Table 12: Probability sampling strategies
Sampling strategy Summary
Simple random sampling The ideal sampling strategy because each element of the population has equal probability of being included in the sample. The sampling procedure is to assign a number to each element in the sampling frame and use a random number to select elements from the sampling frame. Most statistical packages can generate random numbers.
Systematic random sampling This sampling strategy uses a list of population elements. We assume that the elements are randomly listed. The first element included in the sample is randomly identified and the subsequent elements are selected using sampling interval. The sampling interval is calculated by dividing the desired sample size by the number of elements in the sampling frame.
Stratified sampling Stratified sampling can be used in a population that consists of mutually exclusive sub-groups (e.g. school population with classes). A random sampling procedure is then used to select elements from each stratum/sub-group. Sample size can be selected proportionately to the stratum size.
Cluster sampling Cluster sampling is commonly used when the population is very large or dispersed across a large geographical area. The goal of cluster sampling is to increase sampling efficiency. However, cluster sampling reduces the population variability in the sample since a group of individuals in the same geographical area is to some extent more homogenous and the probability of each element to be selected in the sample is not equal. To address this limitation, sample size calculation in a cluster sampling strategy needs to take into account design effect, which will increase sample size. Furthermore, the researcher can use the ‘probability proportionate to size’ procedure to correct the difference in cluster size and adjust the chance that clusters will be selected. A common example is the Expanded Programme for Immunisation (EPI) cluster sampling framework.