Advantages and disadvantages of stratified random sampling
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Advantages
Stratified random sampling accurately reflects the population being studied because researchers are stratifying the entire population before applying random sampling methods. In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling.
With simple random sampling, there isn't any guarantee that any particular subgroup or type of person gets chosen. In our earlier example of the university students, using simple random sampling to procure a sample of 100 from the population might result in the selection of only 25 male undergraduates or only 25% of the total population. Also, 35 female graduates might be selected (35% of the population) resulting in underrepresentation for male undergraduates and overrepresentation for female graduates. Any errors in the representation of the population have the potential to diminish the accuracy of the study.
Disadvantages
Unfortunately, stratified random sampling cannot be used in every study. The method's disadvantage is that several conditions must be met for it to be used properly. Researchers must identify every member of a population being studied and classify each of them into one, and only one, subpopulation. As a result, stratified random sampling is disadvantageous when researchers can't confidently classify every member of the population into a subgroup. Also, finding an exhaustive and definitive list of an entire population can be challenging.
Overlapping can be an issue if there are subjects that fall into multiple subgroups. When the random sampling is performed, those that are in multiple subgroups are more likely to be chosen and as a result, would be a misrepresentation or inaccurate reflection of the population.
The above example makes it easy; undergraduate, graduate, male, and female are clearly defined groups. In other situations, however, it might be far more difficult. Imagine bringing characteristics such as race, ethnicity or religion into play. The sorting process becomes more difficult, rendering stratified random sampling an ineffective and less than ideal method
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Advantages
Stratified random sampling accurately reflects the population being studied because researchers are stratifying the entire population before applying random sampling methods. In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling.
With simple random sampling, there isn't any guarantee that any particular subgroup or type of person gets chosen. In our earlier example of the university students, using simple random sampling to procure a sample of 100 from the population might result in the selection of only 25 male undergraduates or only 25% of the total population. Also, 35 female graduates might be selected (35% of the population) resulting in underrepresentation for male undergraduates and overrepresentation for female graduates. Any errors in the representation of the population have the potential to diminish the accuracy of the study.
Disadvantages
Unfortunately, stratified random sampling cannot be used in every study. The method's disadvantage is that several conditions must be met for it to be used properly. Researchers must identify every member of a population being studied and classify each of them into one, and only one, subpopulation. As a result, stratified random sampling is disadvantageous when researchers can't confidently classify every member of the population into a subgroup. Also, finding an exhaustive and definitive list of an entire population can be challenging.
Overlapping can be an issue if there are subjects that fall into multiple subgroups. When the random sampling is performed, those that are in multiple subgroups are more likely to be chosen and as a result, would be a misrepresentation or inaccurate reflection of the population.
The above example makes it easy; undergraduate, graduate, male, and female are clearly defined groups. In other situations, however, it might be far more difficult. Imagine bringing characteristics such as race, ethnicity or religion into play. The sorting process becomes more difficult, rendering stratified random sampling an ineffective and less than ideal method
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Answer:
The advantages and disadvantages of stratified random sampling are:-
Advantages:
A stratified sample requires a smaller sample.
A stratified sample can gives greater accuracy than a simple random sample for the same size.
Disadvantages:
The stratified sampling decreases the costs for data gathering but it increases the work on the ground for the planning and research for keeping the precariousness within an acceptable level.
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