One of the most common methods of gathering data in surveys and research studies involves some type of quota sampling
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.
This post discusses quota sampling and offers some examples to help you better understand how it works and when you might use it.
What is quota sampling?
A quota refers to some specific requirement or category.
So quota sampling is a type of non-probability sampling where you create a sample with individuals that represent your target market. These individuals are chosen according to quotas, or categories, that represent specific characteristics of your audience.
Ideally, the final sample should have similar proportions of individuals with these characteristics as the entire audience.
For example, Fashion Revolution commissioned a survey in 2020 to find out how supply chain transparency and sustainability influenced EU customers’ decisions when buying clothing, accessories and shoes. So they applied quotas on the age group of 16-75 within the EU’s biggest markets.
In addition to this, you need to ensure that the composition of the final sample meets the study’s quota requirements. Because with every extra quota, it may take longer to locate these individuals. This adds costs and time to the quota sampling process.
There are different methods for doing quota sampling; read on to learn more.
Side note: Let’s get technical…
….and talk sampling types. One key difference between probability and non-probability samples is whether they are simple random samples or complex samples.
Simple random samples draw from the entire audience using variables like birth date or social security number—the same way every person gets chosen for jury duty.
However, if you were working with smaller groups or specific topics—say parents who had lost children to gun violence—you might use complex random sampling instead.
Quota sampling methods
Quota sampling methods can be divided into two broad categories:
- Controlled quota sampling: This method imposes specific limits on the researcher’s choice of samples. For eg., the sample size for each category cannot go beyond 50.
- Uncontrolled quota sampling: This does not place any limits or restrictions on the researcher’s choice of samples.
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Why is quota sampling important?
- Quota sampling allows you to study specific segments within a larger population. When used correctly, quotas are an effective and ethical way to collect data from these segments.
- Quota sampling helps ensure that enough participants from each segment are included in the study. Diversity and inclusion studies are a good example of this.
- This type of sampling also allows you to investigate traits of certain segments in greater detail. For eg., the shopping habits of millennials versus Gen Z.
- Moreover, quota sampling is ideal when you want to investigate relationships or overlapping traits between segments.
- The quota sampling method helps address some biases that are inherent in non-probability sampling, like survivorship bias.
- Quota samples can help determine a more precise audience average than simple random sampling.
- Quota sampling is one of the most popular forms of sampling because it is equally useful for business or research. Also, you can choose to do it via personal interviews, offline questionnaires or online surveys.
How to use quota sampling
Stage 1: Divide your audience into segments based on the relevant quotas – like age, gender, income, or job role.
Stage 2: Identify the proportions of these segments in the audience. These same proportions will be applied to the sample.
Stage 3: Select participants from each segment while following the proportions noted in the previous stage.
For 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 given population, 43% of the students are male and 57% are female. So for a sample size of 1,000, the researcher knows that 430 men and 570 women will be required from that audience.
Stage 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.
More quota sampling examples
Studying market trends during COVID 19
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:
- They counted U.S. businesses that were reported as temporarily closed and then reopened between March 1, 2020 and September 30, 2021.
- Temporary closures were reported by the business owner on the business’s Yelp page – including by changing its hours or through a COVID-19 banner.
- Each temporarily closed business was counted only once, on the date of its most recent closure.
- One-day closures that appear to be unrelated to the pandemic were excluded from the quota.
- The reopenings quota included the termination of temporary closures by a business through Yelp’s temporary closure feature, or by the editing of business hours (excluding holiday closures).
- Each reopened business was counted only once – on the date of its most recent reopening during the time period above.
A/B testing content for different followers
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?
- Consider running a series of polls that helps you get to know the people in your event. For example, asking them to choose an answer that describes their job role.
- Based on the job roles that pop up most often, you can craft relevant content that will be helpful for them.
- Then, you can use the quota sampling process to segment your audience into lists (based on their answers to the job role poll).
- Share the content and measure the opens, clicks and response rates.
Wrapping up: Things to keep in mind while doing quota sampling
#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 pre-defined 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 the global happiness mean. Factors like this are hard to catch with quota sampling alone.
#2. Ultimately, the researcher uses their personal judgement to select the final sample. So despite our best efforts, it is possible that a little bit of bias will creep into the quota sampling process.
As long as we keep these things in mind during data collection and analysis, it’s possible to have greater confidence about our results and their accuracy.