Sampling methods

There are two major types of sampling methods: probability sampling and non-probability sampling (see Figure 4.2).

Probability samples comprise samples in which the elements being included have a known chance of being selected. A probability sample enables sampling error to be estimated. This, in simple terms, is the difference between the sample value and the true value of the population being surveyed. A sampling error can

be stated in mathematical terms: usually plus or minus a certain percentage. A larger sample usually implies a smaller sampling error.

Non-probability samples are ones in which participants are selected in a purposeful way. The selection may require certain percentages of the sample to be women or men, housewives under 30 or a similar criterion. This type of selection is an effort to reach a cross-section of the elements being sampled. However, because the sample is not rigorously chosen it is statistically impossible to state a true sampling error.

Today, most samples chosen for applied research are non-probability samples. If carefully done - with quotas, for example, of persons to be studied - the findings are usually valid. A true probability sample, because of the stringent requirements, is likely to be far too expensive and too time-consuming for most uses. The sampling method chosen for any particular study, therefore, must be explained carefully, with the reasons for its acceptability and likelihood of supplying accurate data.

The research plan may not require that the whole country be sampled. Cost and time factors may lead to the decision to cover only part of a country. Past experience for the products or services being studied may indicate that the selected areas are representative of the nation with respect to what is being studied. However, the more specific the description of those to be studied, the larger the sample must be.

The major problem with the quota method of sampling is that the interviewers are allowed discretion in choosing the individual respondents within the quota categories. This discretion introduces a possible source of bias, because the resulting sample can largely omit some types of people, such as those who are difficult to contact.

A much better approach is the probability method of sampling, in which specific respondents are chosen by random selection methods. The result of this method is that no type of individual is systematically omitted from the sample and the likely amount of error in the resulting data can be calculated.

Statistical laws have established that no matter how large the population being studied (from a small city to a whole country), the size of the sample is the main factor that determines the expected range of error in a probability sample. Most current polls use samples ranging in size from 1000 to 2000 individuals. Many polling organisations have adopted probability methods in selecting their samples, but the less reputable polls still use quota methods or even non-scientific haphazard methods of sample selection - and the quality of their findings suffers accordingly.

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