What is quota sampling?

Quota sampling is a non-probability sampling method that makes sure certain subgroups, like age, gender or income, are represented accurately in your study.

It’s different from random sampling because instead of leaving it to chance, researchers get to set quotas for each group.

This makes it fast and really handy when you don’t have a full list of your audience. Quota sampling is also one of the most cost-effective ways to gather deep insights.

So, in this guide, we’ll give you a quota sampling definition, look at the different types, and explain how to do it step by step. We’ll also weigh up the pros and cons so you can see if it fits your next research project.

TL;DR

• Quota sampling helps you get the right mix of people in your survey fast. You set targets for key groups (like age, gender, region) and keep recruiting until you hit each quota.
• You can set quotas to match the population (proportional quotas) or oversample certain groups to make comparisons more reliable (non-proportional quotas).
• It’s a great fit when you need fast, practical insights with solid subgroup coverage,  like concept testing, tracking and comparing audience segments.
• The process is straightforward: define groups, set quotas, recruit and monitor. You choose the characteristics that matter (“strata”), decide sample sizes per group (proportional or not), then keep recruiting until quotas are met.
• The trade-off: because participants aren’t randomly selected, quota sampling can still introduce bias and you can’t confidently apply a margin of error.
• Tools like Attest make it easier by tracking quotas in real time and preventing over-quota responses, so you spend less time wrangling subgroups and more time interpreting what matters.

Quota sampling defined 

Quota sampling is a research method where you handpick participants so that your sample reflects specific characteristics of your target audience. This could include age, gender, region or income level.

Instead of relying on random samples, you set quotas for each group you want represented, then recruit people until each quota is filled.

Quota sampling is important because real-world audiences aren’t perfectly uniform. If you want insights that actually reflect your market, you need the right mix of people. 

This method gives you control over that mix, especially when time, cost or feasibility make random sampling unrealistic. For example, if you’re researching snack preferences in the UK, you might set quotas to match the population, say 51% women and 49% men, with age brackets proportionally represented.

Once each group hits the target number, you stop recruiting for that group. The result is that your sample looks much closer to the real market that you’re trying to understand. 

“Quota sampling is a way of controlling the audience you receive within a survey, rather than allowing anyone to enter it.”
– Nicholas White, Head of Strategic Research at Attest 

Types of quota sampling

When researchers talk about quota sampling, they’re usually referring to two main approaches. Both let you shape your sample around specific characteristics, but they work in slightly different ways.

Proportional (controlled) quota sampling

With proportional quota sampling, your sample mirrors the population in the same proportions you’d see in the real world.

If your market is 60% female and 40% male, your survey sticks to that exact ratio. The same applies to age, bands or any other key characteristics you need to match.

This approach is especially common in national polls, consumer behavior studies and brand perception research, where having a representative sample is essential.

Because each group is controlled so tightly, your final sample ends up looking much closer to the real audience you’re studying.

This type of quota sampling is a straightforward way to minimize bias and make sure your sample accurately reflects the population you’re trying to understand.

Non-proportional quota sampling

With non-proportional quota sampling, you’re not trying to mirror the population. Instead, you give each subgroup the same number of participants, or whatever number makes sense for your study, even if those groups aren’t equal in real life. 

This approach is great for early-stage, exploratory or comparative research where you’re more interested in spotting differences than building a perfectly representative sample. 

For example, if you’re running concept testing for a new product, you might recruit 100 respondents from each region you’re studying, purely so you can compare reactions side-by-side.

You could also oversample a smaller group or segment  so you have enough responses to compare it reliably (even if that group is smaller in real life).

This type of sampling gives you cleaner contrasts, faster insights and a lot more flexibility when your goal isn’t population accuracy but rather understanding how groups stack up against each other.

How quota sampling compares to other sampling methods

Quota sampling often gets confused with other sampling methods, so it helps to draw a clear line between them.

➡️ Convenience sampling: This is the loosest approach. You simply survey whoever’s easiest to reach. There’s no structure or sampling frame, and no guarantee your results will reflect the wider audience.

➡️ Random sampling: This method sits at the opposite end of the spectrum. Every person in the population has an equal chance of being selected, which reduces bias but can be expensive or time-consuming.

➡️ Stratified sampling: This method is a bit more structured. You divide the population into groups, like age or region, then randomly select people within each group. But it’s rigorous and needs more planning than quota sampling.

