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Quota sampling: Difition, application, advantages and disadvantages

 


Quota sampling


Quota sampling is a non-probability sampling technique wherein the assembled sample has the same proportions of individuals as the entire population with respect to known characteristics, traits or focused phenomenon.

When to Use Quota Samples

The main reason why researchers choose quota samples is that it allows the researchers to sample a subgroup that is of great interest to the study. If a study aims to investigate a trait or a characteristic of a certain subgroup, this type of sampling is the ideal technique.
Quota sampling also allows the researchers to observe relationships between subgroups. In some studies, traits of a certain subgroup interact with other traits of another subgroup. In such cases, it is also necessary for the researcher to use this type of sampling technique.

Connection to stratified sampling
Quota sampling is the non-probability version of stratified sampling. In stratified sampling, subsets of the population are created so that each subset has a common characteristic, such as gender. Random sampling chooses a number of subjects from each subset with, unlike a quota sample, each potential subject having a known probability of being selected.
In the process of sampling these subgroups, other traits in the sample may be overrepresented. In a study that considers gender, socioeconomic status and religion as the basis of the subgroups, the final sample may have skewed representation of age, race, educational attainment, marital status and a lot more.

The Advantages of Quota Sampling

·        Relatively easy to administer
·        Can be performed quickly
·        Cost-effective
·        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




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