Quota sampling is a type of non-probability sampling technique. Non-probability sampling focuses on sampling techniques that are based on the judgement of the researcher [see our article, Non-probability sampling explained, to learn more about non-probability sampling]. With proportional quota sampling, the aim is to end up with a sample where the strata (groups) being studied (e.g. males vs. females) are proportional to the population being studied [see our article, Sampling: The basics, if you are unsure about the terms units, sample, strata and population]. This article (a) explains what quota sampling is, (b) how to create a quota sample, and (c) the advantages and disadvantages of quota sampling. Whilst there are two types of quota sampling (i.e. proportional and non-proportional), we focus on proportional quota sampling.

• Quota sampling explained
• Creating a quota sample
• Advantages and disadvantages of quota sampling

Quota sampling explained

Imagine that a researcher wants to understand more about the career goals of students at a single university. Let’s say that the university has roughly 10,000 students. These 10,000 students are our population (N). Each of the 10,000 students is known as a unit (although sometimes other terms are used to describe a unit; see Sampling: The basics). In order to understand more about this population, we only need to examine a sample (n) of students, not the whole population of 10,000 students. Furthermore, sometimes we are interested in particular strata (groups) within the population. Therefore, the quota sample involves dividing the population into two or more strata (groups). These strata are expressed as H.

Building on the example above, imagine we were interested in comparing the differences in career goals between male and female students at the single university. If this was the case, we would want to ensure that the sample we selected had a proportional number of male and female students relative to the population. Therefore, the total number of male and female students included in our quota would only be equal if 5,000 students from the university were male and the other 5,000 students were female. Since this is unlikely to be the case, the number of units that should be selected for each stratum (that is, the number of male and female students selected) will vary. We explain how this is achieved in the next section: Creating a quota sample.

Creating a quota sample

To create a quota sample, there are three steps:

1. Choose the relevant stratification and divide the population accordingly
2. Calculate a quota for each stratum
3. Continue to invite units until the quota for each stratum is met

Each of the above steps is discussed in turn.

First: Choose the relevant stratification and divide the population accordingly

If we wanted to look at the differences in male and female students, this would mean choosing gender as the stratification, but it could similarly involve choosing students from different subjects (e.g. social sciences, medicine, engineering, education, etc.), year groups, or some other variable(s).

Second: Calculate a quota for each stratum

The number of units that should be included in each stratum will vary depending on the make-up of each stratum within the population. If we were to examine the differences in male and female students, for example, the number of students from each group that we would include in the sample would be based on the proportion of male and female students amongst the 10,000 university students. For example, if there were 6,000 male students (60% of the total) and 4,000 female students (40% of the total), our sample would need to be made up of 60% males and 40% females. If our desired sample size was 100 students, this would mean our sample should include 60 male students and 40 female students.

Third: Continue to invite units until the quota for each stratum is met

Once you have selected the number of units you need in each stratum, you simply need to keep inviting participants to take part in your research until each of these quotas are filled.

Advantages and disadvantages of quota sampling

• Advantages of quota sampling
  
  Quota sampling is particularly useful when you are unable to obtain a probability sample, but you are still trying to create a sample that is as representative as possible of the population being studied. In this respect, it is the non-probability based equivalent of the stratified random sample.
  
  Unlike probability sampling techniques, especially stratified random sampling, quota sampling is much quicker and easier to carry out because it does not require a sampling frame and the strict use of random sampling techniques (i.e. probability sampling techniques). This makes quota sampling popular in undergraduate and master’s level dissertations where there is a need to divide the population being studied into strata (groups).
  
  The quota sample improves the representation of particular strata (groups) within the population, as well as ensuring that these strata are not over-represented. For example, it would ensure that we have sufficient male students taking part in the research (60% of our sample size of 100; hence, 60 male students). It would also make sure we did not have more than 60 male students, which would result in an over-representation of male students in our research.
  
  The use of a quota sample, which leads to the stratification of a sample (e.g. male and female students), allows us to more easily compare these groups (strata).
  


• Disadvantages of quota sampling
  
  In quota sampling, the sample has not been chosen using random selection, which makes it impossible to determine the possible sampling error. Indeed, it is possible that the selection of units to be included in the sample will be based on ease of access and cost considerations, resulting in sampling bias. It also means that it is not possible to make generalisations (i.e. statistical inferences) from the sample to the population. This can lead to problems of external validity.
  
  Also, with quota sampling it must be possible to clearly divide the population into strata; that is, each unit from the population must only belong to one stratum. In our example, this would be fairly simple, since our strata are male and female students. Clearly, a student could only be classified as either male or female. No student could fit into both categories (ignoring transgender issues).
  
  Furthermore, imagine extending the sampling requirements such that we were also interested in how career goals changed depending on whether a student was an undergraduate or postgraduate. Since the strata must be mutually exclusive, this means that we would need to sample four strata from the population: undergraduate males, undergraduate females, postgraduate males, and postgraduate females. This will increase overall sample size required for the research, which can increase costs and time to carry out the research [see the article How to calculate your sample size when using a quota sample] coming soon.
  

If you are an undergraduate or master’s level dissertation student considering using quota sampling, you may also want to read more about how to put together your sampling strategy [see the section: Sampling Strategy].