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Sampling and Samples written by Joanne Birchall from Rainbow ResearchUnless you are in the luxurious position of having access to everyone who forms your population, you will need to take some form of sample from which to glean information for Market Research purposes. In addition to accessibility, the method chosen will depend upon a variety of statistical and practical factors. You will want to ensure your sample size is sufficient for the purpose of the analysis you intend to perform, ensure your sample is representative of the population you are attempting to say something about, and of course you will need to take into account your affordability.
This section covers the following:
- Sampling methods
- Calculating a sample size
- Calculating a sampling error
In most surveys, access to the entire population is near on impossible, however, the results from a survey with a carefully selected sample will reflect extremely closely those that would have been obtained had the population provided the data.
Sampling therefore is a very important part of the Market Research process. If you have surveyed using an appropriate sampling technique, you can be confident that your results will be generalised to the population in question. If the sample were biased in any way, for example, if the selection technique gave older people more of a chance of selection than younger people, it would be inadvisable to make generalisations from the findings.
There are essentiality two types of sampling: probability and non-probability sampling.
Probability Sampling Methods
Probability or random sampling gives all members of the population a known chance of being selected for inclusion in the sample and this does not depend upon previous events in the selection process. In other words, the selection of individuals does not affect the chance of anyone else in the population being selected.
Many statistical techniques assume that a sample was selected on a random basis. There are four basic types of random sampling techniques:
1) Simple Random Sampling
This is the ideal choice as it is a ‘perfect’ random method. Using this method, individuals are randomly selected from a list of the population and every single individual has an equal chance of selection.
This method is ideal, but if it cannot be adopted, one of the following alternatives may be chosen if any shortfall in accuracy.
2) Systematic Sampling
Systematic sampling is a frequently used variant of simple random sampling. When performing systematic sampling, every kth element from the list is selected (this is referred to as the sample interval) from a randomly selected starting point. For example, if we have a listed population of 6000 members and wish to draw a sample of 2000, we would select every 30th (6000 divided by 200) person from the list. In practice, we would randomly select a number between 1 and 30 to act as our starting point.
The one potential problem with this method of sampling concerns the arrangement of elements in the list.? If the list is arranged in any kind of order e.g. if every 30th house is smaller than the others from which the sample is being recruited, there is a possibility that the sample produced could be seriously biased.
3) Stratified SamplingStratified sampling methods also come in two types – proportionate and disproportionate.
Stratified sampling is a variant on simple random and systematic methods and is used when there are a number of distinct subgroups, within each of which it is required that there is full representation. A stratified sample is constructed by classifying the population in sub-populations (or strata), base on some well-known characteristics of the population, such as age, gender or socio-economic status. The selection of elements is then made separately from within each strata, usually by random or systematic sampling methods.
In proportionate sampling, the strata sample sizes are made proportional to the strata population sizes.For example if the first strata is made up of males, then as there are around 50% of males in the UK population, the male strata will need to represent around 50% of the total sample.
In disproportionate methods, the strata are not sampled according to the population sizes, but higher proportions are selected from some groups and not others. This technique is typically used in a number of distinct situations:
The costs of collecting data may differ from subgroup to subgroup.
We might require more cases in some groups if estimations of populations values are likely to be harder to make i.e. the larger the sample size (up to certain limits), the more accurate any estimations are likely to be.
We expect different response rates from different groups of people. Therefore, the less co-operative groups might be ‘over-sampled’ to compensate.