The Advantages & Disadvantages of Quota Sampling
What is Quota Sampling?
Quota sampling is a non-probability sampling technique in which researchers look for a specific characteristic in their respondents, and then take a tailored sample that is in proportion to a population of interest.
How to Choose a Quota Sample
Choosing a quota sample can be broken down into three steps.
First, the researcher must divide the population of interest into strata, or groups of individuals that are similar in some way that is important to the response.
These strata should be exclusive, meaning that participants in the study only belong to one strata.
For example, you could create strata from a population of employees depending on the number of years they’ve worked at your company.
Second, the researcher must then determine the proportion of subgroups in the population.
For example, employees that have been at your company for between four and six years could be one in five.
Next, the researcher must choose their sample size.
For example, if you are sampling 1,000 people you might choose a quota sample of 100.
Finally, the researchers must then choose participants to partake in their study. It’s imperative to adhere to the subgroup to population proportion.
Sticking with our example above, if one in five employees has been at the company for four to six years, 20 percent of our sample should also have worked at their current company for four to six years.
Researchers must then continue this process until their quotas are filled, or in the case of our example, until we have 1,000 participants.
Quota sampling is very similar to stratified random sampling, with one exception.
In quota sampling, the samples from each stratum do not need to be random samples.
For this reason, stratified random sampling is a preferable method over quota sampling, as the random selection in stratified random sampling ensures a more accurate representation of the larger population.
However, researchers use quota sampling when stratified random sampling is not possible.
An Example of Quota Sampling
Let’s consider a theoretical example of quota sampling so that we can better understand this concept.
Imagine that a researcher is interested in the purchasing preferences of consumers at a local shopping plaza.
Since the researcher believes that men and women have different purchasing preferences, the researcher decides to stratify the population by gender. It’s important to note that gender was selected here because it is important to the response, as the researcher is ultimately interested in purchasing preferences of consumers at the shopping plaza.
After doing some preliminary research, the researcher finds statistical evidence that 60 percent of the people who shop at the shopping plaza are female.
The researcher wants a sample size of 400.
In order to get a proportional sample, the researcher decides to sample 240 females (which represents 60 percent of his sample) and 160 males (40 percent of the sample).
Since the deadline for his study is approaching quickly, the researcher decides that in order to save time he should post an advertisement in the shopping plaza soliciting volunteers for his study. This advertisement brings in 240 females and 160 males for the researcher’s sample.
The research process outlined above is in fact an example of quota sampling, as the researcher did not take a random sample.
The Advantages and Disadvantages of Quota Sampling
Quota sampling comes with both advantages and disadvantages.
The Advantages of Quota Sampling
- Relatively easy to administer
- Can be performed quickly
- Accounts for population proportions
- A useful method when probability sampling techniques are not possible
The Disadvantages of Quota Sampling
- Sample selection is not random
- There is a potential for selection bias, which can result in a sample that is unrepresentative of the population
As mentioned previously, it’s best for researchers to try to use stratified random sampling instead of quota sampling. However, quota sampling is a convenient and useful tool when stratified random sampling is not possible.
Have you used quota sampling to support your research initiatives? If so, we’d love to hear what you thought of the process. Did you end up receiving the results you needed? Let us know in the comments below!