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date: 08 February 2023

# sampling

Source:
A Dictionary of Human Geography

The method of selecting a subset of data from the total population of potential data. Sampling is used because the total population of potential data might be very large and it is unfeasible in terms of time and resources to harvest it all. Instead a sample of data is made that seeks to be free of bias and as representative of the whole population as possible. There are many different sampling methods, each designed to cope with a particular limitation such as difficulty of identifying potential respondents. If the data are to be analysed using probability-based statistical methods then a scientific approach to sampling has to be undertaken. A systematic sample involves the methodical selection of cases from a sampling frame, where, although the initial start point is randomly selected, the subsequent selection of points is at a regular interval (e.g. every tenth house). A random sample avoids any interviewer/sample bias as every unit in a population has the same probability of independent selection. There are a variety of other designs based on the simple random sample, which include the stratified random sample (the sampling frame divided into subgroups or strata, which are then each sampled using the simple random method), and the multistage random sample (the sampling frame divided into hierarchical levels or stages, wherein each level is sampled using a simple random method which selects the elements to be included at the next level). A judgemental sample (also known as a purposive sample) is the most subjective sampling method. Sampling elements are selected based on the interviewer’s experience that they are likely to produce the required results. A quota sample selects those elements to be included in the sample on the basis of satisfying a predefined quota but is not random or stratified in nature. Another non-random sampling method is a snowball sample, wherein initial contacts provide details of other people who might fulfil the sampling requirements. With regards to sample size, it is often thought that ‘more is better’, with larger samples producing a more representative dataset than smaller ones.... ...

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