What Is Quota Sampling? | Definition & Examples

Quota sampling is a non-probability sampling method that relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata.

The aim of quota sampling is to control what or who makes up your sample. Your design may:

  • Replicate the true composition of the population of interest
  • Include equal numbers of different types of respondents
  • Over-sample a particular type of respondent, even if population proportions differ
Example: Quota sampling 
Suppose you want to gauge consumer interest in a new meal kit delivery service in Washington, D.C.

Depending on your research goals, you can divide your population into several strata, such as:

  • Dietary preferences
  • Age group
  • Zip code

Let’s say you want to focus on dietary preferences. You divide the population into meat eaters, vegetarians, and vegans, drawing a sample of 600 people. Since the company wants to cater to all consumers, you set a quota of 200 people for each dietary group. In this way, all dietary preferences are equally represented in your research, and you can easily compare these groups.

You continue recruiting until you reach the quota of 200 participants for each subgroup.

When to use quota sampling

Quota sampling is used in both qualitative and quantitative research designs in order to gain insight about a characteristic of a particular subgroup or investigate relationships between different subgroups.

It is most commonly used in research studies where there is no sampling frame available, since it can help researchers obtain a sample that is as representative as possible of the population being studied.

Note that quota sampling only provides information about the responding sample. Unlike probability sampling, quota sampling cannot be generalized to the wider population and is at high risk for research bias.

Quota sampling can be helpful for getting a broad picture of attitudes, behaviors, or circumstances, such as understanding the range of concerns facing respondents about an issue. Quota sampling is also useful when your respondents come to you randomly, like through pop-up surveys, surveys embedded on websites, or street surveys.

Because quota sampling doesn’t require a large investment of time or budget, it can be completed fairly quickly. If you need results fast, quota sampling is a good method to consider.

Types of quota sampling

There are two types of quota sampling:

Proportional quota sampling

In proportional quota sampling, the major characteristics of the population are represented by sampling them in regards to their proportion in the population of study. Proportional quota sampling is often used in surveys and opinion polls, where the total number of people to be surveyed is typically decided in advance.

Example: Proportional quota sampling
You are researching summer travel intentions among residents of your city. You have decided to draw a sample of 1,000 people. To make the sample demographically representative, you divide your sample into distinct subgroups (strata):

  • Gender identity
  • Age
  • Working status
  • Residential location
  • Housing situation

By combining the above variables (e.g., working women under 25 years old), you divide your sample into distinct subgroups (strata).

Strata are combined in a hierarchical structure. First, the sample is stratified—for example, by gender identity, then within gender identity by age, within age groups by employment status, and so on. When a quota is defined by more than one variable, it is called interlocking.

You use information from the last census to determine the quota for each subgroup, selecting your sample in the same proportions as recorded for the population in the census according to your criteria.

You stop your sampling once you reach a number of respondents across the combined strata in the same proportions as the population of the city.

Non-proportional quota sampling

On the other hand, non-proportional quota sampling is less restrictive. Here, you specify the minimum number of sampled units you want in each category. In other words, non-proportional quota sampling does not require numbers that match the proportions in the population.

Example: Non-proportional quota sampling 
Suppose you are researching how a clothing brand can serve its customers better in terms of offering inclusive sizes.

Since you do not know the total number of customers or their shopping preferences, you decide to conduct an online focus group. You aim for an equal percentage of clients who choose size S through L and size XL through 3X.

The responses you gather from the latter group can then be compared with those given by people who shop for S–L sizes. Comparing input from both groups can help you understand how to create products that offer all customers the same ease of access.

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Example: Step-by-step guide to quota sampling

Unlike probability sampling methods, quota sampling does not require researchers to follow strict rules or a random selection process. However, there are still general guidelines to keep in mind.

You can draw a quota sample in three steps:

Step 1: Divide the population into strata

First, you identify important strata, subgroups in your population of interest. These subgroups must be mutually exclusive, meaning that units can only qualify for one subgroup.

Example: Forming your strata
Suppose you are investigating the career goals of students at your university.

Depending on your research goal, you can choose students from different class years, subjects, or any other variable for the stratification.

You decide that you want to look at the differences among majors, so your strata are the different programs (e.g., economics, engineering, education).

Step 2: Determine a quota for each stratum

Next, you estimate the proportions of each stratum in the population. These are your quotas. This estimation can be based on existing records, like administrative data, or previous studies. Otherwise, you are free to use your judgment regarding how many units you need to choose from each subgroup to acquire valid results.

Example: Determine your quota
You decide to examine the difference between economics and education students in regard to their career goals. The number of students from each major that you include in your sample should be based on the proportion of economics and education students to the total number of students in these two programs.

For example, if there are 2,000 university students enrolled in the two programs, made up of 800 (40%) education students and 1,200 (60%) economics students, your sample should comprise 40% education students and 60% economics students.

If your desired sample size was 100 students, your sample should include a quota of 40 education students and 60 economics students.

Keep in mind that you can divide your quotas into further subcategories. For example, your quota of 40 education students could include a proportional number of undergraduate and graduate students. If the proportion is 50/50, you would choose 20 undergraduate and 20 graduate students.

By dividing your quota into subcategories, you have assigned a weight to different traits within a target population and confirmed their representation in your study.

Step 3: Continue recruiting until the quota for each stratum is met

Once you have selected the number of units you need in each subgroup, continue recruiting units to take part in your research until each of your quotas is filled.

Example: Continue recruiting
Once you reach the quota of 40 education majors, you stop and only recruit economics students until you meet the set quota of 60 economics majors.

Difference between convenience sampling and quota sampling

It can be tricky to differentiate between convenience sampling and quota sampling. While they are both non-probability sampling methods, there are key differences between the two.

Convenience sampling is primarily guided by proximity or ease of access to the researcher. In convenience sampling, the characteristics of the units are not known to the researcher before the study, and for this reason, it is not possible to draw a representative sample.

On the other hand, in quota sampling, you need to know the characteristics of the units in advance in order to divide them into subgroups (or strata) and determine how many participants are needed from each stratum. In this way, you can ensure that diverse segments are represented in the sample, preferably in the proportion in which they occur in the population.

Note that non-proportional quota sampling can be quite similar to convenience sampling, as both methods use judgment-based selection by the researcher.

Advantages and disadvantages of quota sampling

Quota sampling is generally a robust method of getting a sample, but just like any other sampling method, it has advantages and disadvantages.

  • Advantages of quota sampling

There are several reasons why you may choose to use quota sampling in your research. Some major advantages include the following:

  • Quota sampling does not require a sampling frame or strict random sampling techniques, which makes this method quicker and easier than other methods.
  • Among non-probability sampling methods, quota sampling is the most likely to accurately represent the entire population, especially when you use proportional quotas. This helps avoid over or underrepresentation, and creates a sample that is more likely to match the population being studied.
  • The use of a quota sample allows for easier comparison between subgroups. Since you have broken your quota into strata, analysis of each strata is built into the model.
  • Disadvantages of quota sampling

However, quota sampling also comes with some challenges:

  • Since quota sampling doesn’t use random selection and the researcher decides who is included in the sample, it can lead to research biases like selection bias.
  • It is not always possible to divide the population into mutually exclusive groups. Specifically, people may belong to more than one group. There are times when people cannot be clearly categorized, which impacts the data collection process and can lead to omitted variable bias and information bias.
  • As only specific characteristics of the population are taken into account when you stratify your sample into subgroups, inaccuracy is very possible. For example, a study with subgroups of gender identity and income may not accurately represent other traits like age, ethnicity, or location in the final sample. This can also lead to information bias.

Frequently asked questions about quota sampling

What is the difference between quota sampling and stratified sampling?

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup (probability sampling). In quota sampling you select a predetermined number or proportion of units, in a non-random manner (non-probability sampling).

What is a sampling frame?

A sampling frame is a list of every member in the entire population. It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Why are samples used in research?

Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

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Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students.