**Stratified Sampling**

Two critical factors to consider while conducting research are sampling and population. Having the right population and sample gives the researcher confidence in the quality of their research outcome. A researcher needs to choose the most appropriate method of sampling for any study. Errors are bound to happen in any survey, and therefore it is important to choose the most useful and relevant sampling method.

Stratified sampling involves seven steps; the first one involves defining the population. Choose the most appropriate stratification, depending on the kind of study being conducted. Step three of the process involves population listing, while the fourth one entails listing the population in relation to stratification that has been identified. Determine the size of the sample required for that particular research to be conducted (Hillson et al., 2015). Ensure that the sample in each stratification is proportional to the entire sample. Choose the sample randomly from the target population.

Through a comparison of all the sampling methods, stratified sampling the most appropriate for the research. In stratified sampling, the researcher chooses various groups of people that compose the target population and identifies needed proportions to become representatives of the sample. A list of each variable that might affect the study is prepared. It is one of the most used techniques since it enables the researcher to obtain a population sample that is most appropriate to represent the whole population being studied. Therefore, the interest of each subgroup of the population is represented.

**Pros**

Stratified sampling has several advantages compared to other sampling techniques. One of the advantages is that there is an accurate reflection of the population being studied. In this technique, researchers stratify the whole population prior to the application of the random sampling technique. All subgroups within the target population are well represented; therefore, eliminating biasness from the data (Lynn, 2019). Stratified sampling ensures that the whole population is well covered since researchers control subgroups to ensure maximum representation. This is a way of minimizing errors in the data and, as a result, improve the research accuracy. Errors are common in all studies, but the lesser they tare, the higher the accuracy.

Stratified sampling aims at reducing potential biasness in the selection of data to be sampled. As a result, the techniques provide results that represent the entire population, and therefore, it is okay to generalize the findings. The chosen samples are selected using the probabilistic techniques; thus, statistical conclusions made from stratified sampling are valid. The methodology can be considered superior since it increases the chances of all units to be well spread. Stratified sampling allows the researcher to use a smaller sample than other sampling methods, saving resources such as money and time. In a scenario where the samples collected are of the same size, stratified sampling provides greater precision.

**Cons**

Stratified sampling is disadvantageous in that several conditions ought to be fulfilled for the process to be successful. The researchers are tasked with knowing every member of the population being identified and grouped into only one subgroup of the population. It becomes a challenge when the researcher cannot decisively group each member to a particular subgroup (Sharma, 2017). It is also challenging to generate a list of the whole population. This creates overlapping since there is a probability of a member falling into more than one subgroup. The process is exhausting and time-consuming due to the complicated process of sorting the population.

References

Hillson, R., Alejandre, J. D., Jacobsen, K. H., Ansumana, R., Bockarie, A. S., Bangura, U., … & Stenger, D. A. (2015). Stratified sampling of neighborhood sections for population estimation: A case study of Bo City, Sierra Leone. PLoS One, 10(7), e0132850.

Lynn, P. (2019). The advantage and disadvantage of implicitly stratified sampling. methods, data, analyses, 13(2), 14.

Sharma, G. (2017). Pros and cons of different sampling techniques. International journal of applied research, 3(7), 749-752.

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