What is Stratified Random Sampling

Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics.

Stratified random sampling is also called proportional random sampling or quota random sampling.


Stratified Random Sampling

BREAKING DOWN Stratified Random Sampling

When running analysis or research on a group of entities with similar characteristics, a researcher may find that the population size is too large to run a research on. In order to save time and money, an analyst may take on a more feasible approach by selecting a small group from the population. The small group used is referred to as a sample size, which is a subset of the population that is used to represent the population. There are a number of ways a sample may be selected from a population, one of which is the stratified random sampling method.

A stratified random sampling involves dividing the entire population into homogeneous groups which are called strata (singular is stratum). Random samples are then selected from each stratum. For example, consider an academic researcher who would like to know the number of MBA students that got a job within three months of graduation in 2007. He will soon find that there were almost 200,000 MBA graduates for the year. He might decide to just take a simple random sample of 50,000 grads and run a survey, or better still, divide the population into strata and take a random sample from the strata. To do this, he could create population groups based on gender, age range, race, country of nationality, and career background. A random sample from each stratum is taken in a number proportional to the stratum's size when compared to the population. These subsets of the strata are then pooled to form a random sample.

Proportionate and Disproportionate Stratification

Stratified random sampling ensures that each subgroups of a given population is adequately represented within the whole sample population of a research study. Stratification can be proportionate or disproportionate. In a proportionate stratified method, the sample size of each stratum is proportionate to the population size of the stratum. For example, if the researcher wanted a sample of 50,000 using age range, the proportionate stratified random sample will be obtained using this formula: (sample size/population size) x stratum size. Assuming a population size of 180,000 MBA grads per year,

Age group





Number of people in stratum





Strata sample size





The strata sample size for MBA grads that are in the range of 24 to 28 years old is calculated as (50,000/180,000) x 90,000 = 25,000. Same method is used for the other age range groups. Now that the strata sample size is known, the researcher can perform simple random sampling in each stratum to select his survey participants. In other words, 25,000 grads from the 24-28 age group will be selected randomly from the entire population, 16,667 grads form the 29-33 age range will be selected from the population randomly, and so on.

In a disproportional stratified sample, the size of each stratum is not proportional to its size in the population. The researcher may decide to sample ½ of the population within the 34-37 age group and 1/3 of the grads with ages ranging from 29-33 years.

It is important to note that one person cannot fit into multiple strata. Each entity must only fit in one stratum. Having overlapping subgroups means that some individuals will have higher chances of being selected for the survey, which completely negates the concept of stratified sampling as a type of probability sampling.

Advantages of Stratified Random Sampling

The main advantage of stratified sampling is that it captures key population characteristics in the sample. Similar to a weighted average, this method of sampling produces characteristics in the sample that are proportional to the overall population. Stratified sampling works well for populations with a variety of attributes, but is otherwise ineffective if subgroups cannot be formed.

Stratification gives a smaller error in estimation and greater precision than the simple random sampling method. The greater the differences between the strata, the greater the gain in precision.