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==Approaches to sampling==
==Approaches to sampling==
===Probability 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.
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====
====Simple random sampling====
Simple random sampling is the optimal method of sampling from a population, from a statistical viewpoint. It requires the formation of a '''sampling frame''', which is a list of all the individuals in the source population. From this, a randomisation procedure is used to select animals for further study. As such, if a sampling frame is not available and cannot be created, simple random sampling cannot be used. A very basic example of a simple random sampling procedure could involve labelling each of the members of the source population on pieces of paper, and randomly selecting a number of these out of a bag - however, computerised techniques involving random numbers are more commonly used nowadays.
====Systematic random sampling====
====Systematic random sampling====
Systematic random sampling does not require a sampling frame, but does require the individuals in the source population to each be identifiable and requires them to be randomly ordered in some way. A member of the population is initially selected, and then other individuals are selected based on a set '''sampling interval''' (calculated by dividing the size of the source population with the required sample size). A common application of systematic random sampling is when animals are ordered in order to pass through a race or when dairy cattle are entering the milking parlour (note, however, that recently calved animals may be excluded from the sample frame in the latter example, which may result in [[Bias|selection bias]]). For example, if you wanted to take a sample of 20 animals from a sheep flock containing 200 animals which are all due to pass through a race in order to be dosed with anthelmintics, you calculate the sampling interval (=10), and then randomly select a number within this interval to indicate the first sheep. For example, assume that the number selected is four: you would then sample the fourth animal passing through the race, followed by every 10th animal (i.e. 14th, 24th, 34th...184th, 194th) - giving you a total sample of 20 animals. Given that the order of passing through the race is random, every sheep has an equal probability of being selected (prior to determination of the sampling interval!).
====Stratified random sampling====
====Stratified random sampling====
This form of sampling is based on simple or systematic random techniques, but prior to selection of the study sample, the source population is divided into a number of strata (often according to factors considered to be associated with disease). Most commonly, the proportion of animals within each stratum in the source population is used as the proportion of the total sample size to be taken from each stratum (and therefore, the number of animals to be selected per stratum). This approach ensures that every animal has an equal probability of selection.
====Cluster sampling====
====Cluster sampling====
Cluster sampling is used in cases where the individual animals of interest are 'clustered' within other groupings (such as animals within farms), and it is easier to sample many animals from a smaller number of clusters than it would be to sample small numbers of animals from many clusters (as would be the likely situation if simple random sampling was used), or if a sampling frame of the clusters (known as the '''primary sampling units''') but not the individual animals is available. A random sample of clusters is first made (using simple or systematic random sampling techniques), followed by sampling of every individual within the selected cluster.
===Non-probability sampling===
===Non-probability sampling===
==Sample size calculation==
==Sample size calculation==
As mentioned earlier, it is important in any study not only that bias is minimised, but that the sample has sufficient precision (descriptive studies) or power (analytic studies).
As mentioned earlier, it is important in any study not only that bias is minimised, but that the sample has sufficient precision (in the case of descriptive studies) or power (in the case of analytic studies).
[[Category:Veterinary Epidemiology - Introduction|E]]
[[Category:Veterinary Epidemiology - Introduction|E]]