Multistage cluster sample is used when the population being examined is in the form of a society where there are no clear-cut similarities (Rabbie, 2011). This ensures that they are clustered together in a manner that would prove that they are appropriately classified and sampled with the least errors. For instance, if one wants to sample first grade English students in the US universities and colleges, there might be some problems, as the population is very diverse and would need a more complex process to determine the people who would be selected for the research, as explained. First, one would use a directory in determining the number of universities and colleges that would be chosen for this research. Then, the first year students in these universities would be selected from the rest of the students. Afterwords, a list of all students who are taking English in these universities would be obtained, and eventually the sample would be developed.
The first process of determining the available colleges and universities in the country is known as the cluster sampling. It is the first step to ensure that all the institutions that qualify for the same are covered. The population considered in the cluster is very diverse, and there is a large area determined as the desired by the research.
Secondly, the selected institutions are divided into the desired first year students in order to exclude the undesired students. This is known as dividing the clusters into blocks in order to narrow down the search for the population targeted. Finally, the first year students in English are picked in what is referred to as sampling. Further, the researcher could list and sample the students as he desires after he has narrowed down to the final population. This method of sampling is known to be used in complex researches where sampling would otherwise look difficult (Rabbie, 2011).
Errors in this method of sampling are usually increased due to many stages that are involved in the sampling process (2013). There are at least two steps before one gets to the actual sample, and this leads to the increased chances of making mistakes. Therefore, researchers have devised ways to reduce the occurrence of mistakes by increasing the population of the samples. As one chooses the people that would be used in the research, he/she may sample homogenous blocks and this would create a biased result. Therefore, a bigger population would lead to a higher level of accuracy since the population would be more diverse. These two factors are known to researchers to reduce the occurrence of errors during an investigation.
Multistage cluster sampling is a very efficient way of carrying out researches that involves a complex society such as a city population or a national population. It is, therefore, good to consider it whenever one is using an assorted population. Through the use of multistage cluster sampling, one is able to pick up the correct sample from the diverse population. For this very reason, it is advisable that many social researches should use multistage cluster sampling in order to reduce the probability of error (2011).