Random sampling is a sampling technique where all elements in a population have an equal probability of being selected to form the sample. It means, therefore, that elements are chosen arbitrarily without following any formulae (Babbie, 2010). This technique is unbiased and it gives true representative statistics, especially when the sample size is large.
In addition, the technique requires minimal prior knowledge of the population. Similarly, it is simple to use since it does not require one to have complex mathematical knowledge (Babbie, 2010).
Systematic sampling on the other hand requires arrangement of the population in a given order. The first element is chosen randomly while the subsequent elements are chosen after certain regular intervals. It should be note that this type of sampling gives every element an equal chance of selection. This type of sampling is easy to use and check incase need arises.
On the same note, since the technique arranges the population in a systematic order, sampling is quick which saves time and labor (Babbie, 2010). In addition, when the frame used in systematic sampling is modern, the technique is efficient compared to random sampling.
Convenience sampling refers to a sampling technique where researchers are free to choose sample elements using any method they deem fit. There is no laid down procedure as to how the elements should be sampled thus, it neither applies probability nor judgment. The technique is easy to use for investigators because they choose the sample that is useful to their study (Babbie, 2010). It is good when one has no time and money to gather a large population, because it does not require specific rules to be met.
In stratified sampling, researchers group the population into different groups using differentiating characteristics. The researcher will then randomly select elements from each stratum using the size of the stratum in relation to the population to determine the number of elements to be picked from each stratum. The elements are then combined to form the sample (Babbie, 2010). The technique allows study of each specific group which might not be possible in a generalized population.
In cases where different segments of the population need different degrees of accuracy, stratified sampling is more applicable. Moreover, the resulting sample is more representative and gives more efficient statistics. Furthermore, stratified sampling gives room for investigators to use different types of sampling methods for each stratum as and when they deem fit (Babbie, 2010).
On the other hand, cluster sampling involves the grouping of the population into groups called clusters. A few clusters are then selected randomly and all the elements in the selected clusters are used to form the sample (Babbie, 2010). The advantage of clustering is that it greatly reduces costs of travelling as well as administrative costs. On the same note, this type reduces variability of the statistics observed as compared to other methods of sampling (Babbie, 2010).
Multi-stage sampling involves combination of two or more sampling techniques. Initially, the researcher divides the population into large clusters. The researcher then subdivides few selected clusters into sub-clusters. The clusters to be subdivided are selected either randomly, or using information collected from elements in the first clusters.
The process is repeated until the elements in the sub-sets are few enough. Finally, the researcher uses any other sampling technique to select sub-sets whose elements are used as a sample. The method is beneficial in cases where it is difficult to get a complete list of the population. It is an advanced form of cluster sampling (Babbie, 2010).
Multi-stage sampling is accurate compared to cluster sampling when the same sample size is used. Moreover, multi-stage sampling is a more convenient way of finding a sample. On the same note, the method is more cost effective and in many instances the survey can be done quickly compared to other methods (Babbie, 2010).
Reference
Babbie, E. R. (2010). The Basics of Social Research. Stanford: Cengage Learning.