General
Quota Sampling 101: Definition, Methods and Examples
Article written by Parvathi Vijayamohan
Content Marketer at SurveySparrow
9 min read
25 February 2024


General
Article written by Parvathi Vijayamohan
Content Marketer at SurveySparrow
9 min read
25 February 2024


Quota sampling is one of the most common methods for collecting data in surveys and research studies.
However, it’s important to understand exactly what this method calls for, as well as its benefits and disadvantages, so you can decide whether or not it’s the right approach for your purpose.
In this article, we’ll cover the following:

A quota refers to a specific requirement or category.
So quota sampling is a type of non-probability sampling in which you create a sample of individuals who represent your target market. You choose these individuals according to quotas, or categories, that represent specific characteristics of your audience.
Moreover, it’s vital to ensure that the final sample’s composition meets the study’s quota requirements. With every extra quota, it may take longer to find suitable respondents. This adds costs and time to the quota sampling process.
Quota sampling methods can be divided into two broad categories:

Now, quota sampling is a great way to make sure your research represents certain groups of people. But it’s not always easy.
Finding the right people can take a lot of time and money. Plus, sometimes, our own biases can affect who we choose. And because quota sampling often uses smaller groups, there’s a higher chance of mistakes in the results.
In such cases, an audience panel could provide an optimal solution. Moreover, it is important to have a tool that mitigates these challenges and streamlines the process. Perhaps SurveySparrow’s quality panel can help you:
Above all, the platform facilitates:
Why don’t you take it for a spin? It’s free!
Now, that you have a tool in hand, let’s look at…
Step 1: Divide your audience into segments based on the relevant quotas – like age, gender, income, or job role.
Step 2: Identify the proportions of these segments in the audience. These same proportions will be applied to the sample.
Step 3: Select participants from each segment while following the proportions noted in the previous stage.
Step 4: Finally, double-check to ensure that the sample represents your audience. The point is not to get a perfect match – that would be impossible. The point is to get a sample where the vital characteristics of each segment are included.
Example:
Let’s look at a target audience of college students at a local college.
Because the researcher can access this data, she knows that in this population, 43% of the students are male and 57% are female. So for a sample size of 1,000, the researcher calculates that she will need 430 men and 570 women from that audience.

In its Q3 report, released in October, Yelp found that 85% of businesses in the US that went through a temporary closure during the pandemic have reopened.
To get this percentage, Yelp followed a few criteria:

Here’s a hypothetical example: you’re promoting an industry event on your LinkedIn page. Lots of people have signed up. This is a golden opportunity for you to serve them better with awesome follow-up content.
How do you do that with quota sampling?

Fashion Revolution commissioned a notable survey in 2020 as part of a three-year project by the European Fair Trade Commission.
Constant check-ins are vital for new hires and seasoned staff too! However, you may not have the time or resources to regularly share check-in questions with everyone at your company.
In this situation, the quota sample method can be a time-saver.
1. Quota sampling is not easy to generalize to the overall audience because it doesn’t account for deviation within segments.
For example, a global study on happiness measures how happy a nation is according to certain traits, and most nations fall along this mean.
However, some nations might have a very different cultural idea of what happiness is, so they fall further away from global happiness mean. Factors like this are hard to catch with quota sampling alone.
2. Choosing the final sample is ultimately up to the researcher’s judgment. Despite our best efforts, it is possible that bias will creep into the quota sampling process. When we keep these things in mind during the data collection and analysis, we can have greater confidence in the results.
And before you go, don’t forget to give SurveySparrow a try!

Thousands of brands trust SurveySparrow to turn feedback into growth. Try it free today!

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