What is quota sampling?

A sample is made up of the participants selected from a population. It’s not usually possible to get information from everyone when you conduct research on a given group of people. You’ll need to select a sample instead.

The purpose of quota sampling is to control who or what is in your sample. To draw valid conclusions from your results, you must carefully plan how you will choose a sample that is representative of the group as a whole. This is known as a sampling method.

Quota sampling is often used when the number of desired people is insufficient since it gives you the most accurate representation given the circumstances.

There are two main ways to select samples for your research: probability sampling and non-probability sampling.

Probability sampling involves picking people at random, enabling you to make strong statistical conclusions about the whole group.

On the other hand, non-probability sampling is a straightforward method of data collection that relies on non-random selection. This is based on convenience or other criteria.

The two primary types of sampling methods can be broken down into several types, giving you access to various sampling techniques. Quota sampling is one such method, and we’ll look at it in this article.

Quota sampling is a non-probability or purposive sampling method where you choose subjects to participate in a study based on mutually-exclusive criteria. In other words, the individuals only meet one category’s requirements. They can’t be part of more than one subgroup.

As a researcher, you consider the participants’ known characteristics, including gender, age, and economic level. You then use this information to classify the subjects into the proper categories.

Next, you use sampling based on convenience or judgment to collect enough data from each subset for a quota.

After all of these statistics have been compiled, the next step is to use them to draw broad conclusions about the population as a whole. However, it is essential to consider the population percentages to guarantee that the sample numbers accurately reflect the whole group.

Unlike random sampling, quota sampling does not require researchers to adhere to precise standards. They can consider and follow recommendations instead.

Here are the steps to follow when compiling a quota sample.

Step 1: Defining the strata

When data is divided into subsets, the subsets are called strata. From here, you pick your sampling method.

When defining the strata, use your expertise in the topic area to identify which are relevant to your study and which are less critical. In general, you would include strata in your quota sample for one of two reasons: to ensure that you represent all groups or to compare the different groups.

Your desired quota sampling percentage will impact both the strata and the quotas. You’ll need to define the subgroups in a way that excludes other group members.

Step 2: Determining the quotas

The proportions that exist when dividing the groups must be maintained throughout the research.

Keep the aims of your study in mind as you divide the sample into groups. First, pick the overall stratum sample size. This will come down to available time and materials.

After that, count how many people are in each category. To accurately represent a population in a quota sample, you must first determine the population proportion of each category and then select sample numbers accordingly. Research is essential to this procedure.

For example, if your goal with quota sampling is to make it easier to compare groups, you have more freedom to choose numbers that work for you. Setting the same number of people in each comparison group is often a good place to start.

Let’s say you want to compare how fans of different science fiction series feel—in this case, you could give each fandom the same number:

• Star Trek: 200

• Doctor Who: 200

• Star Wars: 200

Another example would be a campus survey of university students. In this case, you’ll need to look at administrative records so that your sample’s distribution of majors matches that of the student body as a whole. If you want to collect data from 1,000 people and 10% of them are statistics majors, then you’ll need to set aside 100 spots for this group. The same procedure should be followed for each additional major.

Step 3: Recruiting

Finally, get out there and find more people to fill those quotas! Remember, alongside your key questions of interest, you’ll need to ask supplementary questions to get enough data to properly categorize your subjects.

Recruitment for quota sampling is similar to that of convenience sampling, except that not all potential participants are used. You’ll have to turn people away once the allotted spots are taken.

In the campus survey example above, you might position recruiters in high-traffic areas of the university. Recruitment for the science fiction fandom survey could be carried out by recruiters at conventions for each franchise.

Quota sampling is a popular method because it gives you a broad overview in a short amount of time.

Selecting representative samples using probability sampling methods, such as simple random sampling, is typically more effective. However, these procedures also tend to be laborious, time-consuming, and expensive.

Research studies benefit most from quota sampling in the following scenarios:

• When it’s used in both qualitative and quantitative research methods to learn more about a subgroup’s features or to explore links between groups. Researchers use it to obtain a sample that’s representative of the population under study when no sampling frame is available.

• When researchers need to get a broad picture of attitudes, behaviors, or situations. For example, it can help you figure out the range of concerns people have about an issue. Quota sampling is also helpful when respondents are recruited at random, such as in the case of a website, pop-up, and street surveys.

• Depending on the study’s specifics, certain research projects don’t call for high precision. These investigations would benefit greatly from quota sampling, and the method is well-suited for this purpose.

