The previously discussed methods of sampling are great if specific characteristics of the population are of no concern to the researcher. If, however, the researcher is considering a specific characteristic of the population (such as race, age, gender) that is not equally distributed in the population to begin with, a different sampling technique is in order. Stratified sampling allows the researcher to choose a sample that is forced to fit the profile of the population. Stratified sampling is used to ensure that the strata (layers) in the population are closely represented in the sample. Let’s say that a political scientist is conducting a study of voting behavior in a particular region where 60% of the voting population is Republican, and 40% is Democrat. It wouldn’t make sense to draw a sample that is half Democrat and half Republican—we know from the start that such a sample would not be representative of the population. If we want a sample that accurately reflects the population, we need a sample where 60% of the voting population is Republican, and 40% is Democrat. Stratified sampling allows us to achieve this.
To achieve a stratified sample, you must list each stratum separately. In the above example, we would need to list Democrats and Republicans separately. Let’s say we want a sample of 100 subjects. To get such a sample, we would select 60 participants from the Republican list and 40 from the Democrat list. Thus, our sample is stratified just like our population.
Last Modified: 02/18/2019