Applications of quota sampling

Quota sampling is really useful in projects where you need fast, structured insights without the heavy lifting of full randomization. 

It’s handy when your audience is diverse, or you want to make sure you’re hearing from the right mix of people. Here are some common use cases.

Consumer behavior research

You can use quota sampling when you want feedback from a representative sample of shoppers or customers, by age, gender or region, but don’t have the luxury of pure random sampling.

“The most relevant situations to use quota sampling are if you’re interviewing your customers or brand considerers, as they tend to have specific demographics.”
– Nicholas White, Head of Strategic Research at Attest

Product and concept testing across segments

If you’re testing multiple ideas or early prototypes, you may want equal numbers from different subgroups so comparisons are clean and meaningful.

This is where teams often use controlled or equal quotas before they determine a sample size for deeper testing.

Tracking studies and regular brand health checks

Quota sampling helps researchers maintain the same audience mix, so shifts in opinion are tied to behavior, not just changes in who you surveyed.

Hard-to-reach audiences

When certain groups are small or hard to find, quotas make sure they’re represented in your data collection, rather than lost in the noise.

Mixed methodology studies

In mixed methodology projects, when you run quantitative research first and qualitative research second, quotas help you ensure the quantitative phase includes the right people to invite into deeper interviews.

ℹ️ TL;DR: When to use quota sampling: 

• When you need a structured sample quickly
• When your audience has important subgroups
• When comparisons across groups matter
• When you want a sample that accurately reflects the people you’re studying, without the cost of full random sampling

How to perform quota sampling in 3 steps

Ready to roll up your sleeves? Performing quota sampling doesn’t have to be complicated. Here’s how it works in practice.

Step # 1: Define your groups (or “strata”)

Quota sampling starts with really understanding your target audience. The first step is deciding which characteristics matter the most for your study. 

Basically, which traits influence the opinions or behaviors you want to measure? These subgroups, called “strata,” are how you structure your sample.

You can use demographic traits like age, gender, income or region, or behavioral characteristics such as purchase frequency or product usage.

“If someone is looking into purchasers of a product, a robust view on frequency would be important to get the best insights, and therefore, one would consider applying quotas on a weekly and monthly basis.”
– Nicholas White, Head of Strategic Research at Attest

The goal is to ensure your sample reflects the different segments of your audience that actually matter for your research question.

An example of quota sampling would be if you’re studying snack preferences, you might define strata by age brackets and dietary habits, maybe 18 to 24, 25 to 34 and 35 to 44. 

You could then define them by whether someone is a vegetarian or eats everything. These become quotas you’ll fill in in later steps.

Step # 2: Select your sample size

Once your strata are defined, it’s time to work out your sample size for each one. This is where you assign quotas: The number of people to include from each subgroup.

In proportional quota sampling, your quotas reflect the population. For example, if your population is 60% female and 40% male, a 1,000-person survey would include 600 women and 400 men.

In non-proportional quota sampling, quotas are set equally or arbitrarily for easier comparison. For instance, you might recruit 500 women and 500 men to compare attitudes side by side.

Getting this step right is crucial because it sets the balance of your final sample and ensures your results are meaningful. 

💡Pro-tip: Want to learn more? We’ve broken down how to work out your sample size in 5 easy steps to make this easy.

Step # 3: Recruit participants and monitor until quotas are filled

With your quotas set, the next step is recruiting participants for each subgroup until every quota is met.

Once a group hits a target, recruitment for that category stops, which ensures no groups are overrepresented.

For example, say your survey on snack preferences needs 200 respondents aged 18 to 24, you stop accepting more from that age group once your quota is filled. The same goes for other strata, like gender, region or dietary habits.

In manual research, this step requires active tracking to make sure each quota is filled correctly. Automated platforms can streamline this process and help you manage multiple quotas simultaneously.

The pros and cons of quota sampling

As you now know, quota sampling is super handy, but it’s not always perfect. Before you decide to use this method, it helps to weigh the advantages against its limitations so you know when it’s a smart choice.

The pros of quota sampling 

Fast and cost-effective

Quota sampling is a time-saver compared with fully random or stratified methods. Because you’re targeting specific groups instead of recruiting at random, you can reach the people you need to faster.

This makes it cost-effective, too, as there are fewer wasted invitations or screenings. 

