Quota sampling – What is it, definition and concept | 2022

Quota sampling is one that is carried out by selecting the members of the statistical sample with the aim of complying with a certain level(s) of representation. This, for certain segments or subgroups of the population.

In other words, through this method, the statistical sample is intended to contain an established percentage for one or more specific groups that make up the target population.

When we refer to specific groups, these can be identified by characteristics or qualities that some individuals in the population share. For example, it can be gender, age, the city where they reside, etc.

Another point to keep in mind is that this technique is a type of non-probabilistic sampling. That is, the researcher does not select the sample at random, but rather, in this case, intentionally selects certain individuals to meet the established quota.

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Likewise, it is worth emphasizing that the established quotas are not created arbitrarily, but based on what is observed in the statistical population. For example, if it is known that 50% of the population is between 20 and 30 years old, when selecting the sample (using quota sampling) 50% of people within this age range will be selected.

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Advantages and disadvantages of quota sampling

Among the advantages of quota sampling we can highlight:

  • It is less expensive compared to probabilistic methods, so it is ideal when there are not many resources for research.
  • With this method, you ensure that there is no overrepresentation or underrepresentation of certain groups in the population. For example, it is guaranteed that the sample will have 50% from Madrid and 50% from Murcia, so that neither group has more representation than the other.
  • It can be convenient when the speed of the study is more valuable than its statistical accuracy.

However, this technique can also have some disadvantages:

  • It may be that the researcher considers the fee according to one criterion, but not another, which could also be important. For example, it may have been segmented by gender, but not by age, and even the latter could be more relevant.
  • As the selection is not random, it can fall into bias. This is, for example, because the researcher can choose individuals to whom he has easy access, as long as he meets the established quota. However, in this scenario, a faithful representation of the statistical population may not be guaranteed.


An example of quota sampling could be the following: A company wants to conduct an opinion poll on one of its products. Thus, you know that 30% of your customers are between 18 and 25 years old, 40% are between 25 and 45 years old, and the rest (30%) are over 45 years old.

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So, when selecting the statistical sample, which will include 1,000 interviewees, we will have 300 young people between 18 and 25 years old (30% of 1,000), 400 adults between 25 and 45 years old, and another 300 people over 45 years old. .

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