Keep in mind that quota sampling only offers data from the respondents themselves. In contrast to probability sampling, it is very vulnerable to research bias because it cannot be extrapolated to the entire population.

When it comes to market research tools, there are two main types of quota sampling: controlled and uncontrolled. Either there is a limit on the choice of samples, or there isn’t.

Controlled/proportional quota sampling

This is where the most important parts of the population are shown by taking samples based on how often they show up in the population being studied.

Proportional quota sampling is often used in polls and surveys, where the total number of people to be surveyed is usually known ahead of time.

Example of controlled/proportional quota sampling

Let’s say you are looking at where people in your city plan to vacation in the summer. You decide to take a sample of 1,000 people. To make sure the sample is representative of the population as a whole, you divide it into distinct subgroups:

• Gender identity

• Working status

• Age

• Housing situation

• Residential location

You can segment your sample into more manageable chunks (strata) by combining the factors above (e.g., working men under the age of 30). Strata are put together in a hierarchical structure. You can first stratify the sample according to demographic characteristics, such as gender and age. Then, you can stratify gender and age by working status and so on. 

You might use data from the last census to determine each subgroup’s quota. Based on your criteria, choose your sample in the same proportions as the population in the census.

Uncontrolled/non-proportional quota sampling

This type is more flexible than controlled quota sampling. In this type, you set the minimum number of units to be sampled in each category. The sample size need not correspond to the actual population distribution.

Example of uncontrolled/non-proportional quota sampling

Suppose you are researching how a clothing brand can better serve its customers by offering sizes for everyone. You decide to hold an online focus group because you don’t know how many customers you have or what they like to buy. You want about the same number of customers to choose sizes S–L and XL–3X.

You can then compare the answers you get from people who shop for sizes XL–3X to those from people who shop for sizes S–L. Comparing what both groups say can help you figure out how to make products that cater to all customers.

Differentiating between convenience sampling and quota sampling can be challenging. While both are non-probability sampling techniques, there are significant differences between them.

Convenience sampling is generally determined by proximity to the researcher or convenience of access. It’s impossible to draw a representative sample with convenience sampling because the researcher does not have access to the units’ characteristics before the investigation.

In contrast, quota sampling necessitates prior knowledge of the units’ characteristics to stratify them into subgroups and calculate the required number of participants from each category. In this way, you can guarantee that all relevant subgroups are included in the sample. Ideally, the proportions would match those seen in the population at large.

Another difference is that quota sampling doesn’t have a specified time limit. A higher quality output could be achieved if you have extra time to set up your experiment.

Quota sampling can be useful in research for several reasons. Significant benefits of quota sampling include:

• Saves money and time: Compared to probability-based approaches, quota sampling typically costs less and has a shorter processing time. You won’t have to generate a complete list of the population and hunt around for the dispersed samples that chance provides. Instead, you can use quick approaches to find individuals in accessible areas.

• Simple and easy to understand: Using quota sampling and the right research questions makes it easier for a researcher to figure out what the information and survey answers mean.

• Accurate representation of the population of interest: A quota sample is more likely to accurately represent a population than other non-probability methods. You can ensure that even the most minor subsets of the population are represented. It’s easier to investigate specific traits later on when your experiment is set up this way.

• Tracks subjects: Quota sampling keeps tabs on the demographics of survey takers and the proportion of eligible respondents who fall into each category. This facilitates data monitoring and enables enhanced management throughout the entire research project. It gives you an opportunity to look for overlapping traits between data sets.

Consider quota sampling if you have a modest budget or a short amount of time but still need to regulate the sample’s representation.

Quota sampling provides various benefits, but it also has some drawbacks.

First, unlike random sampling, quota sampling does not eliminate the possibility of bias. There is no sampling error to rely on because quota sampling is not a probability sampling method. Researchers make the final decision on who participates in a study, and in most cases, they do so based on practical considerations. You can’t extrapolate as much from the sample to the population if there’s a greater chance of sampling bias.

You could divide your quota sample into groups based on important characteristics to make it more representative in those areas. However, it might not be representative of the groups you leave out. For instance, if you divide people into groups based on gender, race, and age, the sample might not be a good representation of income or religion.

Taking customer surveys is a great way to learn more about your target audience and how you can better serve them. Having the right data enables you to target your audience with relevant and effective messaging. Plus, providing a good customer experience is a win-win for you and your company.

The key difference between quota sampling and other approaches is that selecting participants is not random.

While quota sampling is an extremely helpful research tool, it is not always the most appropriate strategy. You should be aware of the situations in which quota sampling could be most helpful. Equally, you should know when you would be better off considering other options.