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Pros and Cons of Stratified Random Sampling

Stratified unpremeditated sampling benefits researchers by enabling them to obtain a sample population that best represents the entire people being studied. All the same, this method of research is not without its disadvantages.


Stratified Random Sampling: An Overview

Stratified non-specific sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to arrangement a test group. A disadvantage is when researchers can’t classify every member of the population into a subgroup.


Stratified non-specific sampling is different from simple random sampling, which involves the random selection of data from the undivided population so that each possible sample is equally likely to occur. In contrast, stratified random sampling subdivides the population into smaller groups, or strata, based on shared characteristics. A random sample is taken from each seam in direct proportion to the size of the stratum compared to the population.


Stratified Random Sampling Example



Researchers are performing a haunt designed to evaluate the political leanings of economics students at a major university. The researchers want to ensure the random sample most appropriate approximates the student population, including gender, undergraduates, and graduate students. The total population in the study is 1,000 critics and from there, subgroups are created as shown below.


Total population = 1,000


Researchers would assign every economics admirer at the university to one of four subpopulations: male undergraduate, female undergraduate, male graduate and female graduate. Researchers leave next count how many students from each subgroup make up the total population of 1,000 students. From there, researchers estimate each subgroup’s percentage representation of the total population. 


Subgroups:


  • Male undergraduates = 450 students (out of 100) or 45% of the citizenry
  • Female undergraduates = 200 students or 20%
  • Male graduate students = 200 students or 20%
  • Female graduate students = 150 schoolgirls or 15%


Random sampling of each subpopulation is done, based on its representation within the population as a whole. Since male undergraduates are 45% of the folk, 45 male undergraduates are randomly chosen out of that subgroup. Because male graduates make up only 20% of the natives, 20 are selected for the sample and so on. 


While stratified random sampling accurately reflects the population being studied, acclimates that need to be met mean this method can’t be used in every study.

Advantages of Stratified Random Sampling

Stratified non-specific sampling has advantages when compared to simple random sampling.


Accurately Reflects Population Studied


Stratified unplanned sampling accurately reflects the population being studied because researchers are stratifying the entire population before utilizing random sampling methods. In short, it ensures each subgroup within the population receives proper representation within the experience. As a result, stratified random sampling provides better coverage of the population since the researchers have control outstanding the subgroups to ensure all of them are represented in the sampling. 


With simple random sampling, there isn’t any guarantee that any close subgroup or type of person is chosen. In our earlier example of the university students, using simple random sampling to bring about a sample of 100 from the population might result in the selection of only 25 male undergraduates or only 25% of the absolute population. Also, 35 female graduate students might be selected (35% of the population) resulting in under-representation of virile undergraduates and over-representation of female graduate students. Any errors in the representation of the population have the potential to diminish the accuracy of the scrutinize.


Disadvantages of Stratified Random Sampling

Stratified random sampling also presents researchers with a disadvantage.


Can’t be Worn in All Studies


Unfortunately, this method of research cannot be used in every study. The method’s disadvantage is that respective 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 at most one, subpopulation. As a result, stratified random sampling is disadvantageous when researchers can’t confidently classify every member of the folk 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 simple random sampling is performed, those who are in multiple subgroups are more acceptable to be chosen.The result could be a misrepresentation or inaccurate reflection of the population. 


The above example makes it easy: Undergraduate, graduate, mans, and female are clearly defined groups. In other situations, however, it might be far more difficult. Imagine incorporating marks such as race, ethnicity, or religion. The sorting process becomes more difficult, rendering stratified random swatch an ineffective and less than ideal method.


Key Takeaways

  • Stratified random sampling allows researchers to obtain a sample natives that best represents the entire population being studied.
  • This method of research can’t be used in every learn about.
  • Stratified random sampling differs from simple random sampling, which involves the random selection of details from a entire population, so each possible sample is equally likely to occur.


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