Voluntary sampling is one type of sampling method that is not probability based. this means that

A voluntary response sample is defined as a type of sample made up of self-chosen participants. These participants volunteer to take part in different research studies to share their opinions on topics that interest them.

A voluntary response sample is made up of persons who volunteer to take research surveys. These persons choose to respond to surveys because they may have a particularly strong opinion towards the subject or due to the convenience of joining the research study and ethical reasons. Participants opt to make up a voluntary response sample.

In stark contrast to random sampling, voluntary sampling yields a response bias as members are self-selected. The responses received through this type of sampling are commonly biased towards a particular topic. It is a type of non-probability sampling. 

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Uses of voluntary response sampling:

Frankly, this type of sampling method is not useful in many cases when the study conducted is of high importance, or big decisions have to be made based on the outcome of the results. The result of the study only reflects one aspect of the whole story. You are better off by applying random sampling. This method, however, may be deployed by TV show hosts or radio show hosts who want to affirm their opinion on a specific topic or topics where the volunteers also tend to lean towards the same idea as to the survey host.

An example of this method is when questions about evolution are asked to viewers/followers of a religious tv show. Their answer will always be based on the influence of the TV show, and they put away ideas of evolution even though there is ample scientific proof to back the theory. The outcome here is biased, unreasonable, and unusable in the real world.

With everything said and done, there is hope and scope for adopting voluntary response sampling to your study. Organizations use this method as a marketing tool to advertise their products all the time. Voluntary response sampling can help turn your existing customers into advocates of your brand, thereby potentially increasing your brand awareness and also the revenue and sales of your product.vVoluntary response example:

Polling through call-in radio shows is an ideal voluntary response sample example. Only a part of the population that listens to the particular radio station (and who chooses to answer by dialing in) participate in the poll. The responses collected do not accurately reflect the feelings of the entire population as only those people who choose to call in and take part in the study will bother to respond.

Voluntary response survey example:

A classic example of a voluntary response survey is American Idol, the singing competition television show created by Simon Fuller. Viewers get to have a say and vote for contestants via the mobile app, online, or via text. But viewers can vote more than once. Every week the voting window opens, viewers can vote up to ten times per voting method. Which means, one viewer is allowed to vote 30 times each week. This results in survey bias as the votes (answers) do not represent the population.

Characteristics of a voluntary response sample

Often, we come across surveys on social media regarding topics like ‘gun control,’ ‘abortion,’ ‘immigration policies,’ ‘police brutality,’ etc. The researcher does not directly contact the respondents to answer the survey. It depends purely on the individual’s willingness and awareness of the topic to participate in the study. The factors that encourage a person to respond to a survey are mostly – ease of responding, strong opinion about the subject, ethical reasons, etc.

The top 4 characteristics of a voluntary response sample are:

• Undercoverage: It occurs when most members of the population are not sufficiently represented in the sample. Only people who follow the talk show/ radio show/ TV show or those who belong to the community can take the survey. People who may have a different view or a neutral opinion about the subject may not be able to participate in the study as they do not follow the talk/radio/TV shows.
• Biased responses: Response bias occurs when sample members are self-selected volunteers, as involuntary samples. The respondents tend to answer questions based on strong opinions about the subject. A good sample is a representative, meaning each sample point represents the attributes of a known number of population elements. The result of the survey may not be accurate.
• Easy to gather data and access: Finding respondents for the survey is easy as persons who have a strong opinion about the subject tend to give their feedback about the topic. The individuals involve themselves as motivation is high regarding the issue.
• Errors in data quality: This goes without saying; responses will tend to lean towards a specific idea compromising the data quality and making the data received very biased and unusable.

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Tips to improve your voluntary response sample survey

A voluntary response sample, in most cases, will yield biased responses if the survey questions aren’t the right ones. The inability or absence of a desire to respond to questions correctly causes response bias. The reason being, respondents, already favor a specific type of outcome and will answer according to that outcome. If you are a researcher seeking quality, varied responses, always avoid this method. But if taking this approach is unavoidable, here are a few factors you must keep in mind to avoid bias as much as possible.

• Keep questions short and clear: Framing the right survey questions means avoiding response bias. Respondents are more likely to answer a clearly understood question compared to a long, complicated question.
• Avoid leading questions: While designing your questionnaire, avoid asking hypothetical questions. Also, avoid direct questions. For the question, ‘are you happy with the product or service?’, instead of giving a yes/no option, provide them with a variety of options to capture data more accurately. Example answer statements maybe – ‘I enjoy using this product/service,’ ‘This product/service meets my needs,’ ‘I wish I could get more out of the product/service,’ ‘The product/service is below my expectation.’
• Break down difficult concepts: To capture accurate data, break down, and simplify the questions for the respondent. Long questions cause fatigue and may not capture the actual feeling of the respondent.
• Provide simple, exhaustive answer options: As you do with simple, direct questions, keep the answer options simple too. Respondents will tend to answer questions better if they have straightforward options laid down in front of them.
• Use precise, straightforward language: Always avoid the use of difficult/high language in questions and answer options. Your respondent will choose the most accurate answer based on his understanding of the question and answer.
• Do not influence the answers: Never influence the answers. This will add no value to the study. You may receive many responses on your survey, but your approach will influence these answers and you will end up with irrelevant data.

