on the results and skew data. Voluntary response also can introduce undercoverage bias. Your population could potentially be a complex and diverse group of people. When you use voluntary response, only those who are inclined to respond are represented in the results. That means you could be undercovering your population and missing out on key trends that are important to note.
But there is a major advantage to using voluntary responses. Randomly selecting a population and getting those chosen to participate in the survey can be difficult, time consuming, and expensive. Voluntary response bias can eliminate that. You aren’t spending time tracking down participants and designing your survey since your sample is just those who are already willing to participate in your survey.
Still, using voluntary response sampling to save time and resources can be risky. There is always the high risk for introducing voluntary response bias that could have a negative effect on your research. It’s important to keep that in mind when weighing the benefits and risks of voluntary responses. Vital surveys that may influence key business decisions might benefit from random sampling rather than voluntary sampling to make sure the results are as accurate to the population as possible.
The Bottom Line
Voluntary response bias is a real risk researchers face when using voluntary response sampling. But considering what voluntary response bias does to a survey also opens up a discussion of the larger challenges with surveying. Choosing methods and creating accurate, simple, and powerful surveys is important, but it’s difficult to do—especially on a deadline or on a tight budget.
That’s what InMoment strives to relieve. InMoment is here to help you collect good data to create beautiful surveys that give you the data power to guide your business decisions. Easily gather the information you need with InMoment. Get started with InMoment to create the surveys you need.
