Quota sampling

In quota sampling, a population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60. This means that individuals can put a demand on who they want to sample (targeting).

This second step makes the technique non-probability sampling. In quota sampling, there is non-random sample selection and this can be unreliable. For example, interviewers might be tempted to interview those people in the street who look most helpful, or may choose to use accidental sampling to question those closest to them, to save time. The problem is that these samples may be biased because not everyone gets a chance of selection, whereas in stratified sampling (its probabilistic version), the chance of any unit of the population is the same as 1/n (n= number of units in the population). This non-random element is a source of uncertainty about the nature of the actual sample and quota versus probability has been a matter of controversy for many years.[1]

Quota sampling is useful when time is limited, a sampling frame is not available, the research budget is very tight or detailed accuracy is not important. Subsets are chosen and then either convenience or judgment sampling is used to choose people from each subset. The researcher decides how many of each category are selected.

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.[2]

• Coefficient of variation
• Standard deviation

• Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9