It may also be difficult to define a complete sampling frame and inconvenient to contact them, especially if different forms of contact are required (email, phone, post) and your sample units are scattered over a wide geographical area. A disadvantage of simple random sampling is that you may not select enough individuals with your characteristic of interest, especially if that characteristic is uncommon. A specific advantage is that it is the most straightforward method of probability sampling. So, if the first three numbers from the random number table were 094, select the individual labelled “94”, and so on.Īs with all probability sampling methods, simple random sampling allows the sampling error to be calculated and reduces selection bias. 1 For example, if you have a sampling frame of 1000 individuals, labelled 0 to 999, use groups of three digits from the random number table to pick your sample. One way of obtaining a random sample is to give each individual in a population a number, and then use a table of random numbers to decide which individuals to include. In this case each individual is chosen entirely by chance and each member of the population has an equal chance, or probability, of being selected. However, non-probability sampling methods tend to be cheaper and more convenient, and they are useful for exploratory research and hypothesis generation. Consequently, you cannot estimate the effect of sampling error and there is a significant risk of ending up with a non-representative sample which produces non-generalisable results. In non-probability (non-random) sampling, you do not start with a complete sampling frame, so some individuals have no chance of being selected. Probability sampling methods tend to be more time-consuming and expensive than non-probability sampling. In this way, all eligible individuals have a chance of being chosen for the sample, and you will be more able to generalise the results from your study. In probability (random) sampling, you start with a complete sampling frame of all eligible individuals from which you select your sample. There are several different sampling techniques available, and they can be subdivided into two groups: probability sampling and non-probability sampling. For example, if the electoral roll for a town was used to identify participants, some people, such as the homeless, would not be registered and therefore excluded from the study by default. This may involve specifically targeting hard to reach groups. If a sample is to be used, by whatever method it is chosen, it is important that the individuals selected are representative of the whole population.
(Calculation of sample size is addressed in section 1B (statistics) of the DFPH syllabus.) Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. It would normally be impractical to study a whole population, for example when doing a questionnaire survey. We are currently in the process of updating this chapter and we appreciate your patience whilst this is being completed.
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