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The Advantages and Disadvantages of Quota Sampling Compared to Random Sampling

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Introduction

The Advantages and Disadvantages of Quota Sampling Compared to Random Sampling

Introduction

Quota sampling is a non-probability sampling method, compared to random sampling methods, which are known as probability sampling methods. Examples of probability methods are stratifying sampling, cluster sampling, systematic sampling and simple random sampling. When a sample needs to be taken from a population, the issue of which type of sampling method to use arises; probability or non-probability. Since we are looking at specifically quota sampling, we need to define it. Quota sampling involves stratifying a population into mutually exclusive sub-groups, as if using the stratified sampling method. However, the difference is, in quota sampling, judgement is used instead of randomness to select units from each stratum. The number of sampling units chosen from each stratum is based on proportion. Random sampling is defined as when every unit in the population has a probability of being chosen. For a random sample to be carried out, there also needs to be a sampling frame.

Advantages of Quota Sampling

Although quota sampling is criticised heavily by academics, it does have its advantages. The biggest case for it is that is incredibly cheap to carry out. Travel

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Middle

(Moser & Stuart, 1953).

We have to also consider the possibility of bias erupting within the quotas at an individual level, leading to misrepresentations of the population. Take for instance; there is a quota for over 60 year olds. If the interviewer only finds people who range from 60 years old to around 65, then there is no representation of people who much older than 65 (Moser & Stuart, 1953).

Another example may be that the interviewer doesn’t like to travel to certain places, therefore only interviews people from a certain area, leading to selection bias in the sample. Random sampling doesn’t have this problem; “Random selection is the only selection mechanism in large-n studies that automatically guarantees the absence of selection bias (Epstein & King, 2002).”

Quota sampling also has the problem of non-response bias, a form of selection bias (selection bias is a non-sampling error). If somebody refuses to be a part of a study, then quota sampling allows the interviewer to go and find the next person who is willing, which results in data that is not wholly representative of the population. The reason for this is, that non-respondents probably have certain characteristics, and because the data obtained from the sample will not represent them at all (it will only be representative of respondents)

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Conclusion

(Moser & Stuart, 1953).

To conclude, there is never going to be a complete dismissal of a particular sampling method. Quota sampling has no theoretical structure; however practicality outweighs its negatives. If a researcher is looking for, “results derived from theoretically safe sampling methods,” then it is safe to say that quota sampling is out of the question (Moser & Stuart, 1953). If there are time and cost constraints to a researcher, then quota sampling can be convenient.

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References

Epstein, L & King, G. (2002). The Rules of Inference. University of Chicago Law Review. 69 (winter), p1-209.

FAO Corporate Document Respository.  Sampling in Marketing Research. Available: http://www.fao.org/docrep/w3241e/w3241e08.htm. Last accessed 6th December 2010.

Lyman Ott, Mendenhall, Scheaffer, (2006). Elementary Survey Sampling. 6th ed. Canada: Thomson.

Moser & Stuart. (1953). An Experimental Study of Quota Sampling. Journal of the Royal Statistical Society. 116 (4), p412-413 & p387-340.

Tansey, O. (2007). Process Tracing and Elite Interviewing: A Case for Non-Probability Sampling. PS: Political Science & Politics. 40 (4), p770.

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