Sampling Methods

Sampling: method of selecting a certain number of units from a total population. (Macleod Clark J and Hockey L. 1981).  The way a sample is selected should be clearly demonstrated in a research report. The aim of a sample is that it should be as unbiased a cross section of the “parent” population as possible, i.e., a sample of subjects needs to be as representative as possible of the population under study.

To obtain a cross section we need to devise a sampling frame to define the boundaries (limits) within the context of the study and to reflect the organisation within which the sampling is taking place. The larger the size of the sample, the lower is the likelihood of it failing to represent the population under study.

However, the law of diminishing returns tells us that there is, for each study, a desirable sample size under which their may fail to be accuracy yet above which there is no better a reflection of the parent population. Sampling may be a) random; b) systematic; or c) refinements of random and systematic

a) random- every individual has a chance of being included individuals given a number and random number tables are used to identify “victims” for the sample.

b) systematic e.g., every 5th/xth this is quicker but not everyone has a chance to be included the first number could be chosen at random.

c) Refinements;

1. Stratified sample Where there is heterogeneity in the population this can be reflected in the strata, i.e., each stratum can be weighed to reflect the heterogeneity. In this way a proportional representation of the whole population can be gained.

2. Cluster sample Best used where there is a wide geographical spread. Clusters may be chosen subjectively to be representative of the whole. The clusters can be further stratified. E.G., if we want to know about all A&E patients in the country we need to take a sample from a variety of A/E’s. Each department can bring a number of patients into the sample according to whether they meet the stratification criteria.

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