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Quota sampling is a type of stratified sampling in which selection within the strata is non-random.
Consider the previous case of wanting to survey 100 students (p. 177). It was established that the strata to be used were year levels. The table below gives the number of students in each year level in the school with a population of 1,000 students:
Calculation of the quota for Year 10 students is:
Percentage of Year 10’s in the school = (150 ÷ 1,000) x 100 = 15%
As 15% of the school population is in Year 10, then you would expect 15% of the sample to contain Year 10 students. Therefore, to calculate the number of Year 10’s to be included in the sample:
= 15% of 100 (sample size)
The main difference between stratified sampling and quota sampling is in the selection of 15 Year 10 students to be included in the sample. Recall that stratified sampling would select these students using a simple random sampling or a systematic sampling method.
In quota sampling, no such technique is used. The 15 students might be selected by choosing the first 15 Year 10 students to enter school or choosing 15 students in the first two rows in a particular classroom.
However, these samples may be biased because not everyone gets a chance of selection. For example, those who come late or sit at the back of the class may be different in some way to those who do have a chance of selection. They may have different views on issues you want to survey.
Market and opinion researchers often use quota sampling. Its main advantages are that it is less costly and easier to administer than many other methods.
Quota sampling ensures that there will be a representative sample of the population for specified criteria or strata, in this case year level. However, the actual sample may not necessarily be selected in a random manner, and therefore, the sample may not be representative for some other important criteria.
The main argument against quota sampling, as already explained, is that it does not meet the basic requirement of randomness. Some units may have no chance of selection, or the chance of selection may be unknown. Therefore, the sample may be biased.
Convenience sampling does not produce a representative sample of the population because people or items are only selected for a sample if they can be accessed easily and conveniently.
Examples of convenient samples include selecting:
The obvious advantage of this type of sampling is its ease of use, but this is greatly offset by the sample being biased.
A common method of volunteer sampling is phone-in sampling, used mainly by television and radio stations to gauge public opinion on current affairs issues such as preferred political party, capital punishment, etc. People are asked to telephone their vote on a particular issue within a certain time, with no limit to the number of people who can call in.
They would possibly give voters three hours to call after which the lines would be closed and a conclusion formed. If 200 people called in, and 114 voted ‘Yes’ and 86 voted ‘No’, then the television station would report that 57% of callers voted ‘Yes’ and 43% voted ‘No’. However, this may or may not represent the opinion of the whole population.
The main advantages of phone-in sampling are that it is cheap in terms of time and money, and very easy to monitor and control.
However, the chance that the sample will be biased is very high because only those with a telephone can vote, and only those watching television or listening to radio at the time would be aware of the survey. As mentioned above, each person can make any number of calls registering their vote, and those not interested in calling will not be included.