Articles

The complete guide to quota sampling

Dr Andrew Gordon

|July 30, 2024

Quota sampling is a non-probability sampling technique. The researcher sets specific criteria and sampling targets to ensure their final sample represents the key traits in the wider population.

With this technique, you don’t randomly select participants. Instead, quota sampling stratifies the sample during recruitment according to pre-specified quota definitions. The quotas are assigned proportionally and mimic the population's proportions, so the sample is representative of the population you’re examining.

For example, if a researcher is studying product preferences in the U.S. and wants their sample to be like the national population, they may set quotas:

51% of the sample must be female
30% must come from the South region
15% should be African American
25% should fall into the 35-49 age range
And so on

As participants are recruited, researchers watch their progress, checking that they fill quotas with the right proportions. Once the quotas are filled, recruitment can stop, as the sample now has enough of those specific characteristics.

Quota sampling lets you make an artificial, non-random sample. It reflects the diversity and distributions in the real population.

Key traits of quota samples

Here are some defining traits and components of quota sampling:

Non-random: Participants aren’t randomly selected but deliberately sourced to fit pre-defined quotas.
Stratification: Samples are stratified by separating or grouping the population according to relevant variables.
Representative samples: The final sample's composition should proportionally mirror the distributions of the wider population being studied.
Category definition: Clear, mutually exclusive categories are defined upfront for quota variables.
Quota setting: Proportions in the wider population determine quota definitions and targets.

Examples of quota sampling in research

To illustrate quota sampling in action, let's review some hypothetical examples from different contexts.

Academic research

Let’s say university researchers want to study factors that influence online shopping by U.S. consumers. They aim to survey a sample. The sample should be like the U.S. population in its characteristics, such as:

Age
Gender
Geographic region
Household income levels

They use census data to set precise quotas. For example, 13% of the sample must come from the West, 28% should report incomes between £50-100K, and 14% should be aged 18-24. As responses start coming in, they monitor and adjust recruitment tactics to hit those targets.

This produces a sample meant to mirror the true national population, letting researchers draw more reliable conclusions.

Corporate sampling research

A brand wants to evaluate its global reputation and perception with a survey. However, surveying every person in all their markets would be impractical and costly.

Instead, the company's researchers set quotas for the composition of samples for each of their top 12 regional markets. Quotas are based on population distributions for variables like:

Age
Gender
Urban vs. rural
Education levels
Income tiers

So, in India, 35% of the sample must be rural, while in France, 22% must come from the top income bracket. Their agency recruits panelists. The aim is to monitor progress towards hitting each quota.

This allows systematic analysis of brand sentiment. It accounts for major demographic variations across markets, producing more nuanced insights than a purely random or convenience sample could.

Political polling

One common use of quota sampling is in political polling. This happens during elections or referendums. Before polling day, researchers set voter quotas carefully aligned with:

Gender
Age
Geographic region
Political affiliation
Likely voter status
Other key characteristics

As data is collected, pollsters fill the quotas for segmentation factors. For example, 30% left-leaning, 25% aged 35-49, 15% Hispanic or Latino, etc. They fill them in proportion to the latest census and population benchmarks.

This quota-based approach ensures that the polls accurately reflect the demographics and voters of the entire group being studied.

These examples show that quota sampling lets researchers systematically recruit samples. It captures the diverse demographics within real populations.

Quota sampling is powerful - but it also has key limits and risks if not done rigorously.

Advantages and disadvantages of quota sampling

Like any method, quota sampling has its own strengths, weaknesses, and tradeoffs. Weigh them all carefully.

Advantages

Cost and resource efficient

Quota sampling is more cost-effective than true probability techniques like stratified random sampling. It also requires a less intensive recruitment process and has lower operational demands.

Control over composition

Researchers have direct control and transparency over the sample makeup. They actively build it to precise specifications using quotas. Quota sampling methods don’t leave sample compositions to chance. Instead, they empower researchers to sculpt samples that capture unique intersections and are relevant to their work because of the attributes they contain.

Targeted segmentation

Targeted segmentation allows focusing on subgroups. They can be based on complex intersections that go beyond what other sampling methods can do. Quota sampling defines quotas around specific combinations of demographics, behaviors, and traits. It lets researchers target the precise audience slices they need to study. This granular focus is difficult to achieve through most broader probability sampling methods.

Deploying in hard-to-reach populations

This route provides a viable path to studying very niche segments or rare target groups by deliberately sourcing those specialized profiles through quotas. Quota sampling techniques let researchers set specific recruitment targets for unique audiences. This is better than using randomization alone, which often fails to accurately represent tiny populations.

Faster than probability sampling

Using quotas to define the desired sample upfront lets researchers recruit more strategically and faster than with pure probability sampling. This approach allows for quicker analysis and the ability to gain insights sooner.

Focused insights

The aim here is to focus on creating a sample that accurately represents the whole population's statistics. Being purposeful about sculpting samples through quotas allows researchers to analyze the specific audience segments, demographic intersections, or niche groups which are most pertinent to their work. This focused approach yields rich insights, enabling generalization from the sample to the wider population.

Disadvantages

Lack of probabilistic strength

Quota sampling is a non-probability technique. While it allows the computation of sampling error and population estimates, these may be less rigorous or accurate than with true probability sampling approaches.

Potential sampling bias

If researchers don't carefully set and check the quotas, selection bias may creep in through subjective recruitment methods. This means that the researchers' personal judgments and decisions during the recruitment process could inadvertently influence which participants are included. Even with quotas, human bias could still distort the sample's composition, whether consciously or unconsciously. This bias can occur if the sourcing and screening of participants aren’t systematic and rigorously controlled.

Limited generalizability

Imposing quotas means the sample will only represent the variables we control for, potentially missing some aspects of the population's full diversity. This limitation is common to most research methods, as nearly all are non-probabilistic in nature. While relaxing the need for pure randomization, quota samples still limit the conclusions and restrict what can be projected to the total population without additional detail.

Access issues

Without access to large sample pools, finding participants with the exact attributes they need can be hard. Even with careful planning, finding the needed character combinations may not always be possible. It depends on the study's scope and available participants.

Resource intensive planning

Assembling the quota sample may be quicker. But planning, defining, and calibrating your quotas upfront takes time. It requires effort from researchers with deep knowledge of the population. Aligning the quotas precisely is important - they must reflect the true demographics of the underlying population. This is a critical first step that demands close attention to detail.

When to use quota sampling

Having a balanced view of quota sampling shows its strengths and weaknesses. But there are certain research goals and situations where this method shines:

Enhancing convenience samples: Simple convenience or sampling won’t work. Quota sampling provides a pathway to more representative samples.
Studying subgroups or niche populations: Quota sampling is great for researching specific demographics, compositions, or attribute intersections.
Validating findings across groups: Can identify if existing findings are consistent across key population groups.
Budgetary and resource constraints: When truly random probability-based sampling is cost- or operationally-prohibitive.

Quota sampling can be a better method for exploration or validation. However, it shouldn’t replace random sampling in studies that aim to generalize to populations.

Consider your research priorities, constraints, and analytical goals. They will help you decide if quota sampling is the right choice. It can produce the sample type and level of representation you need.

Quota sampling strikes a balance. It has more systematic rigor than purely convenience-based methods. But it avoids the intensity and costs of true probability sampling.

Summary: meeting the quota

Choosing the best method means aligning your sampling strategy to the goals, limits, and impact of your research. Experienced researchers who execute it diligently find that quota sampling is powerful. It captures focused, granular, and projectable insights for targeted analyses and informed decision-making.