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In quota sampling, the 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.

It is this second step which makes the technique one of non-probability sampling. In quota sampling, the selection of the sample is non-random unlike random sampling and can often be found 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 which are closest to them, for time-keeping sake. The problem is that these samples may be biased because not everyone gets a chance of selection. This non-random element is its greatest weakness and quota versus probability has been a matter of controversy for many years.

Quota sampling is useful when time is limited, sampling frame is not available, research budget is very tight or when detailed accuracy is not important. You can also choose how many of each category is selected.

A quota sample is a convenience sample with an effort made to insure a certain distribution of demographic variables. Subjects are recruited as they arrive and the researcher will assign them to demographic groups based on variables like age and gender. When the quota for a given demographic group is filled, the researcher will stop recruiting subjects from that particular group.

This is the non probability version of stratified sampling. Subsets are chosen and then either convenience or judgment sampling is used to choose people from each subset.

Stratified sampling is probably the most commonly used probability method. 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.


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