When experimenters or researchers are looking for data, it is often impossible to measure every individual data point in a population. However, statistical methods allow for inferences about a population by analyzing the results of a smaller sample extracted from that population. There are several methods of sampling.
Stratified random sampling is one common method that is used by researchers because it enables them to obtain a sample population that best represents the entire population being studied, making sure that each subgroup of interest is represented. All the same, this method of research is not without its disadvantages.
Key Takeaways
- Stratified random sampling allows researchers to obtain a sample population that best represents the entire population being studied by dividing it into subgroups called strata.
- This method of statistical sampling, however, cannot be used in every study design or with every data set.
- Stratified random sampling differs from simple random sampling, whichinvolves the random selection of data from an entire population, so each possible sample is equally likely to occur.
Stratified Random Sampling: An Overview
Stratified random sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to form a test group. A disadvantage is when researchers can't classify every member of the population into a subgroup.
Stratified random sampling is different from simple random sampling, whichinvolves the random selection of data from the entire population so that each possible sample is equally likely to occur.In contrast, stratified random samplingdivides the population into smaller groups, or strata, based on shared characteristics. A random sample is taken from each stratum in direct proportion to the size of the stratum compared to the population.
Stratified Random Sampling Example
Researchers are performinga study designed to evaluate the political leanings of economics students at a major university. The researchers want to ensure the random sample best approximates the studentpopulation, includinggender, undergraduates, andgraduate students. The total population in the studyis 1,000 students and from there, subgroups are created as shown below.
Total population = 1,000
Researchers would assign every economics student at the university to one of four subpopulations: male undergraduate, female undergraduate, male graduate, and female graduate. Researchers would nextcount how many students from each subgroup make up the totalpopulation of1,000 students. From there, researcherscalculateeachsubgroup's percentage representationof the total population.
Subgroups:
- Male undergraduates = 450 students (out of 100)or 45% of the population
- Female undergraduates = 200 students or 20%
- Male graduate students = 200 students or 20%
- Female graduate students = 150 students or 15%
Random sampling of each subpopulation is done, based on its representation within the population as a whole. Since male undergraduates are45% of the population,45 male undergraduates are randomly chosen out of that subgroup. Because male graduates make up only 20% of the population, 20 are selected for the sample, and so on.
While stratified random sampling accurately reflects the population being studied, conditions that need to be met mean this method can't be used in every study.
Advantages of Stratified Random Sampling
Stratified random sampling has advantages when compared to simple random sampling.
Accurately Reflects Population Studied
Stratified random sampling accurately reflects the population being studiedbecause 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 ensureall of them are represented in the sampling.
With simple random sampling, there isn't anyguarantee that any particularsubgroup or type of person is chosen. In our earlier exampleof 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 totalpopulation. Also, 35female graduate students might be selected (35% of the population)resulting in under-representation of male 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 study.
Disadvantages of Stratified Random Sampling
Stratified random sampling also presents researchers with a disadvantage.
Can't Be Used in All Studies
Unfortunately, this method of research 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 isdisadvantageouswhen researchers can'tconfidently classify every member of the population into a subgroup. Also, finding an exhaustive and definitive list of an entire populationcan be challenging.
Overlapping can be an issue if there are subjectsthat fall into multiple subgroups. When simple random sampling is performed, those who are in multiple subgroups are more likely to be chosen. The result could be a misrepresentationor inaccurate reflectionof the population.
The above example makes it easy: Undergraduate, graduate, male, and female are clearly defined groups. In other situations, however, it might befar more difficult. Imagine incorporating characteristics such as race, ethnicity, or religion. The sorting process becomes more difficult, rendering stratified random sampling an ineffective and less-than-ideal method.
