Sampling strategies

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The information gained through the study of disease in populations which will be increased if more members of the population are sampled. However, the sampling of every individual in a population is rarely feasible from either a logistical or an economic perspective (except in the case of very small-scale studies). Censuses are a form of descriptive study which aims to systematically collect information about every member of the population of interest (the source population), and are carried out in many countries for both livestock as well as for humans (although information regarding disease may not be collected). Statistical surveys are another type of descriptive study, which aim to select a sample (known as the study sample) from the source population, with the intention of extrapolating the information about these individuals to the source population. Similarly, in most analytic studies, a sample of the population must be selected for the same reasons.

This process of sampling from populations poses potential problems, as it must both select a sufficient number of individuals in order to be useful for the purposes of the study (whilst not sampling more than is required), and must also ensure that any biases in the selection process are minimised.

Concepts

A number of concepts relating to sampling from populations are presented here, using the example of a descriptive study investigating the prevalence of bovine tuberculosis amongst beef cattle in England.

Target population

The target population is the population to which the results of the study may be extrapolated out to, even if not all members of this population were eligible for sampling, and is often not clearly defined. In the example given here, it may be that the target population is viewed as all cattle (beef, dairy and noncommercial) in England, or all beef cattle in Great Britain, or all cattle in Great Britain. The decision regarding which population the results can be extrapolated to will depend on the knowledge and experience of the person interpreting the study, and the suitability of this extrapolation is described as the external validity of the study.

Source population

The source population is the population from which the sample was taken, and therefore all members of this population should have a chance of being selected for inclusion in the study. In the case of the example given here, the source population may be all registered beef herds in England. As such, the results obtained from the sample should relate to this population - if this is not the case, then there are likely to be considerable problems in the interpretation of the results. This is known as the internal validity of the study.

Study sample

The sample population includes those animals which are included in the final study. It is important to remember that in most epidemiological studies, we are not interested in this population per se - rather, we are interested in using this sample in order to make statements regarding the source population (and possibly the target population). Because not all members of the source population have been sampled, statistical techniques need to be applied to the results from the study group in order to estimate what the characteristics of the source population are expected to be. Due to this extrapolation, there is always a possibility that any estimates from a sample are incorrect due to random variation in the sample. Although this random variation cannot be controlled without increasing the sample size (or redefining the source population), the accuracy of the estimate can be maximised by ensuring that sources of bias are minimised.

Approaches to sampling

Probability sampling

In probability sampling, every individual in the source population has an equal probability of being randomly selected for the study sample. As such, it is the only appropriate method of sampling for descriptive studies (since extrapolation to the source population is of integral importance in these cases). Some types of probability sampling are described below.

Simple random sampling

Systematic random sampling

Stratified random sampling

Cluster sampling

Non-probability sampling

Sample size calculation