For example, if you need insights from certain age or income brackets, you can focus only on those segments, which cuts down both recruitment time and expenses. 

Accurately represents the entire population

By filling quotas based on characteristics, quota sampling ensures your final sample mirrors the broader population you’re studying.

Whether you’re measuring brand perception or consumer behavior, this approach helps your sample reflect key segments like age, gender or region.

Unlike convenience sampling, which can skew results, quota sampling helps you build a more representative sample. This improves the reliability of your insights and makes your conclusions more actionable.

Flexible and simple to implement

Quota sampling is straightforward, even for complex research projects. You simply define your groups, set quotas and recruit participants until each target is filled.

This flexibility works for both proportional and non-proportional quota sampling, as you can tailor the approach to your goals.

It’s also easier to track and adjust on the fly compared with fully randomized designs, which makes it an accessible option for researchers at all experience levels.

The cons of quota sampling 

Selection bias and convenience sampling risk

A drawback of quota sampling is the potential for selection bias. Because participants aren’t chosen randomly, researchers or interviewers may rely on convenience or personal judgment when recruiting, which could skew results.

For example, recruiting the easiest-to-reach respondents could overrepresent certain behaviors or opinions.

While quotas ensure subgroup representation, the underlying sampling may still be biased, which makes it important to monitor recruitment closely and combine quota sampling with careful participant selection.

Limited generalizability

Quota sampling doesn’t give everyone in the population an equal chance of selection. That means findings can be reliable for your final sample, but they may not confidently apply to the entire population.

For instance, even if your quotas mirror key demographics, underrepresented characteristics or unseen biases can limit broader conclusions.

Researchers should recognize that quota sampling sacrifices some generalizability for speed and structure, which makes it ideal for more targeted insights rather than universal claims.

Dependence on outdated or inaccurate quota data

Quota sampling relies heavily on accurate population data to set targets for each subgroup. If this data is outdated or flawed, your sample might be misaligned with reality, which can skew results.

Relying on old census numbers, for example, could underrepresent emerging demographics. 

So, ensuring that the sample is current and reliable is crucial. Otherwise, your quotas risk creating a sample that doesn’t reflect your target audience’s true composition.

Lack of measurable sampling error

Because quota sampling is non-random, traditional statistical measures like confidence intervals or margins of error aren’t valid.

Without these metrics, it’s difficult to quantify how much the results might fluctuate if you repeated the study.

Researchers must acknowledge this limitation when interpreting findings. Even with carefully filled quotas, there’s no formal way to measure sampling error. 

This may reduce the perceived reliability of conclusions compared with random sampling approaches.

Inconsistent quality control

Quota sampling often depends on interviews or recruiters to monitor and fulfill quotas accurately. If they interpret instructions differently or prioritize convenience, inconsistencies can creep in.

For example, one interviewer might stop recruiting too early, while another oversamples a subgroup. This variability undermines reliability and makes it harder to ensure each quota is filled accurately.

Rigorous monitoring or automated platforms can help maintain data consistency and integrity.

Quota sampling with Attest

The Attest platform makes it straightforward to manage quota sampling without the issues that come with manual tracking.

Once you’ve defined your characteristics and set your quotas — for example, by age, gender, region or other demographic factors — Attest automatically recruits participants to meet those targets.

Each quota is monitored in real-time, so once a group reaches its target, the platform stops additional responses from that subgroup. This helps maintain a representative sample.

Researchers can use both proportional and non-proportional quota sampling, but this depends on whether the goal is population monitoring or easier comparison between subgroups. 

Attest also helps to ensure the sampling is balanced across all relevant characteristics, minimizing over- or under-representation.

Last, but not least, the platform allows teams to focus on interpreting results rather than manually tracking each subgroup. It also keeps them in control of sample composition and data integrity.

Why quota sampling deserves a place in your research toolkit

Quota sampling gives researchers a practical way to reach a representative sample quickly, whether you’re using proportional quota sampling to mirror the population or non-proportional sampling for easier subgroup comparisons.

It also helps minimize bias and ensures your sample accurately reflects your audience.

While it has limitations, like potential selection bias and reliance on accurate quota data, it’s a flexible tool for tracking things like brand perception, conducting concept testing or exploring niche audiences.

Want to make the most out of your next research study? Learn how to choose the right method for your goals in our guide to different types of surveys.