Advantages of voluntary response sample

Here are the top advantages of surveying individuals using the voluntary response sample technique.

The advantages of a voluntary response sample are:

• A simple way to conduct a study
• Inexpensive
• Data easy to gather
• Easy to access
• Requires little effort by the researcher
• Has scope to provide rich, qualitative information
• Minimal effort required

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Research samples with QuestionPro Audience

QuestionPro Audience maintains a pool of 22 million+ survey respondents around the globe, who are pre-screened and mobile-ready to participate in various research activities. Many of these respondents sign up with us to share their thoughts on various topics that interest them. These respondents are profiled with over 300+ data points so that you can target the right respondents for your research study. Try QuestionPro Audience to help you come up with creative solutions for your business.

Definition: Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research.

Non-probability sampling is a sampling method in which not all members of the population have an equal chance of participating in the study, unlike probability sampling. Each member of the population has a known chance of being selected. Non-probability sampling is most useful for exploratory studies like a pilot survey (deploying a survey to a smaller sample compared to pre-determined sample size). Researchers use this method in studies where it is impossible to draw random probability sampling due to time or cost considerations.

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Types of non-probability sampling

Here are the types of non-probability sampling methods:

Convenience sampling is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to the researcher. Researchers choose these samples just because they are easy to recruit, and the researcher did not consider selecting a sample that represents the entire population.
Ideally, in research, it is good to test a sample that represents the population. But, in some research, the population is too large to examine and consider the entire population. It is one of the reasons why researchers rely on convenience sampling, which is the most common non-probability sampling method, because of its speed, cost-effectiveness, and ease of availability of the sample.

This non-probability sampling method is very similar to convenience sampling, with a slight variation. Here, the researcher picks a single person or a group of a sample, conducts research over a period, analyzes the results, and then moves on to another subject or group if needed. Consecutive sampling technique gives the researcher a chance to work with many topics and fine-tune his/her research by collecting results that have vital insights.

Hypothetically consider, a researcher wants to study the career goals of male and female employees in an organization. There are 500 employees in the organization, also known as the population. To understand better about a population, the researcher will need only a sample, not the entire population. Further, the researcher is interested in particular strata within the population. Here is where quota sampling helps in dividing the population into strata or groups.

In the judgmental sampling method, researchers select the samples based purely on the researcher’s knowledge and credibility. In other words, researchers choose only those people who they deem fit to participate in the research study. Judgmental or purposive sampling is not a scientific method of sampling, and the downside to this sampling technique is that the preconceived notions of a researcher can influence the results. Thus, this research technique involves a high amount of ambiguity.

Snowball sampling helps researchers find a sample when they are difficult to locate. Researchers use this technique when the sample size is small and not easily available. This sampling system works like the referral program. Once the researchers find suitable subjects, he asks them for assistance to seek similar subjects to form a considerably good size sample.

Non-probability sampling examples

Here are three simple examples of non-probability sampling to understand the subject better.

1. An example of convenience sampling would be using student volunteers known to the researcher. Researchers can send the survey to students belonging to a particular school, college, or university, and act as a sample.
2. In an organization, for studying the career goals of 500 employees, technically, the sample selected should have proportionate numbers of males and females. Which means there should be 250 males and 250 females. Since this is unlikely, the researcher selects the groups or strata using quota sampling.
3. Researchers also use this type of sampling to conduct research involving a particular illness in patients or a rare disease. Researchers can seek help from subjects to refer to other subjects suffering from the same ailment to form a subjective sample to carry out the study.

When to use non-probability sampling?

• Use this type of sampling to indicate if a particular trait or characteristic exists in a population.
• Researchers widely use the non-probability sampling method when they aim at conducting qualitative research, pilot studies, or exploratory research.
• Researchers use it when they have limited time to conduct research or have budget constraints.
• When the researcher needs to observe whether a particular issue needs in-depth analysis, he applies this method.
• Use it when you do not intend to generate results that will generalize the entire population.

Advantages of non-probability sampling

Here are the advantages of using the non-probability technique

• Non-probability sampling techniques are a more conducive and practical method for researchers deploying surveys in the real world. Although statisticians prefer probability sampling because it yields data in the form of numbers, however, if done correctly, it can produce similar if not the same quality of results.
• Getting responses using non-probability sampling is faster and more cost-effective than probability sampling because the sample is known to the researcher. The respondents respond quickly as compared to people randomly selected as they have a high motivation level to participate.

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Difference between non-probability sampling and probability sampling:

Sampling with QuestionPro Audience

Why restrict yourself to a limited population when you can get access to 22 million+ survey respondents around the globe? Every day, QuestionPro Audience enables researchers to collect actionable insights from pre-screened and mobile-ready respondents. Don’t let your survey receive biased answers. Good survey results are derived when the sample is truly representative of the population.