# 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

## 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 generalised to the wider population.

Quota sampling can be helpful for getting a broad picture of attitudes, behaviours, 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.

### 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: 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.

### 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 judgement regarding how many units you need to choose from each subgroup to acquire valid results.

### 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.

## 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 judgement-based selection by the researcher.

## Prevent plagiarism, run a free check.

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 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 categorised, which impacts the data collection process.
• 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.

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.

What is the difference between quota sampling and convenience sampling?

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

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