The type of sampling method that the researcher used was stratified sampling. This is a technique that tries to restrict the possible samples to those that are less extreme by making sure that all parts of the population are well represented to increase efficiency. This kind of sampling is best suited to a situation whereby the population can easily be grouped into groups based on the factor that might affect the variable that is being measured. For Instance, in this case, the researcher had a sample size of 129 individuals with different characteristics. The researcher targeted the population that had the specific feature to enable the researcher to obtain the required results. The sample of 129 comprised of individuals with more than 45 years old. Furthermore, the sampled person was supposed to have been diagnosed to the cardiovascular system, and lastly, they should have been employed within the last three years.
The groups that the responded are portioned into are referred to as strata while a particular person is called stratum. With this type of sampling, the researcher is supposed to partition the populace into a group, find a simple random sample from every group and lastly, the researcher should collect data from every group that he/she randomly selected. In this article, the participants were recruited from employee health, occupation health program and outpatient programs that serve outpatient populations.
Conclusively, the sampling method is applicable when the researcher split the heterogeneous population into homogeneous groups that are selected fairly. If researchers are interested in getting a more precise estimate of the population targeted, random sampling remains one of the best sampling methods.
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