Stratified Sampling

Strata are formed in stratified random sampling. Stratified sampling is also referred to as stratification. Strata is formed based on participants’ common attributes and characteristics, such as revenue or educational achievement. Proportional random sampling and quota random sampling are other phrases for defining stratified random sampling. Researchers can use stratified random sampling to acquire a sample component that best includes the entire sample members that are being evaluated.

Stratified Random Sampling:

Stratified random sampling involves classifying the entire sample data into groups of homogeneous sets known as strata. Stratified random sampling differentiates from simple random sampling as it involves the random selection of information from an entire sample so that each sampling unit has an equal chance of occurring.

This technique is best used with sample data which is easily broken down into distinct subgroups. Samples are then taken from each subgroup based on the ratio of the size of the subgroup to the total data sample size. A stratified random sampling includes age, socio-economic status divisions, national origin, religious doctrine, academic achievement, and other categorization.

Working of the Stratified Random Sampling:

When conducting assessments or investigations on a set of entities with similar features, a researcher may explore that the size of the population is too huge to conduct studies on. An observer may take a much more viable strategy by choosing a subset sample from the population to save time & expense. A representative sample is a comparatively tiny subset of people which is used to obtain a representative sample. A specimen can be drawn from the population in a range of methods, one of which is stratified random sampling.

In contrast to simple random sampling, which continues to treat all elements of the population as equivalent and with an equivalent probability of being surveyed, stratified sampling is being used to set specific groups in a sample.

Stratified Sampling Example:

Consider the following scenario: a research team is trying to seek viewpoints on religious practices from people of various ages. Instead of soliciting feedback from the nation’s 326,044,985 citizens, a random sample of around 10,000 people can be chosen for data analysis. These ten thousand citizens can be divided into age groups of 18-29, 30-39, 40-49, 50-59, and 60 and up. Each stratum would have its own set of representatives, as well as a distinct number of individuals. This is an example of a stratified random sampling example. Each stratum shall be unique and shall cover the entire population sample set of data. 

Advantages Of Stratified Random Sampling:

• A stratified sample covers topics out of each subgroup, making sure that it is reflective of one’s population’s diversification.

• You need a comparable representative sample for every subgroup if you really want the collected data from each subset to have a comparable amount of variation.

• Even though your total population could be quite diverse, certain subsets may be more uniform.

• You may have to use distinct methods for data collection from various subsets at instances.

Conclusion 

We discussed What is stratified sampling, examples of stratified sampling, examples of stratified random sampling, and other related topics through the study material notes on Biosphere Reserves in India. We also discussed the Advantages of stratified random sampling to give you proper knowledge. 

 Stratified Random Sampling is a method found in market research software that selects the random sample in two steps. The division of a population first into homogeneous subgroups, or strata, that are reciprocally exclusive all around. This means that each member of a population should indeed be delegated to only one stratum, there should be no crossover among strata. Some stratified variables, such as revenue or place of residence, are being used to divide the people into various strata.