Researchers spend stratified random sampling to obtain a sample population that best represents the entire population being studied. Its advantages include minimizing sample choice bias and ensuring certain segments of the population are not overrepresented or underrepresented. Its disadvantage is that it is unusable when researchers cannot confidently classify every member of the population into a subgroup.
Stratified random sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to form a test group. Consider a study designed to assess the political leanings of economics students at a major university. The researchers want to fabricate every effort to ensure the sample best approximates the actual population regarding gender and level of study, such as undergraduate versus graduate.
First, the researchers assign every economics student at the university to one of four subpopulations: male undergraduate, female undergraduate, male graduate and female graduate. Random sampling is done for each subpopulation based on its representation within the population as a whole. Suppose male undergraduates comprise 45% of the population. whether the study’s sample size is 100, it features 45 male undergraduates. Because male graduates fabricate up only 20% of the population, 20 are selected for the sample.
The biggest advantage of stratified random sampling is that it reduces choice bias. Stratifying the entire population before applying random sampling methods helps ensure a sample that accurately reflects the population being studied in terms of the criteria used for stratification.
Stratified random sampling is also advantageous when it can be used accurately because it ensures each subgroup within the population receives proper representation within the sample. Using simple random sampling to procure a sample of 100 from the population described above might result in the choice of only 25 male undergraduates. Thirty-five male graduates might also be selected, resulting in underrepresentation for male undergraduates and overrepresentation for male graduates. Because educational attainment is shown to affect political views in many past studies, such errors in representation like the potential to decrease the accuracy of the study.
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. Finding an exhaustive and definitive list of an entire population is the first challenge. In some cases, it is downright impossible.
The other challenge is accurately sorting each member of the population into a single stratum. The above example makes it easy; undergraduate, graduate, male and female are clearly defined groups. In other situations, however, it is far more difficult. Imagine bringing defining 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.
travel further into random sampling – Read The inequity Between Stratified and Simple Random Sampling and Examples of Stratified Random